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Zhang Y, Hu J, Zhang X, Liang M, Wang X, Gan D, Li J, Lu X, Wan J, Feng S, Lu X. Protein Signature Differentiating Neutrophils and Myeloid-Derived Suppressor Cells Determined Using a Human Isogenic Cell Line Model and Protein Profiling. Cells 2024; 13:795. [PMID: 38786019 PMCID: PMC11119164 DOI: 10.3390/cells13100795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 05/01/2024] [Accepted: 05/05/2024] [Indexed: 05/25/2024] Open
Abstract
Myeloid-derived suppressor cells (MDSCs) play an essential role in suppressing the antitumor activity of T lymphocytes in solid tumors, thus representing an attractive therapeutic target to enhance the efficacy of immunotherapy. However, the differences in protein expression between MDSCs and their physiological counterparts, particularly polymorphonuclear neutrophils (PMNs), remain inadequately characterized, making the specific identification and targeting of MDSCs difficult. PMNs and PMN-MDSCs share markers such as CD11b+CD14-CD15+/CD66b+, and some MDSC-enriched markers are emerging, such as LOX-1 and CD84. More proteomics studies are needed to identify the signature and markers for MDSCs. Recently, we reported the induced differentiation of isogenic PMNs or MDSCs (referred to as iPMNs and iMDSCs, respectively) from the human promyelocytic cell line HL60. Here, we profiled the global proteomics and membrane proteomics of these cells with quantitative mass spectrometry, which identified a 41-protein signature ("cluster 6") that was upregulated in iMDSCs compared with HL60 and iPMN. We further integrated our cell line-based proteomics data with a published proteomics dataset of normal human primary monocytes and monocyte-derived MDSCs induced by cancer-associated fibroblasts. The analysis identified a 38-protein signature that exhibits an upregulated expression pattern in MDSCs compared with normal monocytes or PMNs. These signatures may provide a hypothesis-generating platform to identify protein biomarkers that phenotypically distinguish MDSCs from their healthy counterparts, as well as potential therapeutic targets that impair MDSCs without harming normal myeloid cells.
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Affiliation(s)
- Yuting Zhang
- Department of Biological Sciences, Boler-Parseghian Center for Rare and Neglected Diseases, Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN 46556, USA
- Integrated Biomedical Sciences Graduate Program, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Jin Hu
- Mass Spectrometry & Metabolomics Core Facility, Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, Hangzhou 310024, China
| | - Xiashiyao Zhang
- Department of BioHealth Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis, Indianapolis, IN 46202, USA
| | - Minzhi Liang
- Department of Biological Sciences, Boler-Parseghian Center for Rare and Neglected Diseases, Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Xuechun Wang
- Department of Biological Sciences, Boler-Parseghian Center for Rare and Neglected Diseases, Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Dailin Gan
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Jun Li
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Xuemin Lu
- Department of Biological Sciences, Boler-Parseghian Center for Rare and Neglected Diseases, Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Jun Wan
- Department of BioHealth Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis, Indianapolis, IN 46202, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Shan Feng
- Mass Spectrometry & Metabolomics Core Facility, Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, Hangzhou 310024, China
| | - Xin Lu
- Department of Biological Sciences, Boler-Parseghian Center for Rare and Neglected Diseases, Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN 46556, USA
- Integrated Biomedical Sciences Graduate Program, University of Notre Dame, Notre Dame, IN 46556, USA
- Tumor Microenvironment and Metastasis Program, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN 46556, USA
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2
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Li Z, Liu S, Gao Z, Ji L, Jiao J, Zheng N, Li X, Wang G, Qin J, Wang Y. Dynamic Proteomic Changes in Tumor and Immune Organs Reveal Systemic Immune Response to Tumor Development. Mol Cell Proteomics 2024; 23:100756. [PMID: 38554776 PMCID: PMC11060955 DOI: 10.1016/j.mcpro.2024.100756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 03/20/2024] [Accepted: 03/27/2024] [Indexed: 04/02/2024] Open
Abstract
In orthotopic mouse tumor models, tumor progression is a complex process, involving interactions among tumor cells, host cell-derived stromal cells, and immune cells. Much attention has been focused on the tumor and its tumor microenvironment, while the host's macroenvironment including immune organs in response to tumorigenesis is poorly understood. Here, we report a temporal proteomic analysis on a subcutaneous tumor and three immune organs (LN, MLN, and spleen) collected on Days 0, 3, 7, 10, 14, and 21 after inoculation of mouse forestomach cancer cells in a syngeneic mouse model. Bioinformatics analysis identified key biological processes during distinct tumor development phases, including an initial acute immune response, the attack by the host immune system, followed by the adaptive immune activation, and the build-up of extracellular matrix. Proteomic changes in LN and spleen largely recapitulated the dynamics of the immune response in the tumor, consistent with an acute defense response on D3, adaptive immune response on D10, and immune evasion by D21. In contrast, the immune response in MLN showed a gradual and sustained activation, suggesting a delayed response from a distal immune organ. Combined analyses of tumors and host immune organs allowed the identification of potential therapeutic targets. A proof-of-concept experiment demonstrated that significant growth reduction can be achieved by dual inhibition of MEK and DDR2. Together, our temporal proteomic dataset of tumors and immune organs provides a useful resource for understanding the interaction between tumors and the immune system and has the potential for identifying new therapeutic targets for cancer treatment.
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Affiliation(s)
- Zhike Li
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Shuwen Liu
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Zhouyong Gao
- Department of Thoracic Surgery, Baodi Clinical College, Tianjin Medical University, Tianjin, China; Department of Child Health Care, Kunshan Maternity and Child Health Care Institute, Kunshan, China
| | - Linlin Ji
- Department of Thoracic Surgery, Baodi Clinical College, Tianjin Medical University, Tianjin, China; Department of Thoracic Surgery, Weifang People's Hospital, Weifang, China
| | - Jiaqi Jiao
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Nairen Zheng
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Xianju Li
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Guangshun Wang
- Department of Thoracic Surgery, Baodi Clinical College, Tianjin Medical University, Tianjin, China
| | - Jun Qin
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Yi Wang
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China.
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3
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Li Z, Liu D, Wang Y, Wang C. Chemoproteomic Profiling of Erastin-Interacting Proteins. Chem Res Toxicol 2024; 37:109-116. [PMID: 38173279 DOI: 10.1021/acs.chemrestox.3c00347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Ferroptosis is an iron-related cell death caused by irregular lipid peroxidation that has been implicated with a variety of disease. Erastin is a canonical ferroptosis inducer that is known to function by inhibiting system Xc- and cystine transport; however, the global interactome of erastin in cells remains unexplored. In this work, we employed a quantitative chemoproteomic approach to profile direct interacting proteins of erastin in living cells using a multifunctional photo-cross-linking probe. A number of novel erastin-interacting proteins were identified, including a serine hydrolase, ABHD6, whose overexpression showed a potentiating impact on ferroptosis. Further biochemical experiments revealed that erastin can allosterically activate ABHD6's activity to produce more arachidonic acids and elevate the level of lipid reactive oxygen species. Collectively, our work provided a global portrait of erastin-interacting proteins and discovered ABHD6 as a new ferroptosis regulator.
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Affiliation(s)
- Zehua Li
- Department of Chemical Biology, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Dongyang Liu
- Department of Chemical Biology, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yankun Wang
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Chu Wang
- Department of Chemical Biology, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
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4
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Lu J, Zhu T, Dai Y, Xing L, Jinqi L, Zhou S, Kong C. The effect of heat treatment on the lactosylation of milk proteins. J Dairy Sci 2023; 106:8321-8330. [PMID: 37641337 DOI: 10.3168/jds.2023-23526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 05/22/2023] [Indexed: 08/31/2023]
Abstract
Protein lactosylation is a significant modification that occurs during the heat treatment of dairy products, causing changes in proteins' physical-chemical and nutritional properties. Knowledge of the detailed lactosylation information on milk proteins under various heat treatments is important for selecting appropriate thermo-processing and identifying markers to monitor heat load in dairy products. In the present study, we used proteomics techniques to investigate lactosylated proteins under different heating temperatures. We observed a total of 123 lactosylated lysines in 65 proteins, with lactosylation even occurring in raw milk. The number of lactosylated lysines and proteins increased moderately at 75°C to 130°C, but dramatically at 140°C. We found that 6 out of 10, 9 out of 16, 6 out of 12, and 5 out of 15 lysine residues in κ-casein, β-lactoglobulin, α-lactalbumin, and αS1-casein, respectively, were lactosylated under the applied heating treatment. Moreover, different lactosylation states of individual lysines and proteins can indicate the intensity of heating processes. Lactosylation of K14 in β-lactoglobulin could distinguish pasteurized and UHT milk, while lactosylation of lactotransferrin can reflect moderate heat treatment of products.
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Affiliation(s)
- Jing Lu
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, School of Food and Health, Beijing Technology and Business University, Beijing 100048, China.
| | - Tong Zhu
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, School of Food and Health, Beijing Technology and Business University, Beijing 100048, China
| | - Ying Dai
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, School of Food and Health, Beijing Technology and Business University, Beijing 100048, China
| | - Lina Xing
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, School of Food and Health, Beijing Technology and Business University, Beijing 100048, China
| | - Liu Jinqi
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, School of Food and Health, Beijing Technology and Business University, Beijing 100048, China
| | - Sumei Zhou
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, School of Food and Health, Beijing Technology and Business University, Beijing 100048, China
| | - Chunli Kong
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, School of Food and Health, Beijing Technology and Business University, Beijing 100048, China
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5
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Tian S, Zhan D, Yu Y, Wang Y, Liu M, Tan S, Li Y, Song L, Qin Z, Li X, Liu Y, Li Y, Ji S, Wang S, Zheng Y, He F, Qin J, Ding C. Quartet protein reference materials and datasets for multi-platform assessment of label-free proteomics. Genome Biol 2023; 24:202. [PMID: 37674236 PMCID: PMC10483797 DOI: 10.1186/s13059-023-03048-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 08/23/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND Quantitative proteomics is an indispensable tool in life science research. However, there is a lack of reference materials for evaluating the reproducibility of label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based measurements among different instruments and laboratories. RESULTS Here, we develop the Quartet standard as a proteome reference material with built-in truths, and distribute the same aliquots to 15 laboratories with nine conventional LC-MS/MS platforms across six cities in China. Relative abundance of over 12,000 proteins on 816 mass spectrometry files are obtained and compared for reproducibility among the instruments and laboratories to ultimately generate proteomics benchmark datasets. There is a wide dynamic range of proteomes spanning about 7 orders of magnitude, and the injection order has marked effects on quantitative instead of qualitative characteristics. CONCLUSION Overall, the Quartet offers valuable standard materials and data resources for improving the quality control of proteomic analyses as well as the reproducibility and reliability of research findings.
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Affiliation(s)
- Sha Tian
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Dongdong Zhan
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Yunzhi Wang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Mingwei Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Subei Tan
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Yan Li
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Lei Song
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Zhaoyu Qin
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Xianju Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Yang Liu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Yao Li
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Shuhui Ji
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Shanshan Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
| | - Fuchu He
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.
| | - Chen Ding
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
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6
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Wang L, Wan F, Xu Y, Xie S, Zhao T, Zhang F, Yang H, Zhu J, Gao J, Shi X, Wang C, Lu L, Yang Y, Yu X, Chen S, Sun X, Ding J, Chen P, Ding C, Xu F, Yu H, Peng H. Hierarchical helical carbon nanotube fibre as a bone-integrating anterior cruciate ligament replacement. NATURE NANOTECHNOLOGY 2023; 18:1085-1093. [PMID: 37142709 DOI: 10.1038/s41565-023-01394-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 03/31/2023] [Indexed: 05/06/2023]
Abstract
High rates of ligament damage require replacements; however, current synthetic materials have issues with bone integration leading to implant failure. Here we introduce an artificial ligament that has the required mechanical properties and can integrate with the host bone and restore movement in animals. The ligament is assembled from aligned carbon nanotubes formed into hierarchical helical fibres bearing nanometre and micrometre channels. Osseointegration of the artificial ligament is observed in an anterior cruciate ligament replacement model where clinical polymer controls showed bone resorption. A higher pull-out force is found after a 13-week implantation in rabbit and ovine models, and animals can run and jump normally. The long-term safety of the artificial ligament is demonstrated, and the pathways involved in integration are studied.
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Affiliation(s)
- Liyuan Wang
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science and Laboratory of Advanced Materials, Fudan University, Shanghai, China
| | - Fang Wan
- Department of Orthopedic Sports Medicine, Huashan Hospital, The Sports Medicine Institute, Fudan University, Shanghai, China
| | - Yifan Xu
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science and Laboratory of Advanced Materials, Fudan University, Shanghai, China
| | - Songlin Xie
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science and Laboratory of Advanced Materials, Fudan University, Shanghai, China
| | - Tiancheng Zhao
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science and Laboratory of Advanced Materials, Fudan University, Shanghai, China
| | - Fan Zhang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Han Yang
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science and Laboratory of Advanced Materials, Fudan University, Shanghai, China
| | - Jiajun Zhu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jingming Gao
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science and Laboratory of Advanced Materials, Fudan University, Shanghai, China
| | - Xiang Shi
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science and Laboratory of Advanced Materials, Fudan University, Shanghai, China
| | - Chuang Wang
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science and Laboratory of Advanced Materials, Fudan University, Shanghai, China
| | - Linwei Lu
- Department of Integrative Medicine, Huashan Hospital, The Academy of Integrative Medicine, Fudan University, Shanghai, China
| | - Yifan Yang
- Department of Aeronautics and Astronautics, Fudan University, Shanghai, China
| | - Xiaoye Yu
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science and Laboratory of Advanced Materials, Fudan University, Shanghai, China
| | - Shiyi Chen
- Department of Orthopedic Sports Medicine, Huashan Hospital, The Sports Medicine Institute, Fudan University, Shanghai, China.
| | - Xuemei Sun
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science and Laboratory of Advanced Materials, Fudan University, Shanghai, China.
| | - Jiandong Ding
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science and Laboratory of Advanced Materials, Fudan University, Shanghai, China
| | - Peining Chen
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science and Laboratory of Advanced Materials, Fudan University, Shanghai, China.
| | - Chen Ding
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
| | - Fan Xu
- Department of Aeronautics and Astronautics, Fudan University, Shanghai, China
| | - Hongbo Yu
- Vision Research Laboratory, School of Life Sciences, State Key Laboratory of Medical Neurobiology, Collaborative Innovation Centre for Brain Science, Fudan University, Shanghai, China
| | - Huisheng Peng
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science and Laboratory of Advanced Materials, Fudan University, Shanghai, China.
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7
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Zhan D, Zheng N, Zhao B, Cheng F, Tang Q, Liu X, Wang J, Wang Y, Liua H, Li X, Su J, Zhong X, Bu Q, Cheng Y, Wang Y, Qin J. Expanding individualized therapeutic options via genoproteomics. Cancer Lett 2023; 560:216123. [PMID: 36907503 DOI: 10.1016/j.canlet.2023.216123] [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: 01/04/2023] [Revised: 03/08/2023] [Accepted: 03/09/2023] [Indexed: 03/13/2023]
Abstract
Clinical next-generation sequencing (NGS)2 tests have enabled treatment recommendations for cancer patients with driver gene mutations. Targeted therapy options for patients without driver gene mutations are currently unavailable. Herein, we performed NGS and proteomics tests on 169 formalin-fixed paraffin-embedded (FFPE)3 samples of non-small cell lung cancers (NSCLC, 65),4 colorectal cancers (CRC, 61),5 thyroid carcinomas (THCA, 14),6 gastric cancers (GC, 2),7 gastrointestinal stromal tumors (GIST, 11),8 and malignant melanomas (MM, 6).9 Of the 169 samples, NGS detected 14 actionable mutated genes in 73 samples, providing treatment options for 43% of the patients. Proteomics identified 61 actionable clinical drug targets approved by the FDA or undergoing clinical trials in 122 samples, providing treatment options for 72% of the patients. In vivo experiments demonstrated that the Mitogen-Activated Protein Kinase (MEK)10 inhibitor induced the overexpression of MEK1 (Map2k1) to block lung tumor growth in mice. Therefore, protein overexpression is a potentially feasible indicator for guiding targeted therapies. Collectively, our analysis suggests that combining NGS and proteomics (genoproteomics) could expand the targeted treatment options to 85% of cancer patients.
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Affiliation(s)
- Dongdong Zhan
- KingMed-Pineal Joint Innovation Laboratory of Clinical Proteomics, Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou, 510009, China; Beijing Pineal Diagnostics Co., Ltd., Beijing, 102206, China
| | - Nairen Zheng
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Beibei Zhao
- KingMed-Pineal Joint Innovation Laboratory of Clinical Proteomics, Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou, 510009, China; KingMed College of Laboratory Medical of Guangzhou Medical University, Guangzhou, 510005, China
| | - Fang Cheng
- KingMed-Pineal Joint Innovation Laboratory of Clinical Proteomics, Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou, 510009, China; Beijing Pineal Diagnostics Co., Ltd., Beijing, 102206, China
| | - Qi Tang
- KingMed-Pineal Joint Innovation Laboratory of Clinical Proteomics, Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou, 510009, China; KingMed College of Laboratory Medical of Guangzhou Medical University, Guangzhou, 510005, China
| | - Xiangqian Liu
- KingMed-Pineal Joint Innovation Laboratory of Clinical Proteomics, Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou, 510009, China; KingMed College of Laboratory Medical of Guangzhou Medical University, Guangzhou, 510005, China
| | - Juanfei Wang
- KingMed-Pineal Joint Innovation Laboratory of Clinical Proteomics, Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou, 510009, China; KingMed College of Laboratory Medical of Guangzhou Medical University, Guangzhou, 510005, China
| | - Yushen Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Haibo Liua
- KingMed-Pineal Joint Innovation Laboratory of Clinical Proteomics, Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou, 510009, China; Beijing Pineal Diagnostics Co., Ltd., Beijing, 102206, China
| | - Xinliang Li
- KingMed-Pineal Joint Innovation Laboratory of Clinical Proteomics, Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou, 510009, China; Beijing Pineal Diagnostics Co., Ltd., Beijing, 102206, China
| | - Juming Su
- KingMed-Pineal Joint Innovation Laboratory of Clinical Proteomics, Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou, 510009, China; KingMed College of Laboratory Medical of Guangzhou Medical University, Guangzhou, 510005, China
| | - Xuejun Zhong
- KingMed-Pineal Joint Innovation Laboratory of Clinical Proteomics, Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou, 510009, China; KingMed College of Laboratory Medical of Guangzhou Medical University, Guangzhou, 510005, China
| | - Qing Bu
- Department of Medical Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Yating Cheng
- KingMed-Pineal Joint Innovation Laboratory of Clinical Proteomics, Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou, 510009, China; KingMed College of Laboratory Medical of Guangzhou Medical University, Guangzhou, 510005, China.
| | - Yi Wang
- KingMed-Pineal Joint Innovation Laboratory of Clinical Proteomics, Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou, 510009, China; State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.
| | - Jun Qin
- KingMed-Pineal Joint Innovation Laboratory of Clinical Proteomics, Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou, 510009, China; State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China; State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Fudan University, Shanghai, 200433, China.
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8
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Multilevel proteomic analyses reveal molecular diversity between diffuse-type and intestinal-type gastric cancer. Nat Commun 2023; 14:835. [PMID: 36788224 PMCID: PMC9929250 DOI: 10.1038/s41467-023-35797-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 01/03/2023] [Indexed: 02/16/2023] Open
Abstract
Diffuse-type gastric cancer (DGC) and intestinal-type gastric cancer (IGC) are the major histological types of gastric cancer (GC). The molecular mechanism underlying DGC and IGC differences are poorly understood. In this research, we carry out multilevel proteomic analyses, including proteome, phospho-proteome, and transcription factor (TF) activity profiles, of 196 cases covering DGC and IGC in Chinese patients. Integrative proteogenomic analysis reveals ARIDIA mutation associated with opposite prognostic effects between DGC and IGC, via diverse influences on their corresponding proteomes. Systematical comparison and consensus clustering analysis identify three subtypes of DGC and IGC, respectively, based on distinct patterns of the cell cycle, extracellular matrix organization, and immune response-related proteins expression. TF activity-based subtypes demonstrate that the disease progressions of DGC and IGC were regulated by SWI/SNF and NFKB complexes. Furthermore, inferred immune cell infiltration and immune clustering show Th1/Th2 ratio is an indicator for immunotherapeutic effectiveness, which is validated in an independent GC anti-PD1 therapeutic patient group. Our multilevel proteomic analyses enable a more comprehensive understanding of GC and can further advance the precision medicine.
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9
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Cancer proteomics: Application of case studies in diverse cancers. Proteomics 2023. [DOI: 10.1016/b978-0-323-95072-5.00003-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
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10
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Ni J, Tian S, Bai L, Lv Q, Liu J, Liu J, Fang Y, Zhai Y, Shen Q, Rao J, Ding C, Xu H. Comparative proteomic analysis of children FSGS FFPE tissues. BMC Pediatr 2022; 22:707. [PMID: 36503536 PMCID: PMC9743561 DOI: 10.1186/s12887-022-03764-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 11/21/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND In children, focal segmental glomerulosclerosis (FSGS) is the main cause of steroid resistant nephrotic syndrome (SRNS). To identify specific candidates and the mechanism of steroid resistance, we examined the formalin-fixed paraffin embedded (FFPE) renal tissue protein profiles via liquid chromatography tandem mass spectrometry (LC-MS/MS). METHODS Renal biopsies from seven steroid-sensitive (SS) and eleven steroid-resistant (SR) children FSGS patients were obtained. We examined the formalin-fixed paraffin embedded (FFPE) renal tissue protein profiles via liquid chromatography tandem mass spectrometry (LC-MS/MS). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment and Gene Ontology (GO) analysis, as well as the construction of protein-protein interaction (PPI) network were performed. Two proteins were further valiadated by immunohistochemistry staining in FSGS patients and mice models. RESULTS In total, we quantified more than 4000 proteins, of which 325 were found to be differentially expressed proteins (DEPs) between the SS and SR group (foldchange ≥2, P<0.05). The results of GO revealed that the most significant up-regulated proteins were primarily related to protein transportation, regulation of the complement activation process and cytolysis. Moreover, clustering analysis showed differences in the pathways (lysosome, terminal pathway of complement) between the two groups. Among these potential candidates, validation analyses for LAMP1 and ACSL4 were conducted. LAMP1 was observed to have a higher expression in glomerulus, while ACSL4 was expressed more in tubular epithelial cells. CONCLUSIONS In this study, the potential mechanism and candidates related to steroid resistance in children FSGS patients were identified. It could be helpful in identifying potential therapeutic targets and predicting outcomes with these proteomic changes for children FSGS patients.
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Affiliation(s)
- Jiajia Ni
- grid.411333.70000 0004 0407 2968Department of Nephrology, Children’s Hospital of Fudan University, National Pediatric Medical Center of China, Shanghai, China ,Kidney Development and Pediatric Kidney Disease Research Center, Shanghai, China
| | - Sha Tian
- grid.413087.90000 0004 1755 3939State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433 China
| | - Lin Bai
- grid.413087.90000 0004 1755 3939State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433 China
| | - Qianying Lv
- grid.411333.70000 0004 0407 2968Department of Nephrology, Children’s Hospital of Fudan University, National Pediatric Medical Center of China, Shanghai, China ,Kidney Development and Pediatric Kidney Disease Research Center, Shanghai, China
| | - Jialu Liu
- grid.411333.70000 0004 0407 2968Department of Nephrology, Children’s Hospital of Fudan University, National Pediatric Medical Center of China, Shanghai, China ,Kidney Development and Pediatric Kidney Disease Research Center, Shanghai, China
| | - Jiaojiao Liu
- grid.411333.70000 0004 0407 2968Department of Nephrology, Children’s Hospital of Fudan University, National Pediatric Medical Center of China, Shanghai, China ,Kidney Development and Pediatric Kidney Disease Research Center, Shanghai, China
| | - Ye Fang
- grid.411333.70000 0004 0407 2968Department of Nephrology, Children’s Hospital of Fudan University, National Pediatric Medical Center of China, Shanghai, China ,Kidney Development and Pediatric Kidney Disease Research Center, Shanghai, China
| | - Yihui Zhai
- grid.411333.70000 0004 0407 2968Department of Nephrology, Children’s Hospital of Fudan University, National Pediatric Medical Center of China, Shanghai, China ,Kidney Development and Pediatric Kidney Disease Research Center, Shanghai, China
| | - Qian Shen
- grid.411333.70000 0004 0407 2968Department of Nephrology, Children’s Hospital of Fudan University, National Pediatric Medical Center of China, Shanghai, China ,Kidney Development and Pediatric Kidney Disease Research Center, Shanghai, China
| | - Jia Rao
- grid.411333.70000 0004 0407 2968Department of Nephrology, Children’s Hospital of Fudan University, National Pediatric Medical Center of China, Shanghai, China ,Kidney Development and Pediatric Kidney Disease Research Center, Shanghai, China
| | - Chen Ding
- grid.413087.90000 0004 1755 3939State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433 China
| | - Hong Xu
- grid.411333.70000 0004 0407 2968Department of Nephrology, Children’s Hospital of Fudan University, National Pediatric Medical Center of China, Shanghai, China ,Kidney Development and Pediatric Kidney Disease Research Center, Shanghai, China
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11
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Lao Y, Li Y, Wang W, Ren L, Qian X, He F, Chen X, Jiang Y. A Cytological Atlas of the Human Liver Proteome from PROTEOME SKY-LIVER Hu 2.0, a Publicly Available Database. J Proteome Res 2022; 21:1916-1929. [PMID: 35820117 DOI: 10.1021/acs.jproteome.2c00190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The liver plays a unique role as a metabolic center of the body, and also performs other important functions such as detoxification and immune response. Here, we establish a cell type-resolved healthy human liver proteome including hepatocytes (HCs), hepatic stellate cells (HSCs), Kupffer cells (KCs), and liver sinusoidal endothelial cells (LSECs) by high-resolution mass spectrometry. Overall, we quantify total 8354 proteins for four cell types and over 6000 proteins for each cell type. Analysis of this data set and regulatory pathway reveals the cellular labor division in the human liver follows the pattern that parenchymal cells make the main components of pathways, but nonparenchymal cells trigger these pathways. Human liver cells show some novel molecular features: HCs maintain KCs and LSECs homeostasis by producing cholesterol and ketone bodies; HSCs participate in xenobiotics metabolism as an agent deliverer; KCs and LSECs mediate immune response through MHC class II-TLRs and MHC class I-TGFβ cascade, respectively; and KCs play a central role in diurnal rhythms regulation through sensing diurnal IGF and temperature flux. Together, this work expands our understandings of liver physiology and provides a useful resource for future analyses of normal and diseased livers.
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Affiliation(s)
- Yuanxiang Lao
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center of Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.,Department of Hepatobiliary Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Yanyan Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center of Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.,Key Laboratory for Regenerative Medicine of Ministry of Education, Institute of Hematology, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Wei Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center of Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Liangliang Ren
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center of Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Xiaohong Qian
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center of Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center of Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Xinguo Chen
- Institute of Liver Transplantation, The Third Medical Center, Chinese PLA General Hospital, Beijing 100039, China
| | - Ying Jiang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center of Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.,Anhui Medical University, Hefei 230031, China
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12
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Bi Y, Liu J, Ma M, Tao L, Peng Y, Dai X, Ji Z, Bai R, Jian M, Chen T, Luo L, Wang F, Ding Z, Liu A, Bao F. Comparative proteomics profiling revealed the involvement of GRB2-ROCK2 axis in Lyme neuroborreliosis caused by Borrelia Burgdorferi. J Cell Mol Med 2022; 26:2312-2321. [PMID: 35212166 PMCID: PMC8995463 DOI: 10.1111/jcmm.17253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 01/23/2022] [Accepted: 02/11/2022] [Indexed: 11/28/2022] Open
Abstract
The zoonotic Lyme neuroborreliosis (LNB) disease is caused by Borrelia burgdorferi, with wide distribution, rapid dissemination and high disability rate. However, the molecular mechanism underlying B. burgdorferi mediated neuroborreliosis remains largely unknown. Here, the frontal cortex from rhesus brains was incubated with B. burgdorferi, and proteomics profiling was evaluated by isobaric tag for relative and absolute quantitation. Proteins were identified and quantified, and differentially expressed proteins (DEPs) were isolated by comparing co-cultured samples and control samples. A total of 43, 164 and 368 DEPs were significantly altered after 6, 12 and 24 h treatment with B. burgdorferi respectively. Gene ontology and KEGG pathway analyses revealed that chemokine biological process was significantly enriched. Two genes in chemokine pathway including GRB2 and ROCK2 were significantly up-regulated after B. burgdorferi co-culturing. By in vitro assay, we confirmed that the expression of GRB2 and ROCK2 was increased after B. burgdorferi infection. In conclusion, our study revealed the involvement of chemokine pathway in the pathogenesis of LNB. GRB2 and ROCK2 may be novel biomarkers and therapeutic targets for LNB.
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Affiliation(s)
- Yunfeng Bi
- Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities, Kunming Medical University, Kunming, China.,Department of Dermatology, the Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jianjun Liu
- Yunnan Key Laboratory of Stem Cell and Regenerative Medicine, Biomedical Engineering Research Center, Kunming Medical University, Kunming, China
| | - Mingbiao Ma
- Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities, Kunming Medical University, Kunming, China
| | - Lvyan Tao
- Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities, Kunming Medical University, Kunming, China
| | - Yun Peng
- Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities, Kunming Medical University, Kunming, China
| | - Xiting Dai
- Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities, Kunming Medical University, Kunming, China
| | - Zhenhua Ji
- Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities, Kunming Medical University, Kunming, China
| | - Ruolan Bai
- Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities, Kunming Medical University, Kunming, China
| | - Miaomiao Jian
- Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities, Kunming Medical University, Kunming, China
| | - Taigui Chen
- Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities, Kunming Medical University, Kunming, China
| | - Lisha Luo
- Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities, Kunming Medical University, Kunming, China
| | - Feng Wang
- Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities, Kunming Medical University, Kunming, China
| | - Zhe Ding
- Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities, Kunming Medical University, Kunming, China
| | - Aihua Liu
- Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities, Kunming Medical University, Kunming, China
| | - Fukai Bao
- Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities, Kunming Medical University, Kunming, China
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13
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Wei Z, Kang J, Liao M, Ju H, Fan R, Shang J, Ning X, Li M. Investigating changes of proteome in the bovine milk serum after retort processing using proteomics techniques. Food Sci Nutr 2022; 10:307-316. [PMID: 35154669 PMCID: PMC8825719 DOI: 10.1002/fsn3.2300] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 03/25/2021] [Accepted: 04/11/2021] [Indexed: 12/14/2022] Open
Abstract
The objective of this study was to investigate the changes of the proteins in bovine milk serum after retort processing by label-free quantification proteomics techniques. A total of 96 and 106 proteins were quantified in control group (CG) and retort group (RG), respectively. Hierarchical clustering analysis of the identified milk serum proteins showed a decrease in the abundance of most proteins, such as serum albumin, lactoperoxidase, lactotransferrin, and complement C3, and an increase in the abundance of other proteins such as κ-casein, lipocalin 2, and Perilipin. Student's t-test showed 21 proteins significantly differential abundance between CG and RG (p < .05), of which intensity-based absolute quantification (iBAQ) of five proteins decreased and iBAQ of 16 proteins increased. Bioinformatics analysis demonstrated that retort processing increased the digestibility of proteins, but this improvement was offset by a decrease in the digestibility of proteins caused by protein modification. Our results provide insight into the proteome of retort sterilized milk for the first time. Given the extremely high security of retort sterilized milk, the proteome of bovine milk serum changes after retort sterilization exposed in this study will contribute to the formula design of retort sterilized milk products.
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Affiliation(s)
- Zikai Wei
- Key Laboratory of Dairy ScienceMinistry of EducationNortheast Agricultural UniversityHarbinChina
| | - Jiaxin Kang
- Key Laboratory of Dairy ScienceMinistry of EducationNortheast Agricultural UniversityHarbinChina
| | - Minhe Liao
- Key Laboratory of Dairy ScienceMinistry of EducationNortheast Agricultural UniversityHarbinChina
| | - Huanhuan Ju
- Key Laboratory of Dairy ScienceMinistry of EducationNortheast Agricultural UniversityHarbinChina
| | - Rong Fan
- Key Laboratory of Dairy ScienceMinistry of EducationNortheast Agricultural UniversityHarbinChina
| | - Jiaqi Shang
- Key Laboratory of Dairy ScienceMinistry of EducationNortheast Agricultural UniversityHarbinChina
| | - Xuenan Ning
- Key Laboratory of Dairy ScienceMinistry of EducationNortheast Agricultural UniversityHarbinChina
| | - Meng Li
- Key Laboratory of Dairy ScienceMinistry of EducationNortheast Agricultural UniversityHarbinChina
- College of Food ScienceNortheast Agricultural UniversityHarbinChina
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14
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Liu J, Lian H, Yu J, Wu J, Chen X, wang P, tian L, Yang Y, Yang J, Li D, Guo S. Study on diverse pathological characteristics of heart failure in different stages based on proteomics. J Cell Mol Med 2022; 26:1169-1182. [PMID: 35048506 PMCID: PMC8831959 DOI: 10.1111/jcmm.17170] [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: 06/10/2021] [Revised: 12/14/2021] [Accepted: 12/21/2021] [Indexed: 11/30/2022] Open
Abstract
Heart failure is a process characterized by significant disturbance of protein turnover. To elucidate the alterations in cardiac protein expression during the various phases of heart failure and to understand the nature of the processes involved, we analysed the proteome in an established heart failure model at different time points to monitor thousands of different proteins simultaneously. Here, heart failure was induced by transverse aortic constriction (TAC) in KM mice. At 2, 4 and 12 weeks after operation, protein expression profiles were determined in sham‐operated (controls) and TAC mice, using label‐free quantitative proteomics, leading to identification and quantification of almost 4000 proteins. The results of the KEGG pathway enrichment analysis and GO function annotation revealed critical pathways associated with the transition from cardiac hypertrophy to heart failure, such as energy pathways and matrix reorganization. Our study suggests that in the pathophysiology of heart failure, alterations of protein groups related to cardiac energy substrate metabolism and cytoskeleton remodelling could play the more dominant roles for the signalling that eventually results in contractile dysfunction and heart failure.
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Affiliation(s)
- Jinying Liu
- College of Traditional Chinese Medicine Chengde Medical University Chengde Hebei Province China
- School of Traditional Chinese Medicine Beijing University of Chinese Medicine Beijing China
| | - Hongjian Lian
- School of Traditional Chinese Medicine Beijing University of Chinese Medicine Beijing China
- Alexa League Central Hospital Inner Mongolia China
| | - Jiang Yu
- School of Traditional Chinese Medicine Beijing University of Chinese Medicine Beijing China
| | - Jie Wu
- College of Traditional Chinese Medicine Chengde Medical University Chengde Hebei Province China
| | - Xiangyang Chen
- Youcare Pharmaceutical Group Drug Research Institute Beijing China
| | - Peng wang
- College of Traditional Chinese Medicine Chengde Medical University Chengde Hebei Province China
| | - Lei tian
- School of Traditional Chinese Medicine Beijing University of Chinese Medicine Beijing China
| | - Yunfei Yang
- Beijing Qinglian Biotech Co., Ltd Beijing China
| | - Jiaqi Yang
- College of Traditional Chinese Medicine Chengde Medical University Chengde Hebei Province China
| | - Dong Li
- School of Basic Medical Sciences Anhui Medical University Hefei China
- State Key Laboratory of Proteomics Beijing Proteome Research Center National Center for Protein Sciences (PHOENIX Center) Beijing Institute of Lifeomics Beijing China
| | - Shuzhen Guo
- School of Traditional Chinese Medicine Beijing University of Chinese Medicine Beijing China
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15
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Chen Y, Wang C. Profiling of Protein Carbonylations in Ferroptosis by Chemical Proteomics. Methods Mol Biol 2022; 2543:141-153. [PMID: 36087265 DOI: 10.1007/978-1-0716-2553-8_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Ferroptosis is a new form of cell death with hallmark of lipid peroxidation and iron accumulation. It has been shown that lipid peroxidation can result in electrophilic metabolites which in turn induce protein carbonylations. Identification of specific carbonylated proteins and sites in ferroptotic cells will be of great significance for understanding the mechanism and discovering potential biomarkers for this new cell death. The protocol described herein is an optimized pipeline which combines the labeling of carbonylated proteins by a commercially available aniline-based probe with the tandem orthogonal proteolysis activity-based protein profiling (TOP-ABPP) strategy to portrait the landscape of carbonylations in ferroptotic cells.
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Affiliation(s)
- Ying Chen
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
| | - Chu Wang
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, China.
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.
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16
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Jiang S, Pan J, Li Y, Ju M, Zhang W, Lu J, Lv J, Li K. Comprehensive Human Milk Patterns Are Related to Infant Growth and Allergy in the CHMP Study. Mol Nutr Food Res 2021; 65:e2100011. [PMID: 34227225 DOI: 10.1002/mnfr.202100011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 06/10/2021] [Indexed: 12/26/2022]
Abstract
SCOPE The aim of the present study is to identify human milk pattern using multi-omics datasets and to explore association between patterns, infant growth, and allergy using data from the Chinese Human Milk Project (CHMP) study. METHODS AND RESULTS Three patterns are identified from integrative analysis of proteome, lipidome, and glycome profiles of 143 mature human milk samples. Factor 1 is positively associated with 128 proteins, phospholipids, and human milk oligosaccharides (HMOs) including lacto N-neohexaose (LNnH) and lacto-N-difucohexaose II (LNDFH II); factor 2 is negatively associated with as1 -casein, phospholipids while positively associates with HMOs including LNnH, lactosialyl tetrasaccharide c (LSTc), and 2'-fucosyllactose (2'FL); factor 3 is positively associated with lysophospholipids while negatively associates with 27 proteins, triglycerides with two saturated fatty acids, 6'-sialyllactose (6'SL) and 2'FL. In general, factor 1 and factor 2 are associated with slower while factor 3 is associated with faster growth rate (p < 0.044). One unit higher in loadings of factor 2 is associated with 34% lower risk of allergies (p ≤ 0.017). Associations are not significant after adjustment for city except for factor 1. CONCLUSIONS Three possible human milk patterns with varying degree of stability are identified. Future work is needed to understand these patterns in terms of generalization, biologic mechanisms, and genotype influences.
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Affiliation(s)
- Shilong Jiang
- Nutrition and Metabolism Research Division, Innovation Center, Heilongjiang Feihe Dairy Co., Ltd., Beijing, 100015, China.,PKUHSC-China Feihe Joint Research Institute of Nutrition and Healthy Lifespan Development, Xueyuan Road 38, Haidian, Beijing, 100083, China
| | - Jiancun Pan
- Nutrition and Metabolism Research Division, Innovation Center, Heilongjiang Feihe Dairy Co., Ltd., Beijing, 100015, China.,PKUHSC-China Feihe Joint Research Institute of Nutrition and Healthy Lifespan Development, Xueyuan Road 38, Haidian, Beijing, 100083, China
| | - Yuanyuan Li
- Nutrition and Metabolism Research Division, Innovation Center, Heilongjiang Feihe Dairy Co., Ltd., Beijing, 100015, China.,PKUHSC-China Feihe Joint Research Institute of Nutrition and Healthy Lifespan Development, Xueyuan Road 38, Haidian, Beijing, 100083, China
| | - Mengnan Ju
- Nutrition and Metabolism Research Division, Innovation Center, Heilongjiang Feihe Dairy Co., Ltd., Beijing, 100015, China.,PKUHSC-China Feihe Joint Research Institute of Nutrition and Healthy Lifespan Development, Xueyuan Road 38, Haidian, Beijing, 100083, China
| | - Wei Zhang
- Nutrition and Metabolism Research Division, Innovation Center, Heilongjiang Feihe Dairy Co., Ltd., Beijing, 100015, China.,PKUHSC-China Feihe Joint Research Institute of Nutrition and Healthy Lifespan Development, Xueyuan Road 38, Haidian, Beijing, 100083, China
| | - Jing Lu
- Key Laboratory of Agro-Food Processing and Quality Control, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China.,School of Food and Health, Beijing Technology and Business University, Fucheng Road 11, Haidian, Beijing, 100048, China
| | - Jiaping Lv
- Key Laboratory of Agro-Food Processing and Quality Control, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Kaifeng Li
- Nutrition and Metabolism Research Division, Innovation Center, Heilongjiang Feihe Dairy Co., Ltd., Beijing, 100015, China.,PKUHSC-China Feihe Joint Research Institute of Nutrition and Healthy Lifespan Development, Xueyuan Road 38, Haidian, Beijing, 100083, China
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17
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Huang W, Zhan D, Li Y, Zheng N, Wei X, Bai B, Zhang K, Liu M, Zhao X, Ni X, Xia X, Shi J, Zhang C, Lu Z, Ji J, Wang J, Wang S, Ji G, Li J, Nie Y, Liang W, Wu X, Cui J, Meng Y, Cao F, Shi T, Zhu W, Wang Y, Chen L, Zhao Q, Wang H, Shen L, Qin J. Proteomics provides individualized options of precision medicine for patients with gastric cancer. SCIENCE CHINA-LIFE SCIENCES 2021; 64:1199-1211. [PMID: 34258712 DOI: 10.1007/s11427-021-1966-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 06/17/2021] [Indexed: 12/19/2022]
Abstract
While precision medicine driven by genome sequencing has revolutionized cancer care, such as lung cancer, its impact on gastric cancer (GC) has been minimal. GC patients are routinely treated with chemotherapy, but only a fraction of them receive the clinical benefit. There is an urgent need to develop biomarkers or algorithms to select chemo-sensitive patients or apply targeted therapy. Here, we carried out retrospective analyses of 1,020 formalin-fixed, paraffin-embedded GC surgical resection samples from 5 hospitals and developed a mass spectrometry-based workflow for proteomic subtyping of GC. We identified two proteomic subtypes: the chemo-sensitive group (CSG) and the chemo-insensitive group (CIG) in the discovery set. The 5-year overall survival of CSG was significantly improved in patients who had received adjuvant chemotherapy after surgery compared with those who received surgery only (64.2% vs. 49.6%; Cox P-value=0.002), whereas no such improvement was observed in CIG (50.0% vs. 58.6%; Cox P-value=0.495). We validated these results in an independent validation set. Further, differential proteome analysis uncovered 9 FDA-approved drugs that may be applicable for targeted therapy of GC. A prospective study is warranted to test these findings for future GC patient care.
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Affiliation(s)
- Wenwen Huang
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Dongdong Zhan
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.,Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Yazhuo Li
- Department of Pathology, The Fourth Medical Center of PLA General Hospital, Beijing, 100048, China
| | - Nairen Zheng
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Xin Wei
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.,Life Sciences Institute, Zhejiang University, Hangzhou, 310058, China
| | - Bin Bai
- State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases and Department of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China
| | - Kecheng Zhang
- Department of General Surgery & Institute of General Surgery, Chinese PLA General Hospital First Medical Center, Beijing, 100853, China
| | - Mingwei Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Xuefei Zhao
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Xiaotian Ni
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Xia Xia
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Jinwen Shi
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Cheng Zhang
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Zhihao Lu
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Jiafu Ji
- Department of Gastrointestinal Surgery, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Juan Wang
- State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases and Department of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China
| | - Shiqi Wang
- State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases and Department of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China
| | - Gang Ji
- State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases and Department of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China
| | - Jipeng Li
- State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases and Department of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China
| | - Yongzhan Nie
- State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases and Department of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China
| | - Wenquan Liang
- Department of General Surgery & Institute of General Surgery, Chinese PLA General Hospital First Medical Center, Beijing, 100853, China
| | - Xiaosong Wu
- Department of General Surgery & Institute of General Surgery, Chinese PLA General Hospital First Medical Center, Beijing, 100853, China
| | - Jianxin Cui
- Department of General Surgery & Institute of General Surgery, Chinese PLA General Hospital First Medical Center, Beijing, 100853, China
| | - Yongsheng Meng
- Department of tumor biobank, Shanxi Cancer Hospital, Taiyuan, 030013, China
| | - Feilin Cao
- Department of tumor biobank, Shanxi Cancer Hospital, Taiyuan, 030013, China
| | - Tieliu Shi
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Weimin Zhu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Yi Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Lin Chen
- Department of General Surgery & Institute of General Surgery, Chinese PLA General Hospital First Medical Center, Beijing, 100853, China.
| | - Qingchuan Zhao
- State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases and Department of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China.
| | - Hongwei Wang
- Department of Pathology, The Fourth Medical Center of PLA General Hospital, Beijing, 100048, China.
| | - Lin Shen
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, 100142, China.
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China. .,State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Fudan University, Shanghai, 200433, China.
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18
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Huang P, Liu C, Gao W, Chu B, Cai Z, Tian R. Synergistic optimization of Liquid Chromatography and Mass Spectrometry parameters on Orbitrap Tribrid mass spectrometer for high efficient data-dependent proteomics. JOURNAL OF MASS SPECTROMETRY : JMS 2021; 56:e4653. [PMID: 32924238 DOI: 10.1002/jms.4653] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 06/09/2020] [Accepted: 07/16/2020] [Indexed: 06/11/2023]
Abstract
Steady improvement in Orbitrap-based mass spectrometry (MS) technologies has greatly advanced the peptide sequencing speed and depth. In-depth analysis of the performance of state-of-the-art MS and optimization of key parameters can improve sequencing efficiency. In this study, we first systematically compared the performance of two popular data-dependent acquisition approaches, with Orbitrap as the first-stage (MS1) mass analyzer and the same Orbitrap (high-high approach) or ion trap (high-low approach) as the second-stage (MS2) mass analyzer, on the Orbitrap Fusion mass spectrometer. High-high approach outperformed high-low approach in terms of better saturation of the scan cycle and higher MS2 identification rate. However, regardless of the acquisition method, there are still more than 60% of peptide features untargeted for MS2 scan. We then systematically optimized the MS parameters using the high-high approach. Increasing the isolation window in the high-high approach could facilitate faster scan speed, but decreased MS2 identification rate. On the contrary, increasing the injection time of MS2 scan could increase identification rate but decrease scan speed and the number of identified MS2 spectra. Dynamic exclusion time should be set properly according to the chromatography peak width. Furthermore, we found that the Orbitrap analyzer, rather than the analytical column, was easily saturated with higher loading amount, thus limited the dynamic range of MS1-based quantification. By using optimized parameters, 10 000 proteins and 110 000 unique peptides were identified by using 20 h of effective liquid chromatography (LC) gradient time. The study therefore illustrated the importance of synchronizing LC-MS precursor ion targeting, fragment ion detection, and chromatographic separation for high efficient data-dependent proteomics.
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Affiliation(s)
- Peiwu Huang
- Department of Chemistry, Southern University of Science and Technology, Shenzhen, 518055, China
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, SAR, China
| | - Chao Liu
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China
| | - Weina Gao
- Department of Chemistry, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Bizhu Chu
- Department of Chemistry, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, SAR, China
| | - Ruijun Tian
- Department of Chemistry, Southern University of Science and Technology, Shenzhen, 518055, China
- Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Southern University of Science and Technology, Shenzhen, 518055, China
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19
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Tian J, Chen C, Sun H, Wang Z, Steinkellner S, Feng J, Liang Y. Proteomic Analysis Reveals the Importance of Exudates on Sclerotial Development in Sclerotinia sclerotiorum. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:1430-1440. [PMID: 33481591 DOI: 10.1021/acs.jafc.0c06685] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Sclerotinia sclerotiorum is a ubiquitous necrotrophic pathogenic fungus causing significant losses in a broad range of plant species. Sclerotia formed by S. sclerotiorum play important roles in both the fungal life cycle and the disease development cycle. Sclerotial exudation during sclerotial development is a characteristic feature of this fungus. In this study, a proteome-level investigation of proteins present in sclerotial exudates was conducted by high-throughput LC-MS/MS analysis. A total of 258 proteins were identified, in which 193 were annotated by GO annotation and 54 were classified by KEGG analysis. Four proteins related to plant cell wall degradation were further validated by measuring the corresponding enzymatic activity of the sclerotial exudates and/or by assessing the gene expression during sclerotial development. Results indicated that the proteins identified in sclerotial exudates help in the development of sclerotia and contribute to host cell necrosis caused by S. sclerotiorum. Furthermore, we proposed that sclerotial exudates can degrade plant cell walls to release carbohydrates that provide nutrition for fungal growth and possibly facilitate fungal cell wall assembly in developing sclerotia. This study also provides new insights on the morphogenesis and pathogenicity of other sclerotia-forming fungi.
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Affiliation(s)
- Jiamei Tian
- College of Plant Protection, Shenyang Agricultural University, Shenyang, Liaoning 110866, China
- Liaoning Key Laboratory of Plant Pathology, Shenyang Agricultural University, Shenyang, Liaoning 110866, China
| | - Caixia Chen
- College of Plant Protection, Shenyang Agricultural University, Shenyang, Liaoning 110866, China
- Liaoning Key Laboratory of Plant Pathology, Shenyang Agricultural University, Shenyang, Liaoning 110866, China
| | - Huiying Sun
- College of Plant Protection, Shenyang Agricultural University, Shenyang, Liaoning 110866, China
- Liaoning Key Laboratory of Plant Pathology, Shenyang Agricultural University, Shenyang, Liaoning 110866, China
| | - Zehao Wang
- College of Plant Protection, Shenyang Agricultural University, Shenyang, Liaoning 110866, China
- Liaoning Key Laboratory of Plant Pathology, Shenyang Agricultural University, Shenyang, Liaoning 110866, China
| | - Siegrid Steinkellner
- Division of Plant Protection, Department of Crop Sciences, University of Natural Resources and Life Sciences, Vienna 1190, Austria
| | - Jie Feng
- Alberta Plant Health Lab, Alberta Agriculture and Forestry, Edmonton, Alberta T5Y 6H3, Canada
| | - Yue Liang
- College of Plant Protection, Shenyang Agricultural University, Shenyang, Liaoning 110866, China
- Liaoning Key Laboratory of Plant Pathology, Shenyang Agricultural University, Shenyang, Liaoning 110866, China
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20
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Zhang F, Yang B, Wang Y, Zhu J, Liu J, Yu G, Qin J, Song W, Ding C. Time- and Dose-Resolved Proteome of PM 2.5-Exposure-Induced Lung Injury and Repair in Rats. J Proteome Res 2020; 19:3162-3175. [PMID: 32519869 DOI: 10.1021/acs.jproteome.0c00155] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In recent years, airborne fine particulate matter (PM2.5) is drawing more public attention due to its various physicochemical features and causing pathological harm, as proven by epidemiological and clinical studies. However, the mechanism of PM2.5-exposure-induced lung injury has not been fully characterized. Here, we established a PM2.5-induced rat injury model for both short-term and long-term exposures at different concentrations. We employed the Fast-seq technique to profile 6316 proteins and the catTFRE approach to profile 387 transcription factors (TFs) in the lung tissue. In short-term exposure, we elucidated gradually upregulated proteins enriched in response to oxidative stress, phagosome, and the extracellular matrix (ECM)-receptor interaction pathway. Long-term exposure mainly showed the immune response pathway to be consisting of increased lymphocytes and cytokines. Intriguingly, we found that immune-related proteins were recoverable during short-term exposure. During the process of PM2.5 exposure, upregulated proteins presented dose-dependence in the lung, including stress response at low dose, minor immune response at middle dose, and severe inflammatory response at high dose. This data set provides a rich resource to facilitate the understanding of PM2.5-induced lung damage and repair mechanism.
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Affiliation(s)
- Fan Zhang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai 200433, China
| | - Bing Yang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai 200433, China
| | - Yunzhi Wang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai 200433, China
| | - Jiajun Zhu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai 200433, China
| | - Jie Liu
- School of Public Health, Fudan University, Shanghai 200433, China
| | - Guoying Yu
- State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan center for outstanding overseas scientists of pulmonary fibrosis, College of Life Science, Institute of Biomedical Science, Henan Normal University, Xinxiang, Henan 453007, China
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Weimin Song
- School of Public Health, Fudan University, Shanghai 200433, China
| | - Chen Ding
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai 200433, China
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21
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Sun H, Kav NNV, Liang Y, Sun L, Chen W. Proteome of the fungus Phoma macdonaldii, the causal agent of black stem of sunflower. J Proteomics 2020; 225:103878. [PMID: 32535146 DOI: 10.1016/j.jprot.2020.103878] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 06/05/2020] [Accepted: 06/10/2020] [Indexed: 11/29/2022]
Abstract
Phoma macdonaldii causes black stem of sunflower, which severely affects sunflower yield and quality. There is currently little molecular information available for this pathogenic fungus. In this study, a global proteomic analysis of P. macdonaldii was performed to determine the biological characteristics and pathogenicity of this pathogen. A total of 1498 proteins were identified by LC-MS/MS in all biological replicates. Among the identified proteins, 1420 proteins were classified into the three main GO categories (biological process, cellular component, and molecular function) while 806 proteins were annotated into the five major KEGG database (metabolism, genetic information processing, environmental information processing, cellular processes, and organismal systems). The regulated expression levels of eight genes encoding selected identified proteins were investigated to assess their potential effects on fungal development and pathogenesis. To the best of our knowledge, this is the first study to characterize the proteome of the necrotrophic fungus P. macdonaldii. The presented results provide novel insights into the development and pathogenesis of P. macdonaldii and possibly other Phoma species. SIGNIFICANCE: Black stem of sunflower is a devastating disease caused by the necrotrophic fungus Phoma macdonaldii. Relatively little is known regarding the molecular characteristics of this pathogen, and no proteomic investigation has been reported. Thus, we conducted a global proteomic analysis of P. macdonaldii. Many proteins were found to be differentially regulated during fungal development and pathogenesis, suggesting they may be important for these two processes. This is the first proteomic study of P. macdonaldii, and the data presented herein will be useful for elucidating the molecular characteristics of this fungus as well as other Phoma species.
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Affiliation(s)
- Huiying Sun
- College of Plant Protection, Shenyang Agricultural University, Shenyang 110866, China; Liaoning Key Laboratory of Plant Pathology, Shenyang Agricultural University, Shenyang 110866, China
| | - Nat N V Kav
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G2P5, Canada
| | - Yue Liang
- College of Plant Protection, Shenyang Agricultural University, Shenyang 110866, China; Liaoning Key Laboratory of Plant Pathology, Shenyang Agricultural University, Shenyang 110866, China.
| | - Lin Sun
- College of Plant Protection, Shenyang Agricultural University, Shenyang 110866, China; Liaoning Key Laboratory of Plant Pathology, Shenyang Agricultural University, Shenyang 110866, China
| | - Weimin Chen
- Xinjiang Yili Vocational Technical College, Yining 835000, China
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22
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Gong T, Zhang C, Ni X, Li X, Li J, Liu M, Zhan D, Xia X, Song L, Zhou Q, Ding C, Qin J, Wang Y. A time-resolved multi-omic atlas of the developing mouse liver. Genome Res 2020; 30:263-275. [PMID: 32051188 PMCID: PMC7050524 DOI: 10.1101/gr.253328.119] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 01/17/2020] [Indexed: 12/13/2022]
Abstract
Liver organogenesis and development are composed of a series of complex, well-orchestrated events. Identifying key factors and pathways governing liver development will help elucidate the physiological and pathological processes including those of cancer. We conducted multidimensional omics measurements including protein, mRNA, and transcription factor (TF) DNA-binding activity for mouse liver tissues collected from embryonic day 12.5 (E12.5) to postnatal week 8 (W8), encompassing major developmental stages. These data sets reveal dynamic changes of core liver functions and canonical signaling pathways governing development at both mRNA and protein levels. The TF DNA-binding activity data set highlights the importance of TF activity in early embryonic development. A comparison between mouse liver development and human hepatocellular carcinoma (HCC) proteomic profiles reveal that more aggressive tumors are characterized with the activation of early embryonic development pathways, whereas less aggressive ones maintain liver function-related pathways that are elevated in the mature liver. This work offers a panoramic view of mouse liver development and provides a rich resource to explore in-depth functional characterization.
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Affiliation(s)
- Tongqing Gong
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Chunchao Zhang
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Xiaotian Ni
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Institute of Lifeomics, Beijing 102206, China.,Department of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Xianju Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Jin'e Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Mingwei Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Dongdong Zhan
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Institute of Lifeomics, Beijing 102206, China.,Department of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Xia Xia
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Lei Song
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Quan Zhou
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Chen Ding
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai 200433, China
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Institute of Lifeomics, Beijing 102206, China.,State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai 200433, China
| | - Yi Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Institute of Lifeomics, Beijing 102206, China.,Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
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23
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Wei P, Xie Y, Abel PW, Huang Y, Ma Q, Li L, Hao J, Wolff DW, Wei T, Tu Y. Transforming growth factor (TGF)-β1-induced miR-133a inhibits myofibroblast differentiation and pulmonary fibrosis. Cell Death Dis 2019; 10:670. [PMID: 31511493 PMCID: PMC6739313 DOI: 10.1038/s41419-019-1873-x] [Citation(s) in RCA: 97] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 06/15/2019] [Accepted: 08/20/2019] [Indexed: 02/07/2023]
Abstract
Transforming growth factor (TGF)-β1, a main profibrogenic cytokine in the progression of idiopathic pulmonary fibrosis (IPF), induces differentiation of pulmonary fibroblasts to myofibroblasts that produce high levels of collagen, leading to concomitantly loss of lung elasticity and function. Recent studies implicate the importance of microRNAs (miRNAs) in IPF but their regulation and individual pathological roles remain largely unknown. We used both RNA sequencing and quantitative RT-PCR strategies to systematically study TGF-β1-induced alternations of miRNAs in human lung fibroblasts (HFL). Our data show that miR-133a was significantly upregulated by TGF-β1 in a time- and concentration-dependent manner. Surprisingly, miR-133a inhibits TGF-β1-induced myofibroblast differentiation whereas miR-133a inhibitor enhances TGF-β1-induced myofibroblast differentiation. Interestingly, quantitative proteomics analysis indicates that miR-133a attenuates myofibroblast differentiation via targeting multiple components of TGF-β1 profibrogenic pathways. Western blot analysis confirmed that miR-133a down-regulates TGF-β1-induced expression of classic myofibroblast differentiation markers such as ɑ-smooth muscle actin (ɑ-SMA), connective tissue growth factor (CTGF) and collagens. miRNA Target Searcher analysis and luciferase reporter assays indicate that TGF-β receptor 1, CTGF and collagen type 1-alpha1 (Col1a1) are direct targets of miR-133a. More importantly, miR-133a gene transferred into lung tissues ameliorated bleomycin-induced pulmonary fibrosis in mice. Together, our study identified TGF-β1-induced miR-133a as an anti-fibrotic factor. It functions as a feed-back negative regulator of TGF-β1 profibrogenic pathways. Thus, manipulations of miR-133a expression may provide a new therapeutic strategy to halt and perhaps even partially reverse the progression of IPF.
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Affiliation(s)
- Peng Wei
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
- Department of Pharmacology and Neuroscience, Creighton University School of Medicine, Omaha, NE, 68178, USA
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yan Xie
- Department of Pharmacology and Neuroscience, Creighton University School of Medicine, Omaha, NE, 68178, USA
| | - Peter W Abel
- Department of Pharmacology and Neuroscience, Creighton University School of Medicine, Omaha, NE, 68178, USA
| | - Yapei Huang
- Department of Pharmacology and Neuroscience, Creighton University School of Medicine, Omaha, NE, 68178, USA
| | - Qin Ma
- Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Linghai Li
- Department of Anesthesiology, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, 101149, China
| | - Junfeng Hao
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Dennis W Wolff
- Kansas City University of Medicine and Biosciences-Joplin, Joplin, MO, 64804, USA
| | - Taotao Wei
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Yaping Tu
- Department of Pharmacology and Neuroscience, Creighton University School of Medicine, Omaha, NE, 68178, USA.
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24
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Liu X, Liu G, Zhu P, Wang Y, Wang J, Zhang W, Wang W, Li N, Wang X, Zhang C, Liu J, Shen X, Liu F. Characterization of seminal plasma proteomic alterations associated with the IVF and rescue-ICSI pregnancy in assisted reproduction. Andrology 2019; 8:407-420. [PMID: 31364287 DOI: 10.1111/andr.12687] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Revised: 06/25/2019] [Accepted: 07/02/2019] [Indexed: 12/12/2022]
Abstract
BACKGROUND Seminal plasma is a promising diagnostic fluid for male infertility. In assisted reproduction, the seminal plasma-based characteristics of normozoospermic men achieving successful clinical pregnancy through rescue intracytoplasmic sperm injection after in vitro fertilization failure remain unclear. OBJECTIVE To identify potential seminal plasma proteins to contribute to a new understanding of unexplained male factor infertility. MATERIALS AND METHODS An approach with isobaric tags for relative and absolute quantification labeling coupled with liquid chromatography matrix-assisted laser desorption ionization mass spectrometry was applied to investigate differentially expressed proteins in the seminal plasma of a rescue intracytoplasmic sperm injection pregnancy group versus an in vitro fertilization pregnancy group of normozoospermic men. RESULT(S) The present work revealed seventy-three differentially expressed seminal plasma proteins between the in vitro fertilization and rescue intracytoplasmic sperm injection groups. Forty-five proteins were upregulated, and 28 proteins were downregulated in the rescue intracytoplasmic sperm injection group compared with the in vitro fertilization group. Bioinformatics analyses showed that these altered proteins were involved in various functions, including the kallikrein-related proteolytic cascade, immune response, and heparin binding. Furthermore, the validity of the proteomic results was verified by Western blot analysis of the proteins (lactoferrin [LTF], fibronectin [FN1], creatine kinase B type [CKB], kallikrein-2 [KLK2], aminopeptidase N [ANPEP], extracellular matrix protein 1 [ECM1], glycodelin [PAEP], alpha-1-antitrypsin [SERPINA1], and semenogelin-1 [SEMG1]) and immunofluorescence. Moreover, 16% of the seminal plasma proteins identified in the present work have not been reported in previous studies. DISCUSSION This panel of altered seminal plasma proteins associated with unexplained male factor infertility might have clinical relevance and may be useful in the diagnosis and prognosis of idiopathic infertility in in vitro fertilization. CONCLUSIONS Our work not only provides a new complementary high-confidence dataset of seminal plasma proteins but also shines new light onto the molecular characteristics of seminal plasma from normozoospermic men with different assisted reproductive outcomes.
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Affiliation(s)
- X Liu
- Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, Shandong, China
| | - G Liu
- Reproductive Center, Tianjin Aiwei Hospital, Tianjin, China
| | - P Zhu
- Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, Shandong, China
| | - Y Wang
- Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, Shandong, China
| | - J Wang
- Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, Shandong, China
| | - W Zhang
- Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, Shandong, China
| | - W Wang
- Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, Shandong, China
| | - N Li
- Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, Shandong, China
| | - X Wang
- Department of Clinical Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, Shandong, China
| | - C Zhang
- Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, Shandong, China
| | - J Liu
- Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, Shandong, China
| | - X Shen
- Reproductive Center, Beijing BaoDao Obstetrics and Gynecology Hospital, Beijing, China
| | - F Liu
- Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, Shandong, China
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25
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Gao Z, Chang C, Yang J, Zhu Y, Fu Y. AP3: An Advanced Proteotypic Peptide Predictor for Targeted Proteomics by Incorporating Peptide Digestibility. Anal Chem 2019; 91:8705-8711. [DOI: 10.1021/acs.analchem.9b02520] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Zhiqiang Gao
- National Center for Mathematics and Interdisciplinary Sciences, Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Cheng Chang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Jinghan Yang
- National Center for Mathematics and Interdisciplinary Sciences, Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yunping Zhu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
- Anhui Medical University, Hefei 230032, China
| | - Yan Fu
- National Center for Mathematics and Interdisciplinary Sciences, Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
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26
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Wang L, Liu Q, Kitamoto T, Hou J, Qin J, Accili D. Identification of Insulin-Responsive Transcription Factors That Regulate Glucose Production by Hepatocytes. Diabetes 2019; 68:1156-1167. [PMID: 30936148 PMCID: PMC6610019 DOI: 10.2337/db18-1236] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 03/20/2019] [Indexed: 01/02/2023]
Abstract
Hepatocyte glucose production is a complex process that integrates cell-autonomous mechanisms with cellular signaling, enzyme activity modulation, and gene transcription. Transcriptional mechanisms controlling glucose production are redundant and involve nuclear hormone receptors and unliganded transcription factors (TFs). Our knowledge of this circuitry is incomplete. Here we used DNA affinity purification followed by mass spectrometry to probe the network of hormone-regulated TFs by using phosphoenolpyruvate carboxykinase (Pck1) and glucose-6-phosphatase (G6pc) in liver and primary hepatocytes as model systems. The repertoire of insulin-regulated TFs is unexpectedly broad and diverse. Whereas in liver the two test promoters are regulated by largely overlapping sets of TFs, in primary hepatocytes Pck1 and G6pc regulation diverges. Insulin treatment preferentially results in increased occupancy by the two promoters, consistent with a model in which the hormone's primary role is to recruit corepressors rather than to clear activators. Nine insulin-responsive TFs are present in both models, but only FoxK1, FoxA2, ZFP91, and ZHX3 require an intact Pck1p insulin response sequence for binding. Knockdown of FoxK1 in primary hepatocytes decreased both glucose production and insulin's ability to suppress it. The findings expand the repertoire of insulin-dependent TFs and identify FoxK1 as a contributor to insulin signaling.
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Affiliation(s)
- Liheng Wang
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY
- Naomi Berrie Diabetes Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY
| | - Qiongming Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences, Beijing, China
| | - Takumi Kitamoto
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY
- Naomi Berrie Diabetes Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY
| | - Junjie Hou
- National Laboratory of Biomacromolecules, Chinese Academy of Sciences Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences, Beijing, China
| | - Domenico Accili
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY
- Naomi Berrie Diabetes Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY
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27
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Qin Z, Xiang C, Zhong F, Liu Y, Dong Q, Li K, Shi W, Ding C, Qin L, He F. Transketolase (TKT) activity and nuclear localization promote hepatocellular carcinoma in a metabolic and a non-metabolic manner. J Exp Clin Cancer Res 2019; 38:154. [PMID: 30971297 PMCID: PMC6458711 DOI: 10.1186/s13046-019-1131-1] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 03/08/2019] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Metabolic reprogramming is one of the hallmarks of cancer cells. The pentose phosphate pathway (PPP), a branch of glycolysis, is an important metabolic pathway for the survival and biosynthesis of cancer cells. Transketolase (TKT) is a key enzyme in the non-oxidative phase of PPP. The mechanistic details of TKT in hepatocellular carcinoma (HCC) development remain unclear. METHODS TKT level and subcellular location were examined in HCC cell lines and tissue samples. We established the TKT overexpression and knocking-down stable cells in HCC cell lines. Proliferation, migration, viability and enzyme activity assays in vitro, tumor growth and metastasis assays in vivo were employed to test the effects of TKT on HCC development. GFP-tagged TKT truncations and mutants were used to locate the nuclear localization sequence (NLSs) of TKT. Cross-linking co-IP/MS was applied to identify the interaction proteins of nuclear TKT. RESULTS We showed that TKT increased the proliferation and migration of HCC cells, as well as the viability under oxidative stress in vitro and accelerated the growth and metastasis of HCC cells in vivo. We found as a key enzyme of PPP, TKT could promote the proliferation, cell cycle, migration and viability by regulating the metabolic flux. Moreover, it was firstly reported that unlike other key enzymes in PPP, TKT showed a strong nuclear localization in HCC cells. We found not only high TKT expression, but also its nuclear localization was a prediction for poor prognosis of HCC patients. We further identified the nuclear localization sequences (NLS) for TKT and demonstrated the NLS mutations decreased the pro-tumor function of TKT independent of the enzyme activity. Cross-linking Co-IP/MS showed that nuclear TKT interacted with kinases and transcriptional coregulators such as EGFR and MAPK3, which are associated with cell activation or stress response processes. EGF treatment significantly increased the viability and proliferation of HCC cells in the enzyme-inactivating mutation TKT-D155A overexpression cells but not in the NLS-D155A double mutant group, which could be blocked by EGFR inhibitor erlotinib treatment. CONCLUSIONS Our research suggests that in addition to the metabolic manner, TKT can promote the development of HCC in a non-metabolic manner via its nuclear localization and EGFR pathway.
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Affiliation(s)
- Zhaoyu Qin
- Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032 China
| | - Chan Xiang
- Department of Pathology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030 China
| | - Fan Zhong
- Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032 China
| | - Yang Liu
- Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032 China
| | - Qiongzhu Dong
- Department of Surgery, Huashan Hospital, Cancer Metastasis Institute, Fudan University, Shanghai, 200040 China
| | - Kai Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing, 102206 China
| | - Wenhao Shi
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing, 102206 China
| | - Chen Ding
- Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032 China
| | - Lunxiu Qin
- Department of Surgery, Huashan Hospital, Cancer Metastasis Institute, Fudan University, Shanghai, 200040 China
| | - Fuchu He
- Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032 China
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing, 102206 China
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28
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Yang H, Li YC, Zhao MZ, Wu FL, Wang X, Xiao WD, Wang YH, Zhang JL, Wang FQ, Xu F, Zeng WF, Overall CM, He SM, Chi H, Xu P. Precision De Novo Peptide Sequencing Using Mirror Proteases of Ac-LysargiNase and Trypsin for Large-scale Proteomics. Mol Cell Proteomics 2019; 18:773-785. [PMID: 30622160 PMCID: PMC6442358 DOI: 10.1074/mcp.tir118.000918] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Revised: 11/20/2018] [Indexed: 11/06/2022] Open
Abstract
De novo peptide sequencing for large-scale proteomics remains challenging because of the lack of full coverage of ion series in tandem mass spectra. We developed a mirror protease of trypsin, acetylated LysargiNase (Ac-LysargiNase), with superior activity and stability. The mirror spectrum pairs derived from the Ac-LysargiNase and trypsin treated samples can generate full b and y ion series, which provide mutual complementarity of each other, and allow us to develop a novel algorithm, pNovoM, for de novo sequencing. Using pNovoM to sequence peptides of purified proteins, the accuracy of the sequence was close to 100%. More importantly, from a large-scale yeast proteome sample digested with trypsin and Ac-LysargiNase individually, 48% of all tandem mass spectra formed mirror spectrum pairs, 97% of which contained full coverage of ion series, resulting in precision de novo sequencing of full-length peptides by pNovoM. This enabled pNovoM to successfully sequence 21,249 peptides from 3,753 proteins and interpreted 44-152% more spectra than pNovo+ and PEAKS at a 5% FDR at the spectrum level. Moreover, the mirror protease strategy had an obvious advantage in sequencing long peptides. We believe that the combination of mirror protease strategy and pNovoM will be an effective approach for precision de novo sequencing on both single proteins and proteome samples.
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Affiliation(s)
- Hao Yang
- From the ‡Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS; University of Chinese Academy of Sciences; Institute of Computing Technology, CAS, Beijing 100190, China
| | - Yan-Chang Li
- §State Key Laboratory of Proteomics; Beijing Proteome Research Center; National Center for Protein Sciences Beijing; Beijing Institute of Lifeomics, Beijing 102206, China
| | - Ming-Zhi Zhao
- §State Key Laboratory of Proteomics; Beijing Proteome Research Center; National Center for Protein Sciences Beijing; Beijing Institute of Lifeomics, Beijing 102206, China
| | - Fei-Lin Wu
- §State Key Laboratory of Proteomics; Beijing Proteome Research Center; National Center for Protein Sciences Beijing; Beijing Institute of Lifeomics, Beijing 102206, China
| | - Xi Wang
- From the ‡Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS; University of Chinese Academy of Sciences; Institute of Computing Technology, CAS, Beijing 100190, China
| | - Wei-Di Xiao
- §State Key Laboratory of Proteomics; Beijing Proteome Research Center; National Center for Protein Sciences Beijing; Beijing Institute of Lifeomics, Beijing 102206, China
| | - Yi-Hao Wang
- §State Key Laboratory of Proteomics; Beijing Proteome Research Center; National Center for Protein Sciences Beijing; Beijing Institute of Lifeomics, Beijing 102206, China
| | - Jun-Ling Zhang
- §State Key Laboratory of Proteomics; Beijing Proteome Research Center; National Center for Protein Sciences Beijing; Beijing Institute of Lifeomics, Beijing 102206, China
| | - Fu-Qiang Wang
- §State Key Laboratory of Proteomics; Beijing Proteome Research Center; National Center for Protein Sciences Beijing; Beijing Institute of Lifeomics, Beijing 102206, China
| | - Feng Xu
- §State Key Laboratory of Proteomics; Beijing Proteome Research Center; National Center for Protein Sciences Beijing; Beijing Institute of Lifeomics, Beijing 102206, China
| | - Wen-Feng Zeng
- From the ‡Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS; University of Chinese Academy of Sciences; Institute of Computing Technology, CAS, Beijing 100190, China
| | - Christopher M Overall
- ‖Centre for Blood Research, University of British Columbia, Vancouver, British Columbia, Canada
| | - Si-Min He
- From the ‡Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS; University of Chinese Academy of Sciences; Institute of Computing Technology, CAS, Beijing 100190, China;.
| | - Hao Chi
- From the ‡Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS; University of Chinese Academy of Sciences; Institute of Computing Technology, CAS, Beijing 100190, China;.
| | - Ping Xu
- §State Key Laboratory of Proteomics; Beijing Proteome Research Center; National Center for Protein Sciences Beijing; Beijing Institute of Lifeomics, Beijing 102206, China;; ¶Key Laboratory of Combinatorial Biosynthesis and Drug Discovery of Ministry of Education Wuhan University, Wuhan University School of Pharmaceutical Sciences, Wuhan 430071, China;; College of Life Sciences, Hebei University, Baoding 071002, China.
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29
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Chen Y, Liu Y, Hou X, Ye Z, Wang C. Quantitative and Site-Specific Chemoproteomic Profiling of Targets of Acrolein. Chem Res Toxicol 2019; 32:467-473. [PMID: 30604966 DOI: 10.1021/acs.chemrestox.8b00343] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Acrolein exists in common pollutants, such as cigarette smoke and car exhaust, which has been implicated with many pathological processes. It is also one type of endogenous lipid-derived electrophile (LDE) generated from lipid peroxidation when cells are under oxidative stress. Chemically, acrolein is able to covalently modify nucleophilic residues in proteins so as to influence their structures and functions, and identification of targets of acrolein modification in proteomes is critical for understanding its biological roles. Here, we report a quantitative chemoproteomic method to globally profile acrolein modifications using an aldehyde-directed aniline-based probe. Collectively, we identified >2300 proteins and >500 cysteine sites that are targeted by acrolein. Our data provide valuable information for understanding acrolein-mediated toxicity and expanding our knowledge of oxidative stress-mediated damage and signaling.
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30
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Global phosphoproteomic analysis reveals ARMC10 as an AMPK substrate that regulates mitochondrial dynamics. Nat Commun 2019; 10:104. [PMID: 30631047 PMCID: PMC6328551 DOI: 10.1038/s41467-018-08004-0] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 12/03/2018] [Indexed: 01/06/2023] Open
Abstract
AMP-activated protein kinase (AMPK) is a key regulator of cellular energy homeostasis. Although AMPK has been studied extensively in cellular processes, understanding of its substrates and downstream functional network, and their contributions to cell fate and disease development, remains incomplete. To elucidate the AMPK-dependent signaling pathways, we performed global quantitative phosphoproteomic analysis using wild-type and AMPKα1/α2-double knockout cells and discovered 160 AMPK-dependent phosphorylation sites. Further analysis using an AMPK consensus phosphorylation motif indicated that 32 of these sites are likely direct AMPK phosphorylation sites. We validated one uncharacterized protein, ARMC10, and demonstrated that the S45 site of ARMC10 can be phosphorylated by AMPK both in vitro and in vivo. Moreover, ARMC10 overexpression was sufficient to promote mitochondrial fission, whereas ARMC10 knockout prevented AMPK-mediated mitochondrial fission. These results demonstrate that ARMC10 is an effector of AMPK that participates in dynamic regulation of mitochondrial fission and fusion. AMPK is regulates cellular energy and has been extensively studied, although our knowledge of downstream substrates is incomplete. Here, Chen et al. perform global quantitative analysis for AMPK-dependent sites and validate one hit, ARMC10, as a direct AMPK effector of mitochondrial dynamics.
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31
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Ni X, Tan Z, Ding C, Zhang C, Song L, Yang S, Liu M, Jia R, Zhao C, Song L, Liu W, Zhou Q, Gong T, Li X, Tai Y, Zhu W, Shi T, Wang Y, Xu J, Zhen B, Qin J. A region-resolved mucosa proteome of the human stomach. Nat Commun 2019; 10:39. [PMID: 30604760 PMCID: PMC6318339 DOI: 10.1038/s41467-018-07960-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Accepted: 12/06/2018] [Indexed: 12/13/2022] Open
Abstract
The human gastric mucosa is the most active layer of the stomach wall, involved in food digestion, metabolic processes and gastric carcinogenesis. Anatomically, the human stomach is divided into seven regions, but the protein basis for cellular specialization is not well understood. Here we present a global analysis of protein profiles of 82 apparently normal mucosa samples obtained from living individuals by endoscopic stomach biopsy. We identify 6,258 high-confidence proteins and estimate the ranges of protein expression in the seven stomach regions, presenting a region-resolved proteome reference map of the near normal, human stomach. Furthermore, we measure mucosa protein profiles of tumor and tumor nearby tissues (TNT) from 58 gastric cancer patients, enabling comparisons between tumor, TNT, and normal tissue. These datasets provide a rich resource for the gastrointestinal tract research community to investigate the molecular basis for region-specific functions in mucosa physiology and pathology including gastric cancer. The human stomach is divided into seven anatomically distinct regions but their protein composition is largely unknown. Here, the authors present a region-resolved map of the healthy human stomach mucosa as well as mucosa proteomes of tumor and tumor nearby tissue from gastric cancer patients.
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Affiliation(s)
- Xiaotian Ni
- Department of Gastrointestinal Oncology, The Fifth Medical Center, General Hospital of PLA, Beijing, 100071, China.,State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China.,Center for Bioinformatics, East China Normal University, Shanghai, 200241, China
| | - Zhaoli Tan
- Department of Gastrointestinal Oncology, The Fifth Medical Center, General Hospital of PLA, Beijing, 100071, China
| | - Chen Ding
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China.,State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Chunchao Zhang
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Lan Song
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China.,Department of Bioinformatics, College of Life Science, Hebei University, Baoding, 071002, China
| | - Shuai Yang
- Department of Gastrointestinal Oncology, The Fifth Medical Center, General Hospital of PLA, Beijing, 100071, China
| | - Mingwei Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China
| | - Ru Jia
- Department of Gastrointestinal Oncology, The Fifth Medical Center, General Hospital of PLA, Beijing, 100071, China
| | - Chuanhua Zhao
- Department of Gastrointestinal Oncology, The Fifth Medical Center, General Hospital of PLA, Beijing, 100071, China
| | - Lei Song
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China
| | - Wanlin Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China
| | - Quan Zhou
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China
| | - Tongqing Gong
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China
| | - Xianju Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China
| | - Yanhong Tai
- Department of Gastrointestinal Oncology, The Fifth Medical Center, General Hospital of PLA, Beijing, 100071, China
| | - Weimin Zhu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China
| | - Tieliu Shi
- Center for Bioinformatics, East China Normal University, Shanghai, 200241, China
| | - Yi Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China.,Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Jianming Xu
- Department of Gastrointestinal Oncology, The Fifth Medical Center, General Hospital of PLA, Beijing, 100071, China.
| | - Bei Zhen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China.
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China. .,State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. .,Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA.
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32
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Li X, Zhang C, Gong T, Ni X, Li J, Zhan D, Liu M, Song L, Ding C, Xu J, Zhen B, Wang Y, Qin J. A time-resolved multi-omic atlas of the developing mouse stomach. Nat Commun 2018; 9:4910. [PMID: 30464175 PMCID: PMC6249217 DOI: 10.1038/s41467-018-07463-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 10/30/2018] [Indexed: 02/07/2023] Open
Abstract
The mammalian stomach is structurally highly diverse and its organ functionality critically depends on a normal embryonic development. Although there have been several studies on the morphological changes during stomach development, a system-wide analysis of the underlying molecular changes is lacking. Here, we present a comprehensive, temporal proteome and transcriptome atlas of the mouse stomach at multiple developmental stages. Quantitative analysis of 12,108 gene products allows identifying three distinct phases based on changes in proteins and RNAs and the gain of stomach functions on a longitudinal time scale. The transcriptome indicates functionally important isoforms relevant to development and identifies several functionally unannotated novel splicing junction transcripts that we validate at the peptide level. Importantly, many proteins differentially expressed in stomach development are also significantly overexpressed in diffuse-type gastric cancer. Overall, our study provides a resource to understand stomach development and its connection to gastric cancer tumorigenesis.
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Affiliation(s)
- Xianju Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, 102206, China
| | - Chunchao Zhang
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Tongqing Gong
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, 102206, China
| | - Xiaotian Ni
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, 102206, China.,Department of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Jin'e Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, 102206, China
| | - Dongdong Zhan
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, 102206, China.,Department of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Mingwei Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, 102206, China
| | - Lei Song
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, 102206, China
| | - Chen Ding
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Jianming Xu
- Department of Gastrointestinal Oncology, Affiliated Hospital Cancer Center, Academy of Military Medical Sciences, Beijing, 100071, China
| | - Bei Zhen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, 102206, China
| | - Yi Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, 102206, China. .,Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA.
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, 102206, China. .,Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA. .,State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
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33
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Liu X, Wang W, Zhu P, Wang J, Wang Y, Wang X, Liu J, Li N, Wang X, Lin C, Liu F. In-depth quantitative proteome analysis of seminal plasma from men with oligoasthenozoospermia and normozoospermia. Reprod Biomed Online 2018; 37:467-479. [DOI: 10.1016/j.rbmo.2018.06.025] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Revised: 06/20/2018] [Accepted: 06/21/2018] [Indexed: 12/21/2022]
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34
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Wei J, Guo F, Zhang M, Xian M, Wang T, Gao J, Wu H, Song L, Zhang Y, Li D, Yang H, Huang L. Signature‐oriented investigation of the efficacy of multicomponent drugs against heart failure. FASEB J 2018; 33:2187-2198. [DOI: 10.1096/fj.201800673rr] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Junying Wei
- Institute of Chinese Materia MedicaChina Academy of Chinese Medical SciencesBeijingChina
| | - Feifei Guo
- Institute of Chinese Materia MedicaChina Academy of Chinese Medical SciencesBeijingChina
| | - Minyu Zhang
- Beijing Key Laboratory of TCM Collateral Disease Theory ResearchSchool of Traditional Chinese MedicineCapital Medical UniversityBeijingChina
| | - Minghua Xian
- Institute of Chinese Materia MedicaChina Academy of Chinese Medical SciencesBeijingChina
| | - Tingting Wang
- Institute of Chinese Materia MedicaChina Academy of Chinese Medical SciencesBeijingChina
| | - Jinhuan Gao
- Institute of Chinese Materia MedicaChina Academy of Chinese Medical SciencesBeijingChina
| | - Hongwei Wu
- Institute of Chinese Materia MedicaChina Academy of Chinese Medical SciencesBeijingChina
| | - Lei Song
- State Key Laboratory of ProteomicsBeijing Proteome Research CenterBeijing Institute of Radiation MedicineBeijingChina
| | - Yi Zhang
- Institute of Chinese Materia MedicaChina Academy of Chinese Medical SciencesBeijingChina
| | - Defeng Li
- Institute of Chinese Materia MedicaChina Academy of Chinese Medical SciencesBeijingChina
| | - Hongjun Yang
- Institute of Chinese Materia MedicaChina Academy of Chinese Medical SciencesBeijingChina
| | - Luqi Huang
- State Key Laboratory Breeding Base of Dao-di HerbsNational Resource Center for Chinese Materia MedicaChina Academy of Chinese Medical SciencesBeijingChina
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35
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Liu X, Liu G, Liu J, Zhu P, Wang J, Wang Y, Wang W, Li N, Wang X, Zhang C, Shen X, Liu F. iTRAQ-based analysis of sperm proteome from normozoospermic men achieving the rescue-ICSI pregnancy after the IVF failure. Clin Proteomics 2018; 15:27. [PMID: 30166971 PMCID: PMC6102933 DOI: 10.1186/s12014-018-9203-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Accepted: 08/14/2018] [Indexed: 12/17/2022] Open
Abstract
Background In the assisted reproduction, the infertile molecules of spermatozoa from normozoospermic men who underwent the unexplained failure of in vitro fertilization (IVF) due to the lack of sperm binding to the normal zona pellucida, and then achieved pregnancy with the rescue intracytoplasmic sperm injection (R-ICSI) remain unclear. More works are still necessary to explore this male infertile mechanism.
Methods Normozoospermicmen with the IVF pregnancy and normozoospermic men with the R-ICSI pregnancy after the conventional IVF failure were collected. iTRAQ-based proteomic approach were performed to reveal the new infertile causes between the IVF pregnancy men and the R-ICSI pregnancy men. To validate the confidence of proteome data, the individual samples were analyzed by western blot and immunofluorescence. Further, the spontaneous acrosome reactions were measured to evaluate the sperm quality. Results Compared with IVF pregnancy group, 56 sperm proteins were differentially expressed in the R-ICSI pregnancy group. Bioinformatic analyses (PANTHER, DAVID, PubMed and STRING) indicated these altered sperm proteins were involved in various molecular functions: reproduction, chromosome organization, and sperm-oocyte interaction. Moreover, the confidence of proteome data was confirmed by western blot and immunofluorescence using the individual samples, which were consistent with our proteomic data. Additionally, an increased rate of the spontaneous acrosome reaction rate was found in the R-ICSI pregnancy group. Conclusions The sealtered sperm proteins and the increased spontaneous acrosome reaction rate might account for this unexplained male infertility in the R-ICSI pregnancy patients. The present proteomic results will throw light on the better understanding of the unexplained infertile mechanisms underlying these normozoospermic man who achieved R-ICSI pregnancy after IVF failure. Electronic supplementary material The online version of this article (10.1186/s12014-018-9203-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xin Liu
- 1Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000 Shandong People's Republic of China
| | - Gensheng Liu
- Reproductive Center, Tianjin Aiwei Hospital, Tianjin, 300011 People's Republic of China
| | - Juan Liu
- 1Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000 Shandong People's Republic of China
| | - Peng Zhu
- 1Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000 Shandong People's Republic of China
| | - Jiahui Wang
- 1Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000 Shandong People's Republic of China
| | - Yanwei Wang
- 1Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000 Shandong People's Republic of China
| | - Wenting Wang
- 1Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000 Shandong People's Republic of China
| | - Ning Li
- 1Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000 Shandong People's Republic of China
| | - Xuebo Wang
- 3Department of Clinical Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000 Shandong People's Republic of China
| | - Chenglin Zhang
- 1Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000 Shandong People's Republic of China
| | - Xiaofang Shen
- 1Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000 Shandong People's Republic of China.,Reproductive Center, Beijing BaoDao Obstetrics and Gynecology Hospital, Beijing, 100000 People's Republic of China
| | - Fujun Liu
- 1Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000 Shandong People's Republic of China
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Liu W, Wei L, Sun J, Feng J, Guo G, Liang L, Fu T, Liu M, Li K, Huang Y, Zhu W, Zhen B, Wang Y, Ding C, Qin J. A reference peptide database for proteome quantification based on experimental mass spectrum response curves. Bioinformatics 2018; 34:2766-2772. [PMID: 29617941 PMCID: PMC6084618 DOI: 10.1093/bioinformatics/bty201] [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: 08/22/2017] [Accepted: 03/29/2018] [Indexed: 11/18/2022] Open
Abstract
Motivation Mass spectrometry (MS) based quantification of proteins/peptides has become a powerful tool in biological research with high sensitivity and throughput. The accuracy of quantification, however, has been problematic as not all peptides are suitable for quantification. Several methods and tools have been developed to identify peptides that response well in mass spectrometry and they are mainly based on predictive models, and rarely consider the linearity of the response curve, limiting the accuracy and applicability of the methods. An alternative solution is to select empirically superior peptides that offer satisfactory MS response intensity and linearity in a wide dynamic range of peptide concentration. Results We constructed a reference database for proteome quantification based on experimental mass spectrum response curves. The intensity and dynamic range of over 2 647 773 transitions from 121 318 peptides were obtained from a set of dilution experiments, covering 11 040 gene products. These transitions and peptides were evaluated and presented in a database named SCRIPT-MAP. We showed that the best-responder (BR) peptide approach for quantification based on SCRIPT-MAP database is robust, repeatable and accurate in proteome-scale protein quantification. This study provides a reference database as well as a peptides/transitions selection method for quantitative proteomics. Availability and implementation SCRIPT-MAP database is available at http://www.firmiana.org/responders/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Wanlin Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Lai Wei
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Jianan Sun
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Jinwen Feng
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Gaigai Guo
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Lizhu Liang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Tianyi Fu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Mingwei Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Kai Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Yin Huang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Weimin Zhu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Bei Zhen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Yi Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.,Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Chen Ding
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.,State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.,Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
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37
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Label-Free Quantitative Proteomics Combined with Biological Validation Reveals Activation of Wnt/β-Catenin Pathway Contributing to Trastuzumab Resistance in Gastric Cancer. Int J Mol Sci 2018; 19:ijms19071981. [PMID: 29986466 PMCID: PMC6073113 DOI: 10.3390/ijms19071981] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 06/30/2018] [Accepted: 07/04/2018] [Indexed: 12/21/2022] Open
Abstract
Resistance to trastuzumab, which specifically target HER2-positive breast and gastric cancer, can develop ultimately in cancer patients. However, the underlying mechanisms of resistance in gastric cancer have not been fully elucidated. Here, we established trastuzumab-resistant MKN45 and NCI N87 gastric cancer sublines from their parental cells. The resistant cells exhibited characteristics of epithelial-mesenchymal transition (EMT) and acquired higher migratory and invasive capacities. To exploit the activated pathways and develop new strategies to overcome trastuzumab resistance, we investigated MKN45 and MKN45/R cells via label-free quantitative proteomics, and found pathways that were altered significantly in MKN45/R cells, with the Wnt/β-catenin pathway being the most significant. We further confirmed the activation of this pathway by detecting its key molecules in MKN45/R and NCI N87/R cells via Western blot, in which Wnt3A, FZD6, and CTNNB1 increased, whereas GSK-3β decreased, manifesting the activation of the Wnt/β-catenin pathway. Correspondingly, inhibition of Wnt/β-catenin pathway by ICG-001, a specific Wnt/β-catenin inhibitor, preferentially reduced proliferation and invasion of trastuzumab-resistant cells and reversed EMT. Concurringly, CTNNB1 knockdown in stable cell lines potently sensitized cells to trastuzumab and induced more apoptosis. Taken together, our study demonstrates that the Wnt/β-catenin pathway mediates trastuzumab resistance, and the combination of Wnt/β-catenin inhibitors with trastuzumab may be an effective treatment option.
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Ye Z, Zhang X, Zhu Y, Song T, Chen X, Lei X, Wang C. Chemoproteomic Profiling Reveals Ethacrynic Acid Targets Adenine Nucleotide Translocases to Impair Mitochondrial Function. Mol Pharm 2018; 15:2413-2422. [PMID: 29763317 DOI: 10.1021/acs.molpharmaceut.8b00250] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Ethacrynic acid (EA) is a diuretic drug that is widely used to treat high-blood pressure and swelling caused by congestive heart failure or kidney failure. It acts through noncovalent inhibition of the Na+-K+-2Cl- cotransporter in the thick ascending limb of Henle's loop. Chemically, EA contains a Michael acceptor group that can react covalently with nucleophilic residues in proteins; however, the proteome reactivity of EA remains unexplored. Herein, we took a quantitative chemoproteomic approach to globally profile EA's targets in cancer cells. We discovered that EA induces impaired mitochondrial function accompanied by increased ROS production. Our profiling revealed that EA targets functional proteins on mitochondrial membranes, including adenine nucleotide translocases (ANTs). Site-specific mapping identified that EA covalently modifies a functional cysteine in ANTs, a mutation of which resulted in the rescuing effect on EA-induced mitochondrial dysfunction. The newly discovered modes of action offer valuable information to repurpose EA for cancer treatment.
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Affiliation(s)
- Zi Ye
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering , Peking University , Beijing 100871 , China.,Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies , Peking University , Beijing 100871 , China
| | - Xiaoyun Zhang
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering , Peking University , Beijing 100871 , China
| | - Yuangang Zhu
- Institute of Molecular Medicine , Peking University , Beijing 100871 , China
| | - Tong Song
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering , Peking University , Beijing 100871 , China
| | - Xiaowei Chen
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies , Peking University , Beijing 100871 , China.,Institute of Molecular Medicine , Peking University , Beijing 100871 , China
| | - Xiaoguang Lei
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering , Peking University , Beijing 100871 , China.,Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies , Peking University , Beijing 100871 , China
| | - Chu Wang
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering , Peking University , Beijing 100871 , China.,Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies , Peking University , Beijing 100871 , China
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van der Reest J, Lilla S, Zheng L, Zanivan S, Gottlieb E. Proteome-wide analysis of cysteine oxidation reveals metabolic sensitivity to redox stress. Nat Commun 2018; 9:1581. [PMID: 29679077 PMCID: PMC5910380 DOI: 10.1038/s41467-018-04003-3] [Citation(s) in RCA: 154] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Accepted: 03/20/2018] [Indexed: 12/19/2022] Open
Abstract
Reactive oxygen species (ROS) are increasingly recognised as important signalling molecules through oxidation of protein cysteine residues. Comprehensive identification of redox-regulated proteins and pathways is crucial to understand ROS-mediated events. Here, we present stable isotope cysteine labelling with iodoacetamide (SICyLIA), a mass spectrometry-based workflow to assess proteome-scale cysteine oxidation. SICyLIA does not require enrichment steps and achieves unbiased proteome-wide sensitivity. Applying SICyLIA to diverse cellular models and primary tissues provides detailed insights into thiol oxidation proteomes. Our results demonstrate that acute and chronic oxidative stress causes oxidation of distinct metabolic proteins, indicating that cysteine oxidation plays a key role in the metabolic adaptation to redox stress. Analysis of mouse kidneys identifies oxidation of proteins circulating in biofluids, through which cellular redox stress can affect whole-body physiology. Obtaining accurate peptide oxidation profiles from complex organs using SICyLIA holds promise for future analysis of patient-derived samples to study human pathologies.
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Affiliation(s)
- Jiska van der Reest
- Cancer Research UK Beatson Institute, Switchback Road, Glasgow, G61 1BD, United Kingdom
| | - Sergio Lilla
- Cancer Research UK Beatson Institute, Switchback Road, Glasgow, G61 1BD, United Kingdom
| | - Liang Zheng
- Cancer Research UK Beatson Institute, Switchback Road, Glasgow, G61 1BD, United Kingdom.
- Pediatric Translational Medicine Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
| | - Sara Zanivan
- Cancer Research UK Beatson Institute, Switchback Road, Glasgow, G61 1BD, United Kingdom.
- Institute of Cancer Sciences, Wolfson Wohl Cancer Research Centre, University of Glasgow, Switchback Road, Glasgow, G61 1QH, UK.
| | - Eyal Gottlieb
- Cancer Research UK Beatson Institute, Switchback Road, Glasgow, G61 1BD, United Kingdom.
- Institute of Cancer Sciences, Wolfson Wohl Cancer Research Centre, University of Glasgow, Switchback Road, Glasgow, G61 1QH, UK.
- Technion Integrated Cancer Center, Faculty of Medicine, Technion - Israel Institute of Technology, 1 Efron St. Bat Galim, Haifa, 3525433, Israel.
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40
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Wang Y, Song L, Liu M, Ge R, Zhou Q, Liu W, Li R, Qie J, Zhen B, Wang Y, He F, Qin J, Ding C. A proteomics landscape of circadian clock in mouse liver. Nat Commun 2018; 9:1553. [PMID: 29674717 PMCID: PMC5908788 DOI: 10.1038/s41467-018-03898-2] [Citation(s) in RCA: 102] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Accepted: 03/20/2018] [Indexed: 01/07/2023] Open
Abstract
As a circadian organ, liver executes diverse functions in different phase of the circadian clock. This process is believed to be driven by a transcription program. Here, we present a transcription factor (TF) DNA-binding activity-centered multi-dimensional proteomics landscape of the mouse liver, which includes DNA-binding profiles of different TFs, phosphorylation, and ubiquitylation patterns, the nuclear sub-proteome, the whole proteome as well as the transcriptome, to portray the hierarchical circadian clock network of this tissue. The TF DNA-binding activity indicates diurnal oscillation in four major pathways, namely the immune response, glucose metabolism, fatty acid metabolism, and the cell cycle. We also isolate the mouse liver Kupffer cells and measure their proteomes during the circadian cycle to reveal a cell-type resolved circadian clock. These comprehensive data sets provide a rich data resource for the understanding of mouse hepatic physiology around the circadian clock. As a circadian organ, liver functions are regulated by circadian clock. Here, the authors present a comprehensive proteomics landscape of the mouse liver, including transcription factor binding profiles, phosphorylation and ubiquitylation patterns, nuclear and whole proteome, and the transcriptome.
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Affiliation(s)
- Yunzhi Wang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Lei Song
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Mingwei Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing, 102206, China
| | - Rui Ge
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Quan Zhou
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing, 102206, China
| | - Wanlin Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing, 102206, China
| | - Ruiyang Li
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Jingbo Qie
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Bei Zhen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing, 102206, China
| | - Yi Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing, 102206, China.,Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Fuchu He
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. .,School of Life Sciences, Tsinghua University, Beijing, 100084, China. .,State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing, 102206, China.
| | - Jun Qin
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. .,State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing, 102206, China. .,Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA.
| | - Chen Ding
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
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41
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Shen X, Sun L. Systematic Evaluation of Immobilized Trypsin-Based Fast Protein Digestion for Deep and High-Throughput Bottom-Up Proteomics. Proteomics 2018; 18:e1700432. [PMID: 29577644 DOI: 10.1002/pmic.201700432] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 03/02/2018] [Indexed: 11/08/2022]
Abstract
Immobilized trypsin (IM) has been recognized as an alternative to free trypsin (FT) for accelerating protein digestion 30 years ago. However, some questions of IM still need to be answered. How does the solid matrix of IM influence its preference for protein cleavage and how well can IM perform for deep bottom-up proteomics compared to FT? By analyzing Escherichia coli proteome samples digested with amine or carboxyl functionalized magnetic bead-based IM (IM-N or IM-C) or FT, it is observed that IM-N with the nearly neutral solid matrix, IM-C with the negatively charged solid matrix, and FT have similar cleavage preference considering the microenvironment surrounding the cleavage sites. IM-N (15 min) and FT (12 h) both approach 9000 protein identifications (IDs) from a mouse brain proteome. Compared to FT, IM-N has no bias in the digestion of proteins that are involved in various biological processes, are located in different components of cells, have diverse functions, and are expressed in varying abundance. A high-throughput bottom-up proteomics workflow comprising IM-N-based rapid protein cleavage and fast CZE-MS/MS enables the completion of protein sample preparation, CZE-MS/MS analysis, and data analysis in only 3 h, resulting in 1000 protein IDs from the mouse brain proteome.
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Affiliation(s)
- Xiaojing Shen
- Department of Chemistry, Michigan State University, East Lansing, MI, USA
| | - Liangliang Sun
- Department of Chemistry, Michigan State University, East Lansing, MI, USA
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Feng J, Ding C, Qiu N, Ni X, Zhan D, Liu W, Xia X, Li P, Lu B, Zhao Q, Nie P, Song L, Zhou Q, Lai M, Guo G, Zhu W, Ren J, Shi T, Qin J. Firmiana: towards a one-stop proteomic cloud platform for data processing and analysis. Nat Biotechnol 2018; 35:409-412. [PMID: 28486446 DOI: 10.1038/nbt.3825] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Jinwen Feng
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.,The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Chen Ding
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.,State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Naiqi Qiu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.,The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Xiaotian Ni
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.,The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Dongdong Zhan
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.,The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Wanlin Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Xia Xia
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Peng Li
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Bingxin Lu
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Qi Zhao
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Peng Nie
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Lei Song
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Quan Zhou
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Mi Lai
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Gaigai Guo
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Weimin Zhu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Jian Ren
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Tieliu Shi
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.,State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Fudan University, Shanghai, China
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Chen Y, Liu Y, Lan T, Qin W, Zhu Y, Qin K, Gao J, Wang H, Hou X, Chen N, Friedmann Angeli JP, Conrad M, Wang C. Quantitative Profiling of Protein Carbonylations in Ferroptosis by an Aniline-Derived Probe. J Am Chem Soc 2018; 140:4712-4720. [PMID: 29569437 DOI: 10.1021/jacs.8b01462] [Citation(s) in RCA: 124] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Ferroptosis is a regulated form of necrotic cell death implicated in carcinogenesis and neurodegeneration that is driven by phospholipid peroxidation. Lipid-derived electrophiles (LDEs) generated during this process can covalently modify proteins ("carbonylation") and affect their functions. Here we report the development of a quantitative chemoproteomic method to profile carbonylations in ferroptosis by an aniline-derived probe. Using the method, we established a global portrait of protein carbonylations in ferroptosis with >400 endogenously modified proteins and for the first time, identified >20 residue sites with endogenous LDE modifications in ferroptotic cells. Specifically, we discovered and validated a novel cysteine site of modification on voltage-dependent anion-selective channel protein 2 (VDAC2) that might play an important role in sensitizing LDE signals and mediating ferroptosis. Our results will contribute to the understanding of ferroptotic signaling and pathogenesis and provide potential biomarkers for ferroptosis detection.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Marcus Conrad
- Institute of Developmental Genetics , Helmholtz Zentrum Munchen , Munchen , Germany
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Ge S, Xia X, Ding C, Zhen B, Zhou Q, Feng J, Yuan J, Chen R, Li Y, Ge Z, Ji J, Zhang L, Wang J, Li Z, Lai Y, Hu Y, Li Y, Li Y, Gao J, Chen L, Xu J, Zhang C, Jung SY, Choi JM, Jain A, Liu M, Song L, Liu W, Guo G, Gong T, Huang Y, Qiu Y, Huang W, Shi T, Zhu W, Wang Y, He F, Shen L, Qin J. A proteomic landscape of diffuse-type gastric cancer. Nat Commun 2018; 9:1012. [PMID: 29520031 PMCID: PMC5843664 DOI: 10.1038/s41467-018-03121-2] [Citation(s) in RCA: 160] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Accepted: 01/18/2018] [Indexed: 12/19/2022] Open
Abstract
The diffuse-type gastric cancer (DGC) is a subtype of gastric cancer with the worst prognosis and few treatment options. Here we present a dataset from 84 DGC patients, composed of a proteome of 11,340 gene products and mutation information of 274 cancer driver genes covering paired tumor and nearby tissue. DGC can be classified into three subtypes (PX1-3) based on the altered proteome alone. PX1 and PX2 exhibit dysregulation in the cell cycle and PX2 features an additional EMT process; PX3 is enriched in immune response proteins, has the worst survival, and is insensitive to chemotherapy. Data analysis revealed four major vulnerabilities in DGC that may be targeted for treatment, and allowed the nomination of potential immunotherapy targets for DGC patients, particularly for those in PX3. This dataset provides a rich resource for information and knowledge mining toward altered signaling pathways in DGC and demonstrates the benefit of proteomic analysis in cancer molecular subtyping.
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Affiliation(s)
- Sai Ge
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Xia Xia
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Chen Ding
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Bei Zhen
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Quan Zhou
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Jinwen Feng
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
- Center for Bioinformatics, East China Normal University, Shanghai, 200241, China
| | - Jiajia Yuan
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Rui Chen
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Yumei Li
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Zhongqi Ge
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Jiafu Ji
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Lianhai Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Jiayuan Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Zhongwu Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Yumei Lai
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Ying Hu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Yanyan Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Yilin Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Jing Gao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Lin Chen
- General Hospital of Chinese People's Liberation Army, Beijing, 100853, China
| | - Jianming Xu
- Affiliated Hospital of Academy of Military Medical Sciences, Beijing, 100071, China
| | - Chunchao Zhang
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Sung Yun Jung
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Jong Min Choi
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Antrix Jain
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Mingwei Liu
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Lei Song
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Wanlin Liu
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Gaigai Guo
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Tongqing Gong
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Yin Huang
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Yang Qiu
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Wenwen Huang
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Tieliu Shi
- Center for Bioinformatics, East China Normal University, Shanghai, 200241, China
| | - Weimin Zhu
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Yi Wang
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Fuchu He
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China.
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
| | - Lin Shen
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China.
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China.
| | - Jun Qin
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China.
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA.
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45
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Qi Y, Xu F, Chen L, Li Y, Xu Z, Zhang Y, Wei W, Su N, Zhang T, Fan F, Wang X, Qin X, Zhang L, Liu Y, Xu P. Quantitative proteomics reveals FLNC as a potential progression marker for the development of hepatocellular carcinoma. Oncotarget 2018; 7:68242-68252. [PMID: 27626164 PMCID: PMC5354476 DOI: 10.18632/oncotarget.11921] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2016] [Accepted: 09/02/2016] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) caused by hepatitis B virus (HBV) infection is one of the most life-threatening human cancers in China. However, the pathogenesis of HCC development is still unclear. Here, we systemically analyzed liver tissues from different stages of HCC patients through 8-plex Isobaric Tags for Relative and Absolute Quantitation (iTRAQ) approach. A total of 4,620 proteins were identified and 3,781 proteins were quantified. When T1, T2 and T3 tumor tissues were compared with T1 non-tumor cells, 330, 365 and 387 differentially expressed proteins were identified respectively. IPA (Ingenuity Pathway Analysis) analysis revealed that these differentially expressed proteins were involved in endothelial cancer, cell spreading, cell adhesion and cell movement of tumor cell lines pathway and so on. Further study showed that the filamin C (FLNC) protein was significantly overexpressed with the development of HCC, which might play an important role in HCC invasion and metastasis. These results were also confirmed with western blot (WB). The mRNA levels were significantly increased in 50 pairs of tumor and adjacent non-tumor tissues from TCGA database. The higher expression of FLNC in HCC might be a common phenomenon, thereby shedding new light on molecular mechanism and biomarker for the diagnosis purpose of HCC development.
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Affiliation(s)
- Yingzi Qi
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Institute of Radiation Medicine, Beijing 102206, China
| | - Feng Xu
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Institute of Radiation Medicine, Beijing 102206, China
| | - Lingsheng Chen
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Institute of Radiation Medicine, Beijing 102206, China.,State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning 530005, China
| | - Yanchang Li
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Institute of Radiation Medicine, Beijing 102206, China
| | - Zhongwei Xu
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Institute of Radiation Medicine, Beijing 102206, China
| | - Yao Zhang
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Institute of Radiation Medicine, Beijing 102206, China.,Institute of Microbiology, Chinese Academy of Science, Beijing 100101, China
| | - Wei Wei
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Institute of Radiation Medicine, Beijing 102206, China
| | - Na Su
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Institute of Radiation Medicine, Beijing 102206, China
| | - Tao Zhang
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Institute of Radiation Medicine, Beijing 102206, China
| | - Fengxu Fan
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Institute of Radiation Medicine, Beijing 102206, China.,Anhui Medical University, Hefei 230032, China
| | - Xing Wang
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Institute of Radiation Medicine, Beijing 102206, China
| | - Xue Qin
- Department of Clinical Laboratory, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
| | - Lingqiang Zhang
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Institute of Radiation Medicine, Beijing 102206, China
| | - Yinkun Liu
- Cancer Research Center, Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Ping Xu
- State Key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Institute of Radiation Medicine, Beijing 102206, China.,Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education and Wuhan University School of Pharmaceutical Sciences, Wuhan 430072, China.,Anhui Medical University, Hefei 230032, China
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46
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Chen D, Shen X, Sun L. Strong cation exchange-reversed phase liquid chromatography-capillary zone electrophoresis-tandem mass spectrometry platform with high peak capacity for deep bottom-up proteomics. Anal Chim Acta 2018; 1012:1-9. [PMID: 29475469 DOI: 10.1016/j.aca.2018.01.037] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2017] [Revised: 01/19/2018] [Accepted: 01/22/2018] [Indexed: 11/29/2022]
Abstract
Two-dimensional (2D) liquid chromatography (LC)-tandem mass spectrometry (MS/MS) are typically employed for deep bottom-up proteomics, and the state-of-the-art 2D-LC-MS/MS has approached over 8000 protein identifications (IDs) from mammalian cell lines or tissues in 1-3 days of mass spectrometer time. Capillary zone electrophoresis (CZE)-MS/MS has been suggested as an alternative to LC-MS/MS for bottom-up proteomics. CZE-MS/MS and LC-MS/MS are complementary in protein/peptide ID from complex proteome digests because CZE and LC are orthogonal for peptide separation. In addition, the migration time of peptides from CZE-MS can be predicted accurately, which is invaluable for evaluating the confidence of peptide ID from the database search and even guiding the database search. However, the number of protein IDs from complex proteomes using CZE-MS/MS is still much lower than the state of the art using 2D-LC-MS/MS. In this work, for the first time, we established a strong cation exchange (SCX)-reversed phase LC (RPLC)-CZE-MS/MS platform for deep bottom-up proteomics. The platform identified around 8200 protein groups and 65,000 unique peptides from a mouse brain proteome digest in 70 h. The data represents the largest bottom-up proteomics dataset using CZE-MS/MS and provides a valuable resource for further improving the tool for prediction of peptide migration time in CZE. The peak capacity of the orthogonal SCX-RPLC-CZE platform was estimated to be around 7000. SCX-RPLC-CZE-MS/MS produced comparable numbers of protein and peptide IDs with 2D-LC-MS/MS (8200 vs. 8900 protein groups, 65,000 vs. 70,000 unique peptides) from the mouse brain proteome digest using comparable instrument time. This is the first time that CZE-MS/MS showed its capability to approach comparable performance to the state-of-the-art 2D-LC-MS/MS for deep proteomic sequencing. SCX-RPLC-CZE-MS/MS and 2D-LC-MS/MS showed good complementarity in protein and peptide IDs and combining those two methods improved the number of protein group and unique peptide IDs by nearly 10% and over 40%, respectively, compared with 2D-LC-MS/MS alone.
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Affiliation(s)
- Daoyang Chen
- Department of Chemistry, Michigan State University, 578 S Shaw Ln, East Lansing, MI 48824, USA
| | - Xiaojing Shen
- Department of Chemistry, Michigan State University, 578 S Shaw Ln, East Lansing, MI 48824, USA
| | - Liangliang Sun
- Department of Chemistry, Michigan State University, 578 S Shaw Ln, East Lansing, MI 48824, USA.
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47
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Li DZ, Liu SF, Zhu L, Wang YX, Chen YX, Liu J, Hu G, Yu X, Li J, Zhang J, Wu ZX, Lu H, Liu W, Liu B. FBXW8-dependent degradation of MRFAP1 in anaphase controls mitotic cell death. Oncotarget 2017; 8:97178-97186. [PMID: 29228602 PMCID: PMC5722554 DOI: 10.18632/oncotarget.21843] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Accepted: 09/24/2017] [Indexed: 01/09/2023] Open
Abstract
Mof4 family associated protein 1 (MRFAP1) is a 14 kDa nuclear protein, which involves in maintaining normal histone modification levels by negatively regulating recruitment of the NuA4 (nucleosome acetyltransferase of H4) histone acetyltransferase complex to chromatin. MRFAP1 has been identified as one of the most up-regulated proteins after NEDD8 (neural precursor cell expressed developmentally down-regulated 8) inhibition in multiple human cell lines. However, the biological function of MRFAP1 and the E3 ligase that targets MRFAP1 for destruction remain mysterious. Here we show, by using an immunoprecipitation-based proteomics screen, that MRFAP1 is an interactor of the F-box protein FBXW8. MRFAP1 is degraded by means of the ubiquitin ligase Cul7/FBXW8 during mitotic anaphase-telophase transition and accumulated in mitotic metaphase. Overexpression of FBXW8 increased the polyubiquitination and decreased the stability of MRFAP1, whereas knockdown of FBXW8 prolonged the half-life of MRFAP1. Moreover, forced expression of MRFAP1 in HeLa cells caused growth retardation and genomic instability, leading to severe mitotic cell death. Thus, Cul7/FBXW8-mediated destruction of MRFAP1 is a regulatory component monitoring the anaphase-telophase transition and preventing genomic instability.
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Affiliation(s)
- Duan-Zhuo Li
- Hubei Key Laboratory for Kidney Disease Pathogenesis and Intervention, Huangshi Central Hospital of Edong Healthcare Group, Hubei Polytechnic University School of Medicine, Huangshi, Hubei 435003, PR China
| | - Shun-Fang Liu
- Department of Oncology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, PR China
| | - Lan Zhu
- School of Molecular Sciences and Biodesign Center for Applied Structural Discovery, Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA
| | - Yu-Xing Wang
- Hubei Key Laboratory for Kidney Disease Pathogenesis and Intervention, Huangshi Central Hospital of Edong Healthcare Group, Hubei Polytechnic University School of Medicine, Huangshi, Hubei 435003, PR China
| | - Yi-Xiang Chen
- Hubei Key Laboratory for Kidney Disease Pathogenesis and Intervention, Huangshi Central Hospital of Edong Healthcare Group, Hubei Polytechnic University School of Medicine, Huangshi, Hubei 435003, PR China
| | - Jie Liu
- Hubei Key Laboratory for Kidney Disease Pathogenesis and Intervention, Huangshi Central Hospital of Edong Healthcare Group, Hubei Polytechnic University School of Medicine, Huangshi, Hubei 435003, PR China
| | - Gang Hu
- Hubei Key Laboratory for Kidney Disease Pathogenesis and Intervention, Huangshi Central Hospital of Edong Healthcare Group, Hubei Polytechnic University School of Medicine, Huangshi, Hubei 435003, PR China
| | - Xin Yu
- Hubei Key Laboratory for Kidney Disease Pathogenesis and Intervention, Huangshi Central Hospital of Edong Healthcare Group, Hubei Polytechnic University School of Medicine, Huangshi, Hubei 435003, PR China
| | - Jian Li
- Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY 40536, USA
| | - Jin Zhang
- School of Basic Medical Sciences, Nanchang University, Nanchang, Jiangxi 330031, PR China
| | - Zhi-Xiang Wu
- Department of Pediatric Surgery, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200092, PR China
| | - Han Lu
- Department of Anesthesiology, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200025, PR China
| | - Wei Liu
- School of Molecular Sciences and Biodesign Center for Applied Structural Discovery, Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA
| | - Bin Liu
- Hubei Key Laboratory for Kidney Disease Pathogenesis and Intervention, Huangshi Central Hospital of Edong Healthcare Group, Hubei Polytechnic University School of Medicine, Huangshi, Hubei 435003, PR China
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48
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Recent progress in mass spectrometry proteomics for biomedical research. SCIENCE CHINA-LIFE SCIENCES 2017; 60:1093-1113. [DOI: 10.1007/s11427-017-9175-2] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Accepted: 09/15/2017] [Indexed: 12/30/2022]
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49
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Shen X, Liu X, Zhu P, Zhang Y, Wang J, Wang Y, Wang W, Liu J, Li N, Liu F. Proteomic analysis of human follicular fluid associated with successful in vitro fertilization. Reprod Biol Endocrinol 2017; 15:58. [PMID: 28750635 PMCID: PMC5530927 DOI: 10.1186/s12958-017-0277-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Accepted: 07/19/2017] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Human follicular fluid (HFF) provides a key environment for follicle development and oocyte maturation, and contributes to oocyte quality and in vitro fertilization (IVF) outcome. METHODS To better understand folliculogenesis in the ovary, a proteomic strategy based on dual reverse phase high performance liquid chromatography (RP-HPLC) coupled to matrix-assisted laser desorption/ionization time-of-flight tandem mass spectrometry (LC-MALDI TOF/TOF MS) was used to investigate the protein profile of HFF from women undergoing successful IVF. RESULTS A total of 219 unique high-confidence (False Discovery Rate (FDR) < 0.01) HFF proteins were identified by searching the reviewed Swiss-Prot human database (20,183 sequences), and MS data were further verified by western blot. PANTHER showed HFF proteins were involved in complement and coagulation cascade, growth factor and hormone, immunity, and transportation, KEGG indicated their pathway, and STRING demonstrated their interaction networks. In comparison, 32% and 50% of proteins have not been reported in previous human follicular fluid and plasma. CONCLUSIONS Our HFF proteome research provided a new complementary high-confidence dataset of folliculogenesis and oocyte maturation environment. Those proteins associated with innate immunity, complement cascade, blood coagulation, and angiogenesis might serve as the biomarkers of female infertility and IVF outcome, and their pathways facilitated a complete exhibition of reproductive process.
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Affiliation(s)
- Xiaofang Shen
- grid.440323.2Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000 Shandong People’s Republic of China
- Reproductive center, Beijing BaoDao obstetrics and gynecology hospital, Beijing, 100000 People’s Republic of China
| | - Xin Liu
- grid.440323.2Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000 Shandong People’s Republic of China
| | - Peng Zhu
- grid.440323.2Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000 Shandong People’s Republic of China
| | - Yuhua Zhang
- Reproductive Medicine Centre, Wei fang people’s hospital, Weifang, Shandong 261000 People’s Republic of China
| | - Jiahui Wang
- grid.440323.2Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000 Shandong People’s Republic of China
| | - Yanwei Wang
- grid.440323.2Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000 Shandong People’s Republic of China
| | - Wenting Wang
- grid.440323.2Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000 Shandong People’s Republic of China
| | - Juan Liu
- grid.440323.2Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000 Shandong People’s Republic of China
| | - Ning Li
- grid.440323.2Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000 Shandong People’s Republic of China
| | - Fujun Liu
- grid.440323.2Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000 Shandong People’s Republic of China
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50
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Liu W, Chang J, Liu M, Yuan J, Zhang J, Qin J, Xia X, Wang Y. Quantitative proteomics profiling reveals activation of mTOR pathway in trastuzumab resistance. Oncotarget 2017; 8:45793-45806. [PMID: 28507275 PMCID: PMC5542228 DOI: 10.18632/oncotarget.17415] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Accepted: 04/12/2017] [Indexed: 12/18/2022] Open
Abstract
Trastuzumab is an antibody-based therapy drug targeting HER2-overexpressing tumors. While it has been proven to be very successful initially, most patients eventually develop resistance to trastuzumab. The mechanism of drug resistance is not well understood. Identifying pathways that mediate trastuzumab resistance will improve our understanding of the underlying mechanism and is crucial for the development of therapeutic strategies to overcome resistance.Here we report a quantitative proteomics profiling of a trastuzumab-sensitive (T-S) gastric cancer cell line NCI N87 and a trastuzumab-resistant NCI N87 (T-R) subline generated by low-dose, continuous trastuzumab treatment. By identifying proteins differentially expressed in these two cell lines, we show that multiple pathways including mTOR, Wnt, DNA damage response and metabolic pathways are significantly altered. We further confirm by western blotting that protein levels of multiple components of the mTOR pathway, including mTOR, AKT and RPS6KB1, are increased, whereas AKT1S1 is decreased, suggesting the activation of mTOR pathway. Importantly, treatment of AZD8055, an mTOR inhibitor, leads to the decreased phosphorylation levels of mTOR downstream molecules RPS6KB1 at Thr421/Ser424 and AKT at Ser473. Furthermore, AZD8055 also preferentially reduces viability, and inhibits migration and invasion abilities of the T-R cells. Together, our findings indicate that mTOR pathway is among multiple signaling pathways that mediate trastuzumab resistance in NCI N87 T-R cells, and that mTOR inhibitors may be used to treat trastuzumab resistant, HER2-positive gastric cancer tumors.
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Affiliation(s)
- Wenhu Liu
- School of Pharmaceutical Sciences and Innovative Drug Research Center, Chongqing University, Chongqing 401331, China
- State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Proteome Research Center, Beijing 102206, China
- School of Pharmacy, North Sichuan Medical College, Nanchong 637007, China
| | - Jinxia Chang
- School of Basic Medical Sciences, North Sichuan Medical College, Nanchong 637007, China
| | - Mingwei Liu
- State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Proteome Research Center, Beijing 102206, China
| | - Jiangbei Yuan
- School of Pharmaceutical Sciences and Innovative Drug Research Center, Chongqing University, Chongqing 401331, China
| | - Jinqiang Zhang
- School of Pharmaceutical Sciences and Innovative Drug Research Center, Chongqing University, Chongqing 401331, China
| | - Jun Qin
- State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Proteome Research Center, Beijing 102206, China
- Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Xuefeng Xia
- School of Pharmaceutical Sciences and Innovative Drug Research Center, Chongqing University, Chongqing 401331, China
| | - Yi Wang
- State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Proteome Research Center, Beijing 102206, China
- Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
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