1
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Shin D, Kim Y, Park J, Kim Y. High-throughput Proteomics-Guided Biomarker Discovery of Hepatocellular Carcinoma. Biomed J 2024:100752. [PMID: 38901798 DOI: 10.1016/j.bj.2024.100752] [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: 01/28/2024] [Revised: 06/07/2024] [Accepted: 06/12/2024] [Indexed: 06/22/2024] Open
Abstract
Liver cancer stands as the fifth leading cause of cancer-related deaths globally. Hepatocellular carcinoma (HCC) comprises approximately 85%-90% of all primary liver malignancies. However, only 20-30% of HCC patients qualify for curative therapy, primarily due to the absence of reliable tools for early detection and prognosis of HCC. This underscores the critical need for molecular biomarkers for HCC management. Since proteins reflect disease status directly, proteomics has been utilized in biomarker developments for HCC. In particular, proteomics coupled with liquid chromatography-mass spectrometer (LC-MS) methods facilitate the process of discovering biomarker candidates for diagnosis, prognosis, and therapeutic strategies. In this work, we investigated LC-MS-based proteomics methods through recent reference reviews, with a particular focus on sample preparation and LC-MS methods appropriate for the discovery of HCC biomarkers and their clinical applications. We classified proteomics studies of HCC according to sample types, and we examined the coverage of protein biomarker candidates based on LC-MS methods in relation to study scales and goals. Comprehensively, we proposed protein biomarker candidates categorized by sample types and biomarker types for appropriate clinical use. In this review, we summarized recent LC-MS-based proteomics studies on HCC and proposed potential protein biomarkers. Our findings are expected to expand the understanding of HCC pathogenesis and enhance the efficiency of HCC diagnosis and prognosis, thereby contributing to improved patient outcomes.
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Affiliation(s)
- Dongyoon Shin
- Proteomics Research Team, CHA Institute of Future Medicine, Seongnam, Republic of Korea
| | - Yeongshin Kim
- Proteomics Research Team, CHA Institute of Future Medicine, Seongnam, Republic of Korea; Department of Medical Science, School of Medicine, CHA University, Seongnam, Republic of Korea
| | - Junho Park
- Proteomics Research Team, CHA Institute of Future Medicine, Seongnam, Republic of Korea; Department of Pharmacology, School of Medicine, CHA University, Seongnam, Republic of Korea.
| | - Youngsoo Kim
- Proteomics Research Team, CHA Institute of Future Medicine, Seongnam, Republic of Korea; Department of Medical Science, School of Medicine, CHA University, Seongnam, Republic of Korea.
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2
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Wu W, Huang Z, Kong W, Peng H, Goh WWB. Optimizing the PROTREC network-based missing protein prediction algorithm. Proteomics 2024; 24:e2200332. [PMID: 37876146 DOI: 10.1002/pmic.202200332] [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: 08/28/2022] [Revised: 09/30/2023] [Accepted: 10/06/2023] [Indexed: 10/26/2023]
Abstract
This article summarizes the PROTREC method and investigates the impact that the different hyper-parameters have on the task of missing protein prediction using PROTREC. We evaluate missing protein recovery rates using different PROTREC score selection approaches (MAX, MIN, MEDIAN, and MEAN), different PROTREC score thresholds, as well as different complex size thresholds. In addition, we included two additional cancer datasets in our analysis and introduced a new validation method to check both the robustness of the PROTREC method as well as the correctness of our analysis. Our analysis showed that the missing protein recovery rate can be improved by adopting PROTREC score selection operations of MIN, MEDIAN, and MEAN instead of the default MAX. However, this may come at a cost of reduced numbers of proteins predicted and validated. The users should therefore choose their hyper-parameters carefully to find a balance in the accuracy-quantity trade-off. We also explored the possibility of combining PROTREC with a p-value-based method (FCS) and demonstrated that PROTREC is able to perform well independently without any help from a p-value-based method. Furthermore, we conducted a downstream enrichment analysis to understand the biological pathways and protein networks within the cancerous tissues using the recovered proteins. Missing protein recovery rate using PROTREC can be improved by selecting a different PROTREC score selection method. Different PROTREC score selection methods and other hyper-parameters such as PROTREC score threshold and complex size threshold introduce accuracy-quantity trade-off. PROTREC is able to perform well independently of any filtering using a p-value-based method. Verification of the PROTREC method on additional cancer datasets. Downstream Enrichment Analysis to understand the biological pathways and protein networks in cancerous tissues.
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Affiliation(s)
- Wenshan Wu
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Zelu Huang
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore, Singapore
| | - Weijia Kong
- Department of Computer Science, National University of Singapore, Singapore, Singapore
- School of Biological Science, Nanyang Technological University, Singapore, Singapore
| | - Hui Peng
- School of Biological Science, Nanyang Technological University, Singapore, Singapore
| | - Wilson Wen Bin Goh
- School of Biological Science, Nanyang Technological University, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Center for Biomedical Informatics, Nanyang Technological University, Singapore, Singapore
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3
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Kitata RB, Yang JC, Chen YJ. Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape. MASS SPECTROMETRY REVIEWS 2023; 42:2324-2348. [PMID: 35645145 DOI: 10.1002/mas.21781] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 12/17/2021] [Accepted: 01/21/2022] [Indexed: 06/15/2023]
Abstract
The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.
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Affiliation(s)
| | - Jhih-Ci Yang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
- Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica and National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
- Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica and National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Chemistry, National Taiwan University, Taipei, Taiwan
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4
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Huang T, Staniak M, da Veiga Leprevost F, Figueroa-Navedo AM, Ivanov AR, Nesvizhskii AI, Choi M, Vitek O. Statistical Detection of Differentially Abundant Proteins in Experiments with Repeated Measures Designs and Isobaric Labeling. J Proteome Res 2023; 22:2641-2659. [PMID: 37467362 PMCID: PMC11090052 DOI: 10.1021/acs.jproteome.3c00155] [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] [Indexed: 07/21/2023]
Abstract
Repeated measures experimental designs, which quantify proteins in biological subjects repeatedly over multiple experimental conditions or times, are commonly used in mass spectrometry-based proteomics. Such designs distinguish the biological variation within and between the subjects and increase the statistical power of detecting within-subject changes in protein abundance. Meanwhile, proteomics experiments increasingly incorporate tandem mass tag (TMT) labeling, a multiplexing strategy that gains both relative protein quantification accuracy and sample throughput. However, combining repeated measures and TMT multiplexing in a large-scale investigation presents statistical challenges due to unique interplays of between-mixture, within-mixture, between-subject, and within-subject variation. This manuscript proposes a family of linear mixed-effects models for differential analysis of proteomics experiments with repeated measures and TMT multiplexing. These models decompose the variation in the data into the contributions from its sources as appropriate for the specifics of each experiment, enable statistical inference of differential protein abundance, and recognize a difference in the uncertainty of between-subject versus within-subject comparisons. The proposed family of models is implemented in the R/Bioconductor package MSstatsTMT v2.2.0. Evaluations of four simulated datasets and four investigations answering diverse biological questions demonstrated the value of this approach as compared to the existing general-purpose approaches and implementations.
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Affiliation(s)
- Ting Huang
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Mateusz Staniak
- Institute of Mathematics, University of Wrocław, Wrocław, Poland
| | | | - Amanda M. Figueroa-Navedo
- Department of Chemistry and Chemical Biology, Barnett Institute of Biological and Chemical Analysis, Northeastern University, Boston, MA, USA
| | - Alexander R. Ivanov
- Department of Chemistry and Chemical Biology, Barnett Institute of Biological and Chemical Analysis, Northeastern University, Boston, MA, USA
| | | | - Meena Choi
- Departments of Microchemistry, Proteomics & Lipidomics, Genentech, South San Francisco, CA, USA
| | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
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5
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Sun R, Ge W, Zhu Y, Sayad A, Luna A, Lyu M, Liang S, Tobalina L, Rajapakse VN, Yu C, Zhang H, Fang J, Wu F, Xie H, Saez-Rodriguez J, Ying H, Reinhold WC, Sander C, Pommier Y, Neel BG, Aebersold R, Guo T. Proteomic Dynamics of Breast Cancer Cell Lines Identifies Potential Therapeutic Protein Targets. Mol Cell Proteomics 2023; 22:100602. [PMID: 37343696 PMCID: PMC10392136 DOI: 10.1016/j.mcpro.2023.100602] [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: 08/24/2022] [Revised: 04/18/2023] [Accepted: 06/12/2023] [Indexed: 06/23/2023] Open
Abstract
Treatment and relevant targets for breast cancer (BC) remain limited, especially for triple-negative BC (TNBC). We identified 6091 proteins of 76 human BC cell lines using data-independent acquisition (DIA). Integrating our proteomic findings with prior multi-omics datasets, we found that including proteomics data improved drug sensitivity predictions and provided insights into the mechanisms of action. We subsequently profiled the proteomic changes in nine cell lines (five TNBC and four non-TNBC) treated with EGFR/AKT/mTOR inhibitors. In TNBC, metabolism pathways were dysregulated after EGFR/mTOR inhibitor treatment, while RNA modification and cell cycle pathways were affected by AKT inhibitor. This systematic multi-omics and in-depth analysis of the proteome of BC cells can help prioritize potential therapeutic targets and provide insights into adaptive resistance in TNBC.
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Affiliation(s)
- Rui Sun
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Weigang Ge
- Bioinformatics Department, Westlake Omics (Hangzhou) Biotechnology Co, Ltd, Hangzhou, Zhejiang, China
| | - Yi Zhu
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China; Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Azin Sayad
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Laura and Isaac Perlmutter Cancer Center, New York University Langone Medical Center, New York, New York, USA
| | - Augustin Luna
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA; Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA
| | - Mengge Lyu
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Shuang Liang
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Luis Tobalina
- Bioinformatics and Data Science, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Vinodh N Rajapakse
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Chenhuan Yu
- Key Laboratory of Experimental Animal and Safety Evaluation, Zhejiang Academy of Medical Sciences, Hangzhou, Zhejiang, China
| | - Huanhuan Zhang
- Key Laboratory of Experimental Animal and Safety Evaluation, Zhejiang Academy of Medical Sciences, Hangzhou, Zhejiang, China
| | - Jie Fang
- Key Laboratory of Experimental Animal and Safety Evaluation, Zhejiang Academy of Medical Sciences, Hangzhou, Zhejiang, China
| | - Fang Wu
- Key Laboratory of Experimental Animal and Safety Evaluation, Zhejiang Academy of Medical Sciences, Hangzhou, Zhejiang, China
| | - Hui Xie
- Key Laboratory of Experimental Animal and Safety Evaluation, Zhejiang Academy of Medical Sciences, Hangzhou, Zhejiang, China
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Institute for Computational Biomedicine, Heidelberg University Hospital, BioQuant, Heidelberg University, Heidelberg, Baden-Württemberg, Germany
| | - Huazhong Ying
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA
| | - William C Reinhold
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Chris Sander
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA; Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA
| | - Yves Pommier
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Benjamin G Neel
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Laura and Isaac Perlmutter Cancer Center, New York University Langone Medical Center, New York, New York, USA.
| | - Ruedi Aebersold
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland; Faculty of Science, University of Zurich, Zurich, Switzerland.
| | - Tiannan Guo
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China; Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
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6
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Yi X, Zhu J, Liu W, Peng L, Lu C, Sun P, Huang L, Nie X, Huang S, Guo T, Zhu Y. Proteome Landscapes of Human Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma. Mol Cell Proteomics 2023; 22:100604. [PMID: 37353004 PMCID: PMC10413158 DOI: 10.1016/j.mcpro.2023.100604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 04/12/2023] [Accepted: 06/20/2023] [Indexed: 06/25/2023] Open
Abstract
Liver cancer is among the top leading causes of cancer mortality worldwide. Particularly, hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (CCA) have been extensively investigated from the aspect of tumor biology. However, a comprehensive and systematic understanding of the molecular characteristics of HCC and CCA remains absent. Here, we characterized the proteome landscapes of HCC and CCA using the data-independent acquisition (DIA) mass spectrometry (MS) method. By comparing the quantitative proteomes of HCC and CCA, we found several differences between the two cancer types. In particular, we found an abnormal lipid metabolism in HCC and activated extracellular matrix-related pathways in CCA. We next developed a three-protein classifier to distinguish CCA from HCC, achieving an area under the curve (AUC) of 0.92, and an accuracy of 90% in an independent validation cohort of 51 patients. The distinct molecular characteristics of HCC and CCA presented in this study provide new insights into the tumor biology of these two major important primary liver cancers. Our findings may help develop more efficient diagnostic approaches and new targeted drug treatments.
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Affiliation(s)
- Xiao Yi
- Center for ProtTalks, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Jiang Zhu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Key laboratory of Biological Targeted Therapy, The Ministry of Education, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wei Liu
- Westlake Omics (Hangzhou) Biotechnology Co, Ltd, Hangzhou, Zhejiang, China
| | - Li Peng
- Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Cong Lu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Key laboratory of Biological Targeted Therapy, The Ministry of Education, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ping Sun
- Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Lingling Huang
- Westlake Omics (Hangzhou) Biotechnology Co, Ltd, Hangzhou, Zhejiang, China
| | - Xiu Nie
- Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shi'ang Huang
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Key laboratory of Biological Targeted Therapy, The Ministry of Education, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Tiannan Guo
- Center for ProtTalks, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China.
| | - Yi Zhu
- Center for ProtTalks, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China.
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7
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Immunopeptidome of hepatocytes isolated from patients with HBV infection and hepatocellular carcinoma. JHEP Rep 2022; 4:100576. [PMID: 36185575 PMCID: PMC9523389 DOI: 10.1016/j.jhepr.2022.100576] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 07/28/2022] [Accepted: 08/16/2022] [Indexed: 01/01/2023] Open
Abstract
Background & Aims Antigen-specific immunotherapy is a promising strategy to treat HBV infection and hepatocellular carcinoma (HCC). To facilitate killing of malignant and/or infected hepatocytes, it is vital to know which T cell targets are presented by human leucocyte antigen (HLA)-I complexes on patient-derived hepatocytes. Here, we aimed to reveal the hepatocyte-specific HLA-I peptidome with emphasis on peptides derived from HBV proteins and tumour-associated antigens (TAA) to guide development of antigen-specific immunotherapy. Methods Primary human hepatocytes were isolated with high purity from (HBV-infected) non-tumour and HCC tissues using a newly designed perfusion-free procedure. Hepatocyte-derived HLA-bound peptides were identified by unbiased mass spectrometry (MS), after which source proteins were subjected to Gene Ontology and pathway analysis. HBV antigen and TAA-derived HLA peptides were searched for using targeted MS, and a selection of peptides was tested for immunogenicity. Results Using unbiased data-dependent acquisition (DDA), we acquired a high-quality HLA-I peptidome of 2 × 105 peptides that contained 8 HBV-derived peptides and 14 peptides from 8 known HCC-associated TAA that were exclusive to tumours. Of these, 3 HBV- and 12 TAA-derived HLA peptides were detected by targeted MS in the sample they were originally identified in by DDA. Moreover, 2 HBV- and 2 TAA-derived HLA peptides were detected in samples in which no identification was made using unbiased MS. Finally, immunogenicity was demonstrated for 5 HBV-derived and 3 TAA-derived peptides. Conclusions We present a first HLA-I immunopeptidome of isolated primary human hepatocytes, devoid of immune cells. Identified HBV-derived and TAA-derived peptides directly aid development of antigen-specific immunotherapy for chronic HBV infection and HCC. The described methodology can also be applied to personalise immunotherapeutic treatment of liver diseases in general. Lay summary Immunotherapy that aims to induce immune responses against a virus or tumour is a promising novel treatment option to treat chronic HBV infection and liver cancer. For the design of successful therapy, it is essential to know which fragments (i.e. peptides) of virus-derived and tumour-specific proteins are presented to the T cells of the immune system by diseased liver cells and are thus good targets for immunotherapy. Here, we have isolated liver cells from patients who have chronic HBV infection and/or liver cancer, analysed what peptides are presented by these cells, and assessed which peptides are able to drive immune responses. We developed a perfusion-free method to isolate primary hepatocytes that are depleted of immune cells. We derived a large-scale unbiased hepatocyte HLA ligandome from patients with HBV and/or HCC. The ligandome included peptides derived from HBV proteins and tumour-associated antigens (TAA). Using a targeted MS regime, the detection sensitivity of several HBV and TAA-derived peptides could be increased. Immunogenicity was demonstrated for a selection of TAA- and HBV-derived HLA peptides.
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Key Words
- Antigen presentation
- Cancer germline antigen
- Cancer testis antigen
- DDA, data-dependent acquisition
- GO, Gene Ontology
- HBV, Hepatitis B virus
- HCC, hepatocellular carcinoma
- HLA
- HLA, human leucocyte antigen
- IEDB, Immune Epitope Database
- IFNγ, interferon γ
- IP, immunoprecipitation
- KEGG, Kyoto Encyclopedia of Genes and Genomes
- LSEC, liver sinusoidal cell
- Liver cancer
- MHC
- MS, mass spectrometry
- PBMCs, peripheral blood mononuclear cells
- PRM, parallel reaction monitoring
- Peptidome
- Pol, polymerase
- T cell epitope
- TAA, tumour-associated antigen
- Viral hepatitis
- cHBV, chronic HBV
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8
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Boys EL, Liu J, Robinson PJ, Reddel RR. Clinical applications of mass spectrometry-based proteomics in cancer: where are we? Proteomics 2022; 23:e2200238. [PMID: 35968695 DOI: 10.1002/pmic.202200238] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/07/2022] [Accepted: 08/09/2022] [Indexed: 11/12/2022]
Abstract
Tumor tissue processing methodologies in combination with data-independent acquisition mass spectrometry (DIA-MS) have emerged that can comprehensively analyze the proteome of multiple tumor samples accurately and reproducibly. Increasing recognition and adoption of these technologies has resulted in a tranche of studies providing novel insights into cancer classification systems, functional tumor biology, cancer biomarkers, treatment response and drug targets. Despite this, with some limited exceptions, MS-based proteomics has not yet been implemented in routine cancer clinical practice. Here, we summarize the use of DIA-MS in studies that may pave the way for future clinical cancer applications, and highlight the role of alternative MS technologies and multi-omic strategies. We discuss limitations and challenges of studies in this field to date and propose steps for integrating proteomic data into the cancer clinic. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Emma L Boys
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Jia Liu
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia.,The Kinghorn Cancer Centre, St Vincent's Hospital, Darlinghurst, NSW, Australia.,School of Clinical Medicine, St Vincent's Campus, University of New South Wales, Sydney, NSW, Australia
| | - Phillip J Robinson
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Roger R Reddel
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
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9
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High-throughput proteomic sample preparation using pressure cycling technology. Nat Protoc 2022; 17:2307-2325. [PMID: 35931778 PMCID: PMC9362583 DOI: 10.1038/s41596-022-00727-1] [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: 12/28/2021] [Accepted: 05/24/2022] [Indexed: 11/09/2022]
Abstract
High-throughput lysis and proteolytic digestion of biopsy-level tissue specimens is a major bottleneck for clinical proteomics. Here we describe a detailed protocol of pressure cycling technology (PCT)-assisted sample preparation for proteomic analysis of biopsy tissues. A piece of fresh frozen or formalin-fixed paraffin-embedded tissue weighing ~0.1–2 mg is placed in a 150 μL pressure-resistant tube called a PCT-MicroTube with proper lysis buffer. After closing with a PCT-MicroPestle, a batch of 16 PCT-MicroTubes are placed in a Barocycler, which imposes oscillating pressure to the samples from one atmosphere to up to ~3,000 times atmospheric pressure. The pressure cycling schemes are optimized for tissue lysis and protein digestion, and can be programmed in the Barocycler to allow reproducible, robust and efficient protein extraction and proteolysis digestion for mass spectrometry-based proteomics. This method allows effective preparation of not only fresh frozen and formalin-fixed paraffin-embedded tissue, but also cells, feces and tear strips. It takes ~3 h to process 16 samples in one batch. The resulting peptides can be analyzed by various mass spectrometry-based proteomics methods. We demonstrate the applications of this protocol with mouse kidney tissue and eight types of human tumors. High-throughput lysis and proteolytic digestion of biopsy-level tissue specimens is a major bottleneck for clinical proteomics. This protocol describes pressure cycling technology (PCT)-assisted sample preparation of biopsy tissues.
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10
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Walzer M, García-Seisdedos D, Prakash A, Brack P, Crowther P, Graham RL, George N, Mohammed S, Moreno P, Papatheodorou I, Hubbard SJ, Vizcaíno JA. Implementing the reuse of public DIA proteomics datasets: from the PRIDE database to Expression Atlas. Sci Data 2022; 9:335. [PMID: 35701420 PMCID: PMC9197839 DOI: 10.1038/s41597-022-01380-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 05/12/2022] [Indexed: 11/14/2022] Open
Abstract
The number of mass spectrometry (MS)-based proteomics datasets in the public domain keeps increasing, particularly those generated by Data Independent Acquisition (DIA) approaches such as SWATH-MS. Unlike Data Dependent Acquisition datasets, the re-use of DIA datasets has been rather limited to date, despite its high potential, due to the technical challenges involved. We introduce a (re-)analysis pipeline for public SWATH-MS datasets which includes a combination of metadata annotation protocols, automated workflows for MS data analysis, statistical analysis, and the integration of the results into the Expression Atlas resource. Automation is orchestrated with Nextflow, using containerised open analysis software tools, rendering the pipeline readily available and reproducible. To demonstrate its utility, we reanalysed 10 public DIA datasets from the PRIDE database, comprising 1,278 SWATH-MS runs. The robustness of the analysis was evaluated, and the results compared to those obtained in the original publications. The final expression values were integrated into Expression Atlas, making SWATH-MS experiments more widely available and combining them with expression data originating from other proteomics and transcriptomics datasets.
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Affiliation(s)
- Mathias Walzer
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom.
| | - David García-Seisdedos
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Ananth Prakash
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Paul Brack
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Oxford Road, Manchester, M13 9PT, United Kingdom
| | - Peter Crowther
- Melandra Limited, 16 Brook Road, Urmston, Manchester, M41 5RY, United Kingdom
| | - Robert L Graham
- School of Biological Sciences, Chlorine Gardens, Queen's University Belfast, Belfast, BT9 5DL, United Kingdom
| | - Nancy George
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Suhaib Mohammed
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Pablo Moreno
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Irene Papatheodorou
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Simon J Hubbard
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Oxford Road, Manchester, M13 9PT, United Kingdom
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom.
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11
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Gao H, Liu Y, Demichev V, Tate S, Chen C, Zhu J, Lu C, Ralser M, Guo T, Zhu Y. Optimization of Microflow LC Coupled with Scanning SWATH and Its Application in Hepatocellular Carcinoma Tissues. J Proteome Res 2022; 21:1686-1693. [PMID: 35653712 DOI: 10.1021/acs.jproteome.2c00078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Scanning SWATH coupled with normal-flow LC has been recently introduced for high-content, high-throughput proteomics analysis, which requires a relatively large amount of sample injection. Here we established the microflow LC coupled with Scanning SWATH for samples with relatively small quantities. First, we optimized several key parameters of the LC and MS settings, including C18 particle size for the analytical column, LC gradient and flow rate, as well as effective ion accumulation time and isolation window width for MS acquisition. We then compared the optimized Scanning SWATH method with the conventional variable window SWATH (referred to as SWATH) method. Results showed that the total ion chromatogram signals in Scanning SWATH were 10 times higher than that of SWATH, and Scanning SWATH identified 12.2-22.2% more peptides than SWATH. Finally, we employed 120 min Scanning SWATH to acquire the proteomes of 62 formalin-fixed, paraffin-embedded (FFPE) tissue samples from 31 patients with hepatocellular carcinoma (HCC). Altogether, 92 334 peptides and 8516 proteins were quantified. Besides the reported biomarkers, including ANXA2, MCM7, SUOX, and AKR1B10, we identified new potential HCC biomarkers such as CST5, TP53, CEBPB, and E2F4. Taken together, we present an optimal workflow integrating microflow LC and Scanning SWATH that effectively improves the protein identification and quantitation.
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Affiliation(s)
- Huanhuan Gao
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, Zhejiang Province, China
| | - Youqi Liu
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., No. 1 Yunmeng Road, Cloud Town, Xihu District, Hangzhou 310024, Zhejiang Province, China
| | - Vadim Demichev
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London WC2N 5DU, U.K.,Department of Biochemistry, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin 10115, Germany
| | | | | | - Jiang Zhu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, Hubei, China
| | - Cong Lu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, Hubei, China
| | - Markus Ralser
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London WC2N 5DU, U.K.,Department of Biochemistry, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin 10115, Germany
| | - Tiannan Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, Zhejiang Province, China
| | - Yi Zhu
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, Zhejiang Province, China
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12
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Li H, Tang D, Chen J, Hu Y, Cai X, Zhang P. The Clinical Value of GDF15 and Its Prospective Mechanism in Sepsis. Front Immunol 2021; 12:710977. [PMID: 34566964 PMCID: PMC8456026 DOI: 10.3389/fimmu.2021.710977] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 08/17/2021] [Indexed: 12/23/2022] Open
Abstract
Growth differentiation factor 15 (GDF15) is involved in the occurrence and development of many diseases, and there are few studies on its relationship with sepsis. This article aims to explore the clinical value of GDF15 in sepsis and to preliminarily explore its prospective regulatory effect on macrophage inflammation and its functions. We recruited 320 subjects (132 cases in sepsis group, 93 cases in nonsepsis group, and 95 cases in control group), then detected the serum GDF15 levels and laboratory indicators, and further investigated the correlation between GDF15 and laboratory indicators, and also analyzed the clinical value of GDF15 in sepsis diagnosis, severity assessment, and prognosis. In vitro, we used LPS to stimulate THP-1 and RAW264.7 cells to establish the inflammatory model, and detected the expression of GDF15 in the culture medium and cells under the inflammatory state. After that, we added GDF15 recombinant protein (rGDF15) pretreatment to explore its prospective regulatory effect on macrophage inflammation and its functions. The results showed that the serum GDF15 levels were significantly increased in the sepsis group, which was correlated with laboratory indexes of organ damage, coagulation indexes, inflammatory factors, and SOFA score. GDF15 also has a high AUC in the diagnosis of sepsis, which can be further improved by combining with other indicators. The dynamic monitoring of GDF15 levels can play an important role in the judgment and prognosis of sepsis. In the inflammatory state, the expression of intracellular and extracellular GDF15 increased. GDF15 can reduce the levels of cytokines, inhibit M1 polarization induced by LPS, and promote M2 polarization. Moreover, GDF15 also enhances the phagocytosis and bactericidal function of macrophages. Finally, we observed a decreased level of the phosphorylation of JAK1/STAT3 signaling pathway and the nuclear translocation of NF-κB p65 with the pretreatment of rGDF15. In summary, our study found that GDF15 has good clinical application value in sepsis and plays a protective role in the development of sepsis by regulating the functions of macrophages and inhibiting the activation of JAK1/STAT3 pathway and nuclear translocation of NF-κB p65.
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Affiliation(s)
- Huan Li
- Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, China
| | - Dongling Tang
- Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, China
| | - Juanjuan Chen
- Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yuanhui Hu
- Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xin Cai
- Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, China
| | - Pingan Zhang
- Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, China
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13
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Plasma Proteomic Analysis in Morquio A Disease. Int J Mol Sci 2021; 22:ijms22116165. [PMID: 34200496 PMCID: PMC8201332 DOI: 10.3390/ijms22116165] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 06/02/2021] [Accepted: 06/04/2021] [Indexed: 12/18/2022] Open
Abstract
Mucopolysaccharidosis type IVA (MPS IVA) is a lysosomal disease caused by mutations in the gene encoding the enzymeN-acetylgalactosamine-6-sulfate sulfatase (GALNS), and is characterized by systemic skeletal dysplasia due to excessive storage of keratan sulfate (KS) and chondroitin-6-sulfate in chondrocytes. Although improvements in the activity of daily living and endurance tests have been achieved with enzyme replacement therapy (ERT) with recombinant human GALNS, recovery of bone lesions and bone growth in MPS IVA has not been demonstrated to date. Moreover, no correlation has been described between therapeutic efficacy and urine levels of KS, which accumulates in MPS IVA patients. The objective of this study was to assess the validity of potential biomarkers proposed by other authors and to identify new biomarkers. To identify candidate biomarkers of this disease, we analyzed plasma samples from healthy controls (n=6) and from untreated (n=8) and ERT-treated (n=5, sampled before and after treatment) MPS IVA patients using both qualitative and quantitative proteomics analyses. The qualitative proteomics approach analyzed the proteomic profile of the different study groups. In the quantitative analysis, we identified/quantified 215 proteins after comparing healthy control untreated, ERT-treated MPSIVA patients. We selected a group of proteins that were dysregulated in MPS IVA patients. We identified four potential protein biomarkers, all of which may influence bone and cartilage metabolism: fetuin-A, vitronectin, alpha-1antitrypsin, and clusterin. Further studies of cartilage and bone samples from MPS IVA patients will be required to verify the validity of these proteins as potential biomarkers of MPS IVA.
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14
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Holzhütter HG, Berndt N. Computational Hypothesis: How Intra-Hepatic Functional Heterogeneity May Influence the Cascading Progression of Free Fatty Acid-Induced Non-Alcoholic Fatty Liver Disease (NAFLD). Cells 2021; 10:cells10030578. [PMID: 33808045 PMCID: PMC7999144 DOI: 10.3390/cells10030578] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 03/01/2021] [Accepted: 03/02/2021] [Indexed: 02/06/2023] Open
Abstract
Non-Alcoholic Fatty Liver Disease (NAFLD) is the most common type of chronic liver disease in developed nations, affecting around 25% of the population. Elucidating the factors causing NAFLD in individual patients to progress in different rates and to different degrees of severity, is a matter of active medical research. Here, we aim to provide evidence that the intra-hepatic heterogeneity of rheological, metabolic and tissue-regenerating capacities plays a central role in disease progression. We developed a generic mathematical model that constitutes the liver as ensemble of small liver units differing in their capacities to metabolize potentially cytotoxic free fatty acids (FFAs) and to repair FFA-induced cell damage. Transition from simple steatosis to more severe forms of NAFLD is described as self-amplifying process of cascading liver failure, which, to stop, depends essentially on the distribution of functional capacities across the liver. Model simulations provided the following insights: (1) A persistently high plasma level of FFAs is sufficient to drive the liver through different stages of NAFLD; (2) Presence of NAFLD amplifies the deleterious impact of additional tissue-damaging hits; and (3) Coexistence of non-steatotic and highly steatotic regions is indicative for the later occurrence of severe NAFLD stages.
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Affiliation(s)
- Hermann-Georg Holzhütter
- Institute of Biochemistry, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Correspondence:
| | - Nikolaus Berndt
- Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany;
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15
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Álvarez VJ, Bravo SB, Chantada-Vazquez MP, Colón C, De Castro MJ, Morales M, Vitoria I, Tomatsu S, Otero-Espinar FJ, Couce ML. Characterization of New Proteomic Biomarker Candidates in Mucopolysaccharidosis Type IVA. Int J Mol Sci 2020; 22:ijms22010226. [PMID: 33379360 PMCID: PMC7795692 DOI: 10.3390/ijms22010226] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 12/21/2020] [Accepted: 12/24/2020] [Indexed: 12/17/2022] Open
Abstract
Mucopolysaccharidosis type IVA (MPS IVA) is a lysosomal storage disease caused by mutations in the N-acetylgalactosamine-6-sulfatase (GALNS) gene. Skeletal dysplasia and the related clinical features of MPS IVA are caused by disruption of the cartilage and its extracellular matrix, leading to a growth imbalance. Enzyme replacement therapy (ERT) with recombinant human GALNS has yielded positive results in activity of daily living and endurance tests. However, no data have demonstrated improvements in bone lesions and bone grow thin MPS IVA after ERT, and there is no correlation between therapeutic efficacy and urine levels of keratan sulfate, which accumulates in MPS IVA patients. Using qualitative and quantitative proteomics approaches, we analyzed leukocyte samples from healthy controls (n = 6) and from untreated (n = 5) and ERT-treated (n = 8, sampled before and after treatment) MPS IVA patients to identify potential biomarkers of disease. Out of 690 proteins identified in leukocytes, we selected a group of proteins that were dysregulated in MPS IVA patients with ERT. From these, we identified four potential protein biomarkers, all of which may influence bone and cartilage metabolism: lactotransferrin, coronin 1A, neutral alpha-glucosidase AB, and vitronectin. Further studies of cartilage and bone alterations in MPS IVA will be required to verify the validity of these proteins as potential biomarkers of MPS IVA.
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Affiliation(s)
- Víctor J. Álvarez
- Department of Forensic Sciences, Pathology, Gynecology and Obstetrics, Pediatrics, Neonatology Service, Department of Paediatrics, Hospital Clínico Universitario de Santiago de Compostela, Health Research Institute of Santiago de Compostela (IDIS), CIBERER, MetabERN, 15706 Santiago de Compostela, Spain or (V.J.Á.); (C.C.); (M.J.D.C.)
- Skeletal Dysplasia Lab Nemours Biomedical Research Nemours/Alfred I. du Pont Hospital for Children, 1600 Rockland Road., Wilmington, DE 19803, USA;
| | - Susana B. Bravo
- Proteomic Platform, Health Research Institute of Santiago de Compostela (IDIS), Hospital Clínico Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain; (S.B.B.); (M.P.C.-V.)
| | - Maria Pilar Chantada-Vazquez
- Proteomic Platform, Health Research Institute of Santiago de Compostela (IDIS), Hospital Clínico Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain; (S.B.B.); (M.P.C.-V.)
| | - Cristóbal Colón
- Department of Forensic Sciences, Pathology, Gynecology and Obstetrics, Pediatrics, Neonatology Service, Department of Paediatrics, Hospital Clínico Universitario de Santiago de Compostela, Health Research Institute of Santiago de Compostela (IDIS), CIBERER, MetabERN, 15706 Santiago de Compostela, Spain or (V.J.Á.); (C.C.); (M.J.D.C.)
| | - María J. De Castro
- Department of Forensic Sciences, Pathology, Gynecology and Obstetrics, Pediatrics, Neonatology Service, Department of Paediatrics, Hospital Clínico Universitario de Santiago de Compostela, Health Research Institute of Santiago de Compostela (IDIS), CIBERER, MetabERN, 15706 Santiago de Compostela, Spain or (V.J.Á.); (C.C.); (M.J.D.C.)
| | - Montserrat Morales
- Minority Diseases Unit Hospital Universitario12 de Octubre, 28041 Madrid, Spain;
| | - Isidro Vitoria
- Nutrition and Metabolophaties Unit, Hospital Universitario La Fe, 46026 Valencia, Spain;
| | - Shunji Tomatsu
- Skeletal Dysplasia Lab Nemours Biomedical Research Nemours/Alfred I. du Pont Hospital for Children, 1600 Rockland Road., Wilmington, DE 19803, USA;
| | - Francisco J. Otero-Espinar
- Paraquasil Platform, Health Research Institute of Santiago de Compostela (IDIS), Hospital Clínico Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain;
- Department of Pharmacology, Pharmacy and Pharmaceutical Technology, School of Pharmacy, Campus Vida, University of Santiago de Compostela, 15872 Santiago de Compostela, Spain
| | - María L. Couce
- Department of Forensic Sciences, Pathology, Gynecology and Obstetrics, Pediatrics, Neonatology Service, Department of Paediatrics, Hospital Clínico Universitario de Santiago de Compostela, Health Research Institute of Santiago de Compostela (IDIS), CIBERER, MetabERN, 15706 Santiago de Compostela, Spain or (V.J.Á.); (C.C.); (M.J.D.C.)
- Correspondence: or ; Tel.: +34-981-951-100
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16
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Krasny L, Huang PH. Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology. Mol Omics 2020; 17:29-42. [PMID: 33034323 DOI: 10.1039/d0mo00072h] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Data-independent acquisition mass spectrometry (DIA-MS) is a next generation proteomic methodology that generates permanent digital proteome maps offering highly reproducible retrospective analysis of cellular and tissue specimens. The adoption of this technology has ushered a new wave of oncology studies across a wide range of applications including its use in molecular classification, oncogenic pathway analysis, drug and biomarker discovery and unravelling mechanisms of therapy response and resistance. In this review, we provide an overview of the experimental workflows commonly used in DIA-MS, including its current strengths and limitations versus conventional data-dependent acquisition mass spectrometry (DDA-MS). We further summarise a number of key studies to illustrate the power of this technology when applied to different facets of oncology. Finally we offer a perspective of the latest innovations in DIA-MS technology and machine learning-based algorithms necessary for driving the development of high-throughput, in-depth and reproducible proteomic assays that are compatible with clinical diagnostic workflows, which will ultimately enable the delivery of precision cancer medicine to achieve optimal patient outcomes.
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Affiliation(s)
- Lukas Krasny
- Division of Molecular Pathology, The Institute of Cancer Research, 237 Fulham Road, London, SW3 6JB, UK.
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17
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Quantitative proteomics identifies a plasma multi-protein model for detection of hepatocellular carcinoma. Sci Rep 2020; 10:15552. [PMID: 32968147 PMCID: PMC7511324 DOI: 10.1038/s41598-020-72510-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 08/31/2020] [Indexed: 12/13/2022] Open
Abstract
More efficient biomarkers are needed to facilitate the early detection of hepatocellular carcinoma (HCC). We aimed to identify candidate biomarkers for HCC detection by proteomic analysis. First, we performed a global proteomic analysis of 10 paired HCC and non-tumor tissues. Then, we validated the top-ranked proteins by targeted proteomic analyses in another tissue cohort. At last, we used enzyme-linked immunosorbent assays to validate the candidate biomarkers in multiple serum cohorts including HCC cases (HCCs), cirrhosis cases (LCs), and normal controls (NCs). We identified and validated 33 up-regulated proteins in HCC tissues. Among them, eight secretory or membrane proteins were further evaluated in serum, revealing that aldo-keto reductase family 1 member B10 (AKR1B10) and cathepsin A (CTSA) can distinguish HCCs from LCs and NCs. The area under the curves (AUCs) were 0.891 and 0.894 for AKR1B10 and CTSA, respectively, greater than that of alpha-fetoprotein (AFP; 0.831). Notably, combining the three proteins reached an AUC of 0.969, which outperformed AFP alone (P < 0.05). Furthermore, the serum AKR1B10 levels dramatically decreased after surgery. AKR1B10 and CTSA are potential serum biomarkers for HCC detection. The combination of AKR1B10, CTSA, and AFP may improve the HCC diagnostic efficacy.
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18
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Zhu T, Zhu Y, Xuan Y, Gao H, Cai X, Piersma SR, Pham TV, Schelfhorst T, Haas RRGD, Bijnsdorp IV, Sun R, Yue L, Ruan G, Zhang Q, Hu M, Zhou Y, Van Houdt WJ, Le Large TYS, Cloos J, Wojtuszkiewicz A, Koppers-Lalic D, Böttger F, Scheepbouwer C, Brakenhoff RH, van Leenders GJLH, Ijzermans JNM, Martens JWM, Steenbergen RDM, Grieken NC, Selvarajan S, Mantoo S, Lee SS, Yeow SJY, Alkaff SMF, Xiang N, Sun Y, Yi X, Dai S, Liu W, Lu T, Wu Z, Liang X, Wang M, Shao Y, Zheng X, Xu K, Yang Q, Meng Y, Lu C, Zhu J, Zheng J, Wang B, Lou S, Dai Y, Xu C, Yu C, Ying H, Lim TK, Wu J, Gao X, Luan Z, Teng X, Wu P, Huang S, Tao Z, Iyer NG, Zhou S, Shao W, Lam H, Ma D, Ji J, Kon OL, Zheng S, Aebersold R, Jimenez CR, Guo T. DPHL: A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery. GENOMICS PROTEOMICS & BIOINFORMATICS 2020; 18:104-119. [PMID: 32795611 PMCID: PMC7646093 DOI: 10.1016/j.gpb.2019.11.008] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 09/03/2019] [Accepted: 11/08/2019] [Indexed: 12/21/2022]
Abstract
To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.
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Affiliation(s)
- Tiansheng Zhu
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China; School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China
| | - Yi Zhu
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China.
| | - Yue Xuan
- Thermo Fisher Scientific (BREMEN) GmbH, Bremen 28195, Germany
| | - Huanhuan Gao
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Xue Cai
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Sander R Piersma
- OncoProteomics Laboratory, Department of Medical Oncology, VU University Medical Center, VU University, Amsterdam 1011, The Netherlands
| | - Thang V Pham
- OncoProteomics Laboratory, Department of Medical Oncology, VU University Medical Center, VU University, Amsterdam 1011, The Netherlands
| | - Tim Schelfhorst
- OncoProteomics Laboratory, Department of Medical Oncology, VU University Medical Center, VU University, Amsterdam 1011, The Netherlands
| | - Richard R G D Haas
- OncoProteomics Laboratory, Department of Medical Oncology, VU University Medical Center, VU University, Amsterdam 1011, The Netherlands
| | - Irene V Bijnsdorp
- OncoProteomics Laboratory, Department of Medical Oncology, VU University Medical Center, VU University, Amsterdam 1011, The Netherlands; Amsterdam UMC, Vrije Universiteit Amsterdam, Urology, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands
| | - Rui Sun
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Liang Yue
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Guan Ruan
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Qiushi Zhang
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Mo Hu
- Thermo Fisher Scientific, Shanghai 201206, China
| | - Yue Zhou
- Thermo Fisher Scientific, Shanghai 201206, China
| | - Winan J Van Houdt
- The Netherlands Cancer Institute, Surgical Oncology, Amsterdam 1011, The Netherlands
| | - Tessa Y S Le Large
- Amsterdam UMC, Vrije Universiteit Amsterdam, Surgery, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands
| | - Jacqueline Cloos
- Amsterdam UMC, Vrije Universiteit Amsterdam, Pediatric Oncology/Hematology, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands
| | - Anna Wojtuszkiewicz
- Amsterdam UMC, Vrije Universiteit Amsterdam, Pediatric Oncology/Hematology, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands
| | - Danijela Koppers-Lalic
- Amsterdam UMC, Vrije Universiteit Amsterdam, Hematology, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands
| | - Franziska Böttger
- Amsterdam UMC, Vrije Universiteit Amsterdam, Medical Oncology, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands
| | - Chantal Scheepbouwer
- Amsterdam UMC, Vrije Universiteit Amsterdam, Neurosurgery, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands; Amsterdam UMC, Vrije Universiteit Amsterdam, Pathology, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands
| | - Ruud H Brakenhoff
- Amsterdam UMC, Vrije Universiteit Amsterdam, Otolaryngology/Head and Neck Surgery, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands
| | | | - Jan N M Ijzermans
- Erasmus MC University Medical Center, Surgery, Rotterdam 1016LV, The Netherlands
| | - John W M Martens
- Erasmus MC University Medical Center, Medical Oncology, Rotterdam 1016LV, The Netherlands
| | - Renske D M Steenbergen
- Amsterdam UMC, Vrije Universiteit Amsterdam, Pathology, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands
| | - Nicole C Grieken
- Amsterdam UMC, Vrije Universiteit Amsterdam, Pathology, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands
| | | | - Sangeeta Mantoo
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169608, Singapore
| | - Sze S Lee
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore 169608, Singapore
| | - Serene J Y Yeow
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore 169608, Singapore
| | - Syed M F Alkaff
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169608, Singapore
| | - Nan Xiang
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Yaoting Sun
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Xiao Yi
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Shaozheng Dai
- School of Computer Science and Engineering, Beihang University, Beijing 100191, China
| | - Wei Liu
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Tian Lu
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Zhicheng Wu
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China; School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China
| | - Xiao Liang
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Man Wang
- MOE Key Laboratory of Carcinogenesis and Translational Research, Department of Gastrointestinal Translational Research, Peking University Cancer Hospital, Beijing 100142, China
| | - Yingkuan Shao
- Cancer Institute (MOE Key Laboratory of Cancer Prevention and Intervention, Zhejiang Provincial Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Xi Zheng
- Cancer Institute (MOE Key Laboratory of Cancer Prevention and Intervention, Zhejiang Provincial Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Kailun Xu
- Cancer Institute (MOE Key Laboratory of Cancer Prevention and Intervention, Zhejiang Provincial Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Qin Yang
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yifan Meng
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Cong Lu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jiang Zhu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jin'e Zheng
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Bo Wang
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Sai Lou
- Phase I Clinical Research Center, Zhejiang Provincial People's Hospital, Hangzhou 310014, China
| | - Yibei Dai
- Department of Laboratory Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Chao Xu
- College of Mathematics and Informatics, Digital Fujian Institute of Big Data Security Technology, Fujian Normal University, Fuzhou 350108, China
| | - Chenhuan Yu
- Zhejiang Provincial Key Laboratory of Experimental Animal and Safety Evaluation, Zhejiang Academy of Medical Sciences, Hangzhou 310015, China
| | - Huazhong Ying
- Zhejiang Provincial Key Laboratory of Experimental Animal and Safety Evaluation, Zhejiang Academy of Medical Sciences, Hangzhou 310015, China
| | - Tony K Lim
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169608, Singapore
| | - Jianmin Wu
- MOE Key Laboratory of Carcinogenesis and Translational Research, Department of Gastrointestinal Translational Research, Peking University Cancer Hospital, Beijing 100142, China
| | - Xiaofei Gao
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China; Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Zhongzhi Luan
- School of Computer Science and Engineering, Beihang University, Beijing 100191, China
| | - Xiaodong Teng
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Peng Wu
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Shi'ang Huang
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Zhihua Tao
- Department of Laboratory Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Narayanan G Iyer
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore 169608, Singapore
| | - Shuigeng Zhou
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China
| | - Wenguang Shao
- Department of Biology, Institute for Molecular Systems Biology, ETH Zurich, Zurich 8092, Switzerland
| | - Henry Lam
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong Special Administrative Region, China
| | - Ding Ma
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jiafu Ji
- MOE Key Laboratory of Carcinogenesis and Translational Research, Department of Gastrointestinal Translational Research, Peking University Cancer Hospital, Beijing 100142, China
| | - Oi L Kon
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore 169608, Singapore
| | - Shu Zheng
- Cancer Institute (MOE Key Laboratory of Cancer Prevention and Intervention, Zhejiang Provincial Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Ruedi Aebersold
- Department of Biology, Institute for Molecular Systems Biology, ETH Zurich, Zurich 8092, Switzerland; Faculty of Science, University of Zurich, Zurich 8092, Switzerland
| | - Connie R Jimenez
- OncoProteomics Laboratory, Department of Medical Oncology, VU University Medical Center, VU University, Amsterdam 1011, The Netherlands
| | - Tiannan Guo
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China.
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19
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Macklin A, Khan S, Kislinger T. Recent advances in mass spectrometry based clinical proteomics: applications to cancer research. Clin Proteomics 2020; 17:17. [PMID: 32489335 PMCID: PMC7247207 DOI: 10.1186/s12014-020-09283-w] [Citation(s) in RCA: 150] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 05/15/2020] [Indexed: 02/07/2023] Open
Abstract
Cancer biomarkers have transformed current practices in the oncology clinic. Continued discovery and validation are crucial for improving early diagnosis, risk stratification, and monitoring patient response to treatment. Profiling of the tumour genome and transcriptome are now established tools for the discovery of novel biomarkers, but alterations in proteome expression are more likely to reflect changes in tumour pathophysiology. In the past, clinical diagnostics have strongly relied on antibody-based detection strategies, but these methods carry certain limitations. Mass spectrometry (MS) is a powerful method that enables increasingly comprehensive insights into changes of the proteome to advance personalized medicine. In this review, recent improvements in MS-based clinical proteomics are highlighted with a focus on oncology. We will provide a detailed overview of clinically relevant samples types, as well as, consideration for sample preparation methods, protein quantitation strategies, MS configurations, and data analysis pipelines currently available to researchers. Critical consideration of each step is necessary to address the pressing clinical questions that advance cancer patient diagnosis and prognosis. While the majority of studies focus on the discovery of clinically-relevant biomarkers, there is a growing demand for rigorous biomarker validation. These studies focus on high-throughput targeted MS assays and multi-centre studies with standardized protocols. Additionally, improvements in MS sensitivity are opening the door to new classes of tumour-specific proteoforms including post-translational modifications and variants originating from genomic aberrations. Overlaying proteomic data to complement genomic and transcriptomic datasets forges the growing field of proteogenomics, which shows great potential to improve our understanding of cancer biology. Overall, these advancements not only solidify MS-based clinical proteomics' integral position in cancer research, but also accelerate the shift towards becoming a regular component of routine analysis and clinical practice.
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Affiliation(s)
- Andrew Macklin
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Shahbaz Khan
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Thomas Kislinger
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
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20
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Gao H, Zhang F, Liang S, Zhang Q, Lyu M, Qian L, Liu W, Ge W, Chen C, Yi X, Zhu J, Lu C, Sun P, Liu K, Zhu Y, Guo T. Accelerated Lysis and Proteolytic Digestion of Biopsy-Level Fresh-Frozen and FFPE Tissue Samples Using Pressure Cycling Technology. J Proteome Res 2020; 19:1982-1990. [PMID: 32182071 DOI: 10.1021/acs.jproteome.9b00790] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Pressure cycling technology (PCT)-assisted tissue lysis and digestion have facilitated reproducible and high-throughput proteomic studies of both fresh-frozen (FF) and formalin-fixed paraffin-embedded (FFPE) tissue of biopsy scale for biomarker discovery. Here, we present an improved PCT method accelerating the conventional procedures by about two-fold without sacrificing peptide yield, digestion efficiency, peptide, and protein identification. The time required for processing 16 tissue samples from tissues to peptides is reduced from about 6 to about 3 h. We analyzed peptides prepared from FFPE hepatocellular carcinoma (HCC) tissue samples by the accelerated PCT method using multiple MS acquisition methods, including short-gradient SWATH-MS, PulseDIA-MS, and 10-plex TMT-based shotgun MS. The data showed that up to 8541 protein groups could be reliably quantified from the thus prepared peptide samples. We applied the accelerated sample preparation method to 25 pairs (tumorous and matched benign) of HCC samples followed by a single-shot, 15 min gradient SWATH-MS analysis. An average of 18 453 peptides from 2822 proteins were quantified in at least 20% samples in this cohort, while 1817 proteins were quantified in at least 50% samples. The data not only identified the previously known dysregulated proteins such as MCM7, MAPRE1, and SSRP1 but also discovered promising novel protein markers, including DRAP1 and PRMT5. In summary, we present an accelerated PCT protocol that effectively doubles the throughput of PCT-assisted sample preparation of biopsy-level FF and FFPE samples without compromising protein digestion efficiency, peptide yield, and protein identification.
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Affiliation(s)
- Huanhuan Gao
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Fangfei Zhang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Shuang Liang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Qiushi Zhang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Mengge Lyu
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Liujia Qian
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Wei Liu
- Department of Clinical Pharmacology, College of Pharmacy, Dalian Medical University, Dalian 200335, Liaoning, China
| | - Weigang Ge
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | | | - Xiao Yi
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Jiang Zhu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, Hubei, China
| | - Cong Lu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, Hubei, China
| | - Ping Sun
- Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, Hubei, China
| | - Kexin Liu
- Department of Clinical Pharmacology, College of Pharmacy, Dalian Medical University, Dalian 200335, Liaoning, China
| | - Yi Zhu
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Tiannan Guo
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
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21
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Sun R, Hunter C, Chen C, Ge W, Morrice N, Liang S, Zhu T, Yuan C, Ruan G, Zhang Q, Cai X, Yu X, Chen L, Dai S, Luan Z, Aebersold R, Zhu Y, Guo T. Accelerated Protein Biomarker Discovery from FFPE Tissue Samples Using Single-Shot, Short Gradient Microflow SWATH MS. J Proteome Res 2020; 19:2732-2741. [PMID: 32053377 DOI: 10.1021/acs.jproteome.9b00671] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
We reported and evaluated a microflow, single-shot, short gradient SWATH MS method intended to accelerate the discovery and verification of protein biomarkers in preclassified clinical specimens. The method uses a 15 min gradient microflow-LC peptide separation, an optimized SWATH MS window configuration, and OpenSWATH software for data analysis. We applied the method to a cohort containing 204 FFPE tissue samples from 58 prostate cancer patients and 10 benign prostatic hyperplasia patients. Altogether we identified 27,975 proteotypic peptides and 4037 SwissProt proteins from these 204 samples. Compared to a reference SWATH method with a 2 h gradient, we found 3800 proteins were quantified by the two methods on two different instruments with relatively high consistency (r = 0.77). The accelerated method consumed only 17% instrument time, while quantifying 80% of proteins compared to the 2 h gradient SWATH. Although the missing value rate increased by 20%, batch effects reduced by 21%. 75 deregulated proteins measured by the accelerated method were selected for further validation. A shortlist of 134 selected peptide precursors from the 75 proteins were analyzed using MRM-HR, and the results exhibited high quantitative consistency with the 15 min SWATH method (r = 0.89) in the same sample set. We further verified the applicability of these 75 proteins in separating benign and malignant tissues (AUC = 0.99) in an independent prostate cancer cohort (n = 154). Altogether, the results showed that the 15 min gradient microflow SWATH accelerated large-scale data acquisition by 6 times, reduced batch effect by 21%, introduced 20% more missing values, and exhibited comparable ability to separate disease groups.
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Affiliation(s)
- Rui Sun
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | | | | | - Weigang Ge
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | | | - Shuang Liang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - Tiansheng Zhu
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - Chunhui Yuan
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - Guan Ruan
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - Qiushi Zhang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - Xue Cai
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - Xiaoyan Yu
- Department of Pathology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Lirong Chen
- Department of Pathology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Shaozheng Dai
- School of Computer Science and Engineering, Beihang University, Beijing 100083, China
| | - Zhongzhi Luan
- School of Computer Science and Engineering, Beihang University, Beijing 100083, China
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8049 Zurich, Switzerland.,Faculty of Science, University of Zurich, 8006 Zurich, Switzerland
| | - Yi Zhu
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - Tiannan Guo
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
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22
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Bao K, Li X, Kajikawa T, Toshiharu A, Selevsek N, Grossmann J, Hajishengallis G, Bostanci N. Pressure Cycling Technology Assisted Mass Spectrometric Quantification of Gingival Tissue Reveals Proteome Dynamics during the Initiation and Progression of Inflammatory Periodontal Disease. Proteomics 2020; 20:e1900253. [PMID: 31881116 DOI: 10.1002/pmic.201900253] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 12/04/2019] [Indexed: 12/13/2022]
Abstract
Understanding the progression of periodontal tissue destruction is at the forefront of periodontal research. The authors aimed to capture the dynamics of gingival tissue proteome during the initiation and progression of experimental (ligature-induced) periodontitis in mice. Pressure cycling technology (PCT), a recently developed platform that uses ultra-high pressure to disrupt tissues, is utilized to achieve efficient and reproducible protein extraction from ultra-small amounts of gingival tissues in combination with liquid chromatography-tandem mass spectrometry (MS). The MS data are processed using Progenesis QI and the regulated proteins are subjected to METACORE, STRING, and WebGestalt for functional enrichment analysis. A total of 1614 proteins with ≥2 peptides are quantified with an estimated protein false discovery rate of 0.06%. Unsupervised clustering analysis shows that the gingival tissue protein abundance is mainly dependent on the periodontitis progression stage. Gene ontology enrichment analysis reveals an overrepresentation in innate immune regulation (e.g., neutrophil-mediated immunity and antimicrobial peptides), signal transduction (e.g., integrin signaling), and homeostasis processes (e.g., platelet activation and aggregation). In conclusion, a PCT-assisted label-free quantitative proteomics workflow that allowed cataloging the deepest gingival tissue proteome on a rapid timescale and provided novel mechanistic insights into host perturbation during periodontitis progression is applied.
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Affiliation(s)
- Kai Bao
- Section of Peridontology and Dental Prevention, Division of Oral Diseases, Department of Dental Medicine, Kartolinska Insitutet, Alfred Nobels alle 8, 14104, Huddinge, Sweden
| | - Xiaofei Li
- Department of Basic and Translational Sciences, Laboratory of Innate Immunity and Inflammation, School of Dental Medicine, University of Pennsylvania, PA, 19104, Philadelphia, USA
| | - Tetsuhiro Kajikawa
- Department of Basic and Translational Sciences, Laboratory of Innate Immunity and Inflammation, School of Dental Medicine, University of Pennsylvania, PA, 19104, Philadelphia, USA
| | - Abe Toshiharu
- Department of Basic and Translational Sciences, Laboratory of Innate Immunity and Inflammation, School of Dental Medicine, University of Pennsylvania, PA, 19104, Philadelphia, USA
| | - Nathalie Selevsek
- Swiss Integrative Center for Human Health, Passage du Cardinal 13 B, CH-1700, Fribourg, Switzerland
| | - Jonas Grossmann
- Function Genomic Centre, ETH Zurich and University of Zurich, 8092, Zurich, Switzerland
| | - George Hajishengallis
- Department of Basic and Translational Sciences, Laboratory of Innate Immunity and Inflammation, School of Dental Medicine, University of Pennsylvania, PA, 19104, Philadelphia, USA
| | - Nagihan Bostanci
- Section of Peridontology and Dental Prevention, Division of Oral Diseases, Department of Dental Medicine, Kartolinska Insitutet, Alfred Nobels alle 8, 14104, Huddinge, Sweden
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23
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Álvarez JV, Bravo SB, García-Vence M, De Castro MJ, Luzardo A, Colón C, Tomatsu S, Otero-Espinar FJ, Couce ML. Proteomic Analysis in Morquio A Cells Treated with Immobilized Enzymatic Replacement Therapy on Nanostructured Lipid Systems. Int J Mol Sci 2019; 20:ijms20184610. [PMID: 31540344 PMCID: PMC6769449 DOI: 10.3390/ijms20184610] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 09/07/2019] [Accepted: 09/13/2019] [Indexed: 12/14/2022] Open
Abstract
Morquio A syndrome, or mucopolysaccharidosis type IVA (MPS IVA), is a lysosomal storage disease due to mutations in the N-acetylgalactosamine-6-sulfatase (GALNS) gene. Systemic skeletal dysplasia and the related clinical features of MPS IVA are due to disruption of cartilage and its extracellular matrix, leading to an imbalance of growth. Enzyme replacement therapy (ERT) with recombinant human GALNS, alpha elosulfase, provides a systemic treatment. However, this therapy has a limited impact on skeletal dysplasia because the infused enzyme cannot penetrate cartilage and bone. Therefore, an alternative therapeutic approach to reach the cartilage is an unmet challenge. We have developed a new drug delivery system based on a nanostructure lipid carrier with the capacity to immobilize enzymes used for ERT and to target the lysosomes. This study aimed to assess the effect of the encapsulated enzyme in this new delivery system, using in vitro proteomic technology. We found a greater internalization of the enzyme carried by nanoparticles inside the cells and an improvement of cellular protein routes previously impaired by the disease, compared with conventional ERT. This is the first qualitative and quantitative proteomic assay that demonstrates the advantages of a new delivery system to improve the MPS IVA ERT.
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Affiliation(s)
- J Víctor Álvarez
- Department of Pharmacology, Pharmacy and Pharmaceutical Technology, School of Pharmacy, Campus Vida, University of Santiago de Compostela, 15872 Santiago de Compostela, Spain.
- Department of Forensic Sciences, Pathology, Gynecology and Obstetrics, Pediatrics, Neonatology Service, Department of Paediatrics, Hospital Clínico Universitario de Santiago de Compostela, Health Research Institute of Santiago de Compostela (IDIS), CIBERER, MetabERN, 15706 Santiago de Compostela, Spain.
- Skeletal Dysplasia Lab Nemours Biomedical Research Nemours/Alfred I. duPont Hospital for Children, 1600 Rockland Road, Wilmington, DE 19803, USA.
| | - Susana B Bravo
- Proteomic Platform, Health Research Institute of Santiago de Compostela (IDIS), Hospital Clínico Universitario de Santiago de Compostela, 15706 Santiago de Compostea, Spain.
| | - María García-Vence
- Proteomic Platform, Health Research Institute of Santiago de Compostela (IDIS), Hospital Clínico Universitario de Santiago de Compostela, 15706 Santiago de Compostea, Spain.
| | - María J De Castro
- Department of Forensic Sciences, Pathology, Gynecology and Obstetrics, Pediatrics, Neonatology Service, Department of Paediatrics, Hospital Clínico Universitario de Santiago de Compostela, Health Research Institute of Santiago de Compostela (IDIS), CIBERER, MetabERN, 15706 Santiago de Compostela, Spain.
| | - Asteria Luzardo
- Department of Pharmacology, Pharmacy and Pharmaceutical Technology, School of Sciences, Campus de Lugo, University of Santiago de Compostela, 27002 Lugo, Spain.
- Paraquasil Platform, Health Research Institute of Santiago de Compostela (IDIS), Hospital Clínico Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain.
| | - Cristóbal Colón
- Department of Forensic Sciences, Pathology, Gynecology and Obstetrics, Pediatrics, Neonatology Service, Department of Paediatrics, Hospital Clínico Universitario de Santiago de Compostela, Health Research Institute of Santiago de Compostela (IDIS), CIBERER, MetabERN, 15706 Santiago de Compostela, Spain.
| | - Shunji Tomatsu
- Skeletal Dysplasia Lab Nemours Biomedical Research Nemours/Alfred I. duPont Hospital for Children, 1600 Rockland Road, Wilmington, DE 19803, USA.
| | - Francisco J Otero-Espinar
- Department of Pharmacology, Pharmacy and Pharmaceutical Technology, School of Pharmacy, Campus Vida, University of Santiago de Compostela, 15872 Santiago de Compostela, Spain.
- Paraquasil Platform, Health Research Institute of Santiago de Compostela (IDIS), Hospital Clínico Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain.
| | - María L Couce
- Department of Forensic Sciences, Pathology, Gynecology and Obstetrics, Pediatrics, Neonatology Service, Department of Paediatrics, Hospital Clínico Universitario de Santiago de Compostela, Health Research Institute of Santiago de Compostela (IDIS), CIBERER, MetabERN, 15706 Santiago de Compostela, Spain.
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24
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Zhu Y, Weiss T, Zhang Q, Sun R, Wang B, Yi X, Wu Z, Gao H, Cai X, Ruan G, Zhu T, Xu C, Lou S, Yu X, Gillet L, Blattmann P, Saba K, Fankhauser CD, Schmid MB, Rutishauser D, Ljubicic J, Christiansen A, Fritz C, Rupp NJ, Poyet C, Rushing E, Weller M, Roth P, Haralambieva E, Hofer S, Chen C, Jochum W, Gao X, Teng X, Chen L, Zhong Q, Wild PJ, Aebersold R, Guo T. High-throughput proteomic analysis of FFPE tissue samples facilitates tumor stratification. Mol Oncol 2019; 13:2305-2328. [PMID: 31495056 PMCID: PMC6822243 DOI: 10.1002/1878-0261.12570] [Citation(s) in RCA: 88] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 08/09/2019] [Accepted: 09/03/2019] [Indexed: 11/06/2022] Open
Abstract
Formalin‐fixed, paraffin‐embedded (FFPE), biobanked tissue samples offer an invaluable resource for clinical and biomarker research. Here, we developed a pressure cycling technology (PCT)‐SWATH mass spectrometry workflow to analyze FFPE tissue proteomes and applied it to the stratification of prostate cancer (PCa) and diffuse large B‐cell lymphoma (DLBCL) samples. We show that the proteome patterns of FFPE PCa tissue samples and their analogous fresh‐frozen (FF) counterparts have a high degree of similarity and we confirmed multiple proteins consistently regulated in PCa tissues in an independent sample cohort. We further demonstrate temporal stability of proteome patterns from FFPE samples that were stored between 1 and 15 years in a biobank and show a high degree of the proteome pattern similarity between two types of histological regions in small FFPE samples, that is, punched tissue biopsies and thin tissue sections of micrometer thickness, despite the existence of a certain degree of biological variations. Applying the method to two independent DLBCL cohorts, we identified myeloperoxidase, a peroxidase enzyme, as a novel prognostic marker. In summary, this study presents a robust proteomic method to analyze bulk and biopsy FFPE tissues and reports the first systematic comparison of proteome maps generated from FFPE and FF samples. Our data demonstrate the practicality and superiority of FFPE over FF samples for proteome in biomarker discovery. Promising biomarker candidates for PCa and DLBCL have been discovered.
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Affiliation(s)
- Yi Zhu
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China.,Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology and Brain Tumor Center, University Hospital Zurich, University of Zurich, Switzerland
| | - Qiushi Zhang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Rui Sun
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Bo Wang
- Department of Pathology, The First Affiliated Hospital of College of Medicine, Zhejiang University, Hangzhou, China
| | - Xiao Yi
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Zhicheng Wu
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Huanhuan Gao
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Xue Cai
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Guan Ruan
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Tiansheng Zhu
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Chao Xu
- College of Mathematics and Informatics, Digital Fujian Institute of Big Data Security Technology, Fujian Normal University, Fuzhou, China
| | - Sai Lou
- Phase I Clinical Research Center, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Xiaoyan Yu
- Department of Pathology, The Second Affiliated Hospital of College of Medicine, Zhejiang University, Hangzhou, China
| | - Ludovic Gillet
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Switzerland
| | - Peter Blattmann
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Switzerland
| | - Karim Saba
- Department of Urology, University Hospital Zurich, University of Zurich, Switzerland
| | | | - Michael B Schmid
- Department of Urology, University Hospital Zurich, University of Zurich, Switzerland
| | - Dorothea Rutishauser
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Switzerland
| | - Jelena Ljubicic
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Switzerland
| | - Ailsa Christiansen
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Switzerland
| | - Christine Fritz
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Switzerland
| | - Niels J Rupp
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Switzerland
| | - Cedric Poyet
- Department of Urology, University Hospital Zurich, University of Zurich, Switzerland
| | - Elisabeth Rushing
- Department of Neuropathology, University Hospital Zurich, University of Zurich, Switzerland
| | - Michael Weller
- Department of Neurology and Brain Tumor Center, University Hospital Zurich, University of Zurich, Switzerland
| | - Patrick Roth
- Department of Neurology and Brain Tumor Center, University Hospital Zurich, University of Zurich, Switzerland
| | - Eugenia Haralambieva
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Switzerland
| | - Silvia Hofer
- Division of Medical Oncology, Lucerne Cantonal Hospital and Cancer Center, Switzerland
| | | | - Wolfram Jochum
- Institute of Pathology, Cantonal Hospital St. Gallen, Switzerland
| | - Xiaofei Gao
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Xiaodong Teng
- Department of Pathology, The First Affiliated Hospital of College of Medicine, Zhejiang University, Hangzhou, China
| | - Lirong Chen
- Department of Pathology, The Second Affiliated Hospital of College of Medicine, Zhejiang University, Hangzhou, China
| | - Qing Zhong
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Switzerland.,Children's Medical Research Institute, University of Sydney, Australia
| | - Peter J Wild
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Switzerland.,Dr. Senckenberg Institute of Pathology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Switzerland.,Faculty of Science, University of Zurich, Switzerland
| | - Tiannan Guo
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China.,Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Switzerland
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25
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Longuespée R, Casadonte R, Schwamborn K, Kriegsmann M. Proteomics in Pathology: The Special Issue. Proteomics Clin Appl 2019; 13:e1800167. [PMID: 30730117 DOI: 10.1002/prca.201800167] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Rémi Longuespée
- Institute of Pathology, University of Heidelberg, 69120, Heidelberg, Germany
| | | | - Kristina Schwamborn
- Institute of Pathology, Technical University of Munich, 81675, Munich, Germany
| | - Mark Kriegsmann
- Institute of Pathology, University of Heidelberg, 69120, Heidelberg, Germany
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