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Wang C, Feng G, Zhao J, Xu Y, Li Y, Wang L, Wang M, Liu M, Wang Y, Mu H, Zhou C. Screening of novel biomarkers for acute kidney transplant rejection using DIA-MS based proteomics. Proteomics Clin Appl 2024; 18:e2300047. [PMID: 38215274 DOI: 10.1002/prca.202300047] [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: 04/27/2023] [Revised: 11/03/2023] [Accepted: 11/22/2023] [Indexed: 01/14/2024]
Abstract
BACKGROUND Kidney transplantation is the preferred treatment for patients with end-stage renal disease. However, acute rejection poses a threat to the graft long-term survival. The aim of this study was to identify novel biomarkers to detect acute kidney transplant rejection. METHODS The serum proteomic profiling of kidney transplant patients with T cell-mediated acute rejection (TCMR) and stable allograft function (STA) was analyzed using data-independent acquisition mass spectrometry (DIA-MS). The differentially expressed proteins (DEPs) of interest were further verified by enzyme-linked immunosorbent assay (ELISA). RESULTS A total of 131 DEPs were identified between STA and TCMR patients, 114 DEPs were identified between mild and severe TCMR patients. The verification results showed that remarkable higher concentrations of serum amyloid A protein 1 (SAA1) and insulin like growth factor binding protein 2 (IGFBP2), and lower fetuin-A (AHSG) concentration were found in TCMR patients when compared with STA patients. We also found higher SAA1 concentration in severe TCMR group when compared with mild TCMR group. The receiver operating characteristics (ROC) analysis further confirmed that combination of SAA1, AHSG, and IGFBP2 had excellent performance in the acute rejection diagnosis. CONCLUSIONS Our data demonstrated that serum SAA1, AHSG, and IGFBP2 could be effective biomarkers for diagnosing acute rejection after kidney transplantation. DIA-MS has great potential in biomarker screening of kidney transplantation.
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Affiliation(s)
- Ce Wang
- Department of Clinical Laboratory, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Gang Feng
- Department of Kidney Transplant, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Jie Zhao
- Department of Kidney Transplant, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Yang Xu
- Department of Kidney Transplant, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Yang Li
- Department of Clinical Laboratory, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Lin Wang
- Department of Clinical Laboratory, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Meng Wang
- Department of Clinical Laboratory, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Miao Liu
- The First Central Clinical School, Tianjin Medical University, Tianjin, China
| | - Yilin Wang
- The First Central Clinical School, Tianjin Medical University, Tianjin, China
| | - Hong Mu
- Department of Clinical Laboratory, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Chunlei Zhou
- Department of Clinical Laboratory, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
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Clouthier KL, Taylor AC, Xuhuai J, Liu Y, Parker S, Van Eyk J, Reddy S. A Noninvasive Circulating Signature of Combined Right Ventricular Pressure and Volume Overload in Tetralogy of Fallot/Pulmonary Atresia/Major Aortopulmonary Collateral Arteries. World J Pediatr Congenit Heart Surg 2024; 15:162-173. [PMID: 38128927 DOI: 10.1177/21501351231213626] [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: 12/23/2023]
Abstract
Background: Despite surgical advances, children with tetralogy of Fallot/pulmonary atresia/major aortopulmonary collaterals (TOF/PA/MAPCAs) are subject to chronic right ventricular (RV) pressure and volume overload. Current diagnostic tools do not identify adverse myocardial remodeling and cannot predict progression to RV failure. We sought to identify a noninvasive, circulating signature of the systemic response to right heart stress to follow disease progression. Methods: Longitudinal data were collected from patients with TOF/PA/MAPCAs (N = 5) at the time of (1) early RV pressure overload and (2) late RV pressure and volume overload. Plasma protein and microRNA expression were evaluated using high-throughput data-independent mass spectroscopy and Agilent miR Microarray, respectively. Results: At the time of early RV pressure overload, median patient age was 0.34 years (0.02-9.37), with systemic RV pressures, moderate-severe hypertrophy, and preserved systolic function. Late RV pressure and volume overload occurred at a median age of 4.08 years (1.51-10.83), with moderate RV hypertrophy and dilation, and low normal RV function; 277 proteins were significantly dysregulated (log2FC ≥0.6/≤-0.6, FDR≤0.05), predicting downregulation in lipid transport (apolipoproteins), fibrinolytic system, and extracellular matrix structural proteins (talin 1, profilin 1); and upregulation in the respiratory burst. Increasing RV size and decreasing RV function correlated with decreasing structural protein expression. Similarly, miR expression predicted downregulation of extracellular matrix-receptor interactions and upregulation in collagen synthesis. Conclusion: To our knowledge, we show for the first time a noninvasive protein and miR signature reflecting the systemic response to adverse RV myocardial remodeling in TOF/PA/MAPCAs which could be used to follow disease progression.
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Affiliation(s)
- Katie L Clouthier
- Department of Pediatrics (Cardiology), Stanford University, Palo Alto, CA, USA
| | - Anne C Taylor
- Department of Pediatrics (Cardiology), Stanford University, Palo Alto, CA, USA
| | - Ji Xuhuai
- Human Immune Monitoring Center and Functional Genomics Facility, Stanford University, Palo Alto, CA, USA
| | - Yuhan Liu
- Department of Medicine (Quantitative Science Unit), Stanford University, Palo Alto, CA, USA
| | - Sarah Parker
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jennifer Van Eyk
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sushma Reddy
- Department of Pediatrics (Cardiology), Stanford University, Palo Alto, CA, USA
- Cardiovascular Institute, Stanford University, Los Angeles, CA, USA
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3
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Zhou Y, Zheng H, Tan Z, Kang E, Xue P, Li X, Guan F. Optimizing and integrating depletion and precipitation methods for plasma proteomics through data-independent acquisition-mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci 2024; 1235:124046. [PMID: 38382157 DOI: 10.1016/j.jchromb.2024.124046] [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: 12/09/2023] [Revised: 01/29/2024] [Accepted: 02/10/2024] [Indexed: 02/23/2024]
Abstract
The application of plasma proteomics is a reliable approach for the discovery of biomarkers. However, the utilization of mass spectrometry-based proteomics in plasma encounters limitations due to the presence of high-abundant proteins (HAPs) and the vast dynamic range. To address this issue, we conducted an optimization and integration of depletion and precipitation strategies eliminating interference from HAPs. The optimized procedure involved utilizing 40 µL of beads for the removal of 1 µL of plasma, and maintaining a ratio of 1:1:1 between plasma, urea, and trichloroacetic acid for the precipitation of 50 µL of plasma. To facilitate high-throughput processing, experimental procedures were carried out utilizing 96-well plates. The depletion method identified a total of 1510 proteins, whereas the precipitated method yielded a total of 802 proteins. The integration of these methods yielded a total of 1794 proteins, including a wide concentration range spanning over 8 orders of magnitude. Furthermore, these approaches exhibited a commendable level of reproducibility, as indicated by median coefficients of variation of 14.7 % and 21.1 % for protein intensities, respectively. The integrative method was found to be effective in precisely quantifying yeast proteins that were intentionally spiked in plasma at predetermined rations of 5, 2, 0.5, and 0.2 with a high genuine positive recovery with a range of 71 % to 91 % of all yeast proteins. The use of a complementary and finely tuned approach involving depletion and precipitation demonstrates tremendous potential in the field of discovering protein biomarkers from large-scale cohort studies.
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Affiliation(s)
- Yue Zhou
- College of Life Science, Northwest University, Xi'an, Shaanxi, China
| | - Helong Zheng
- College of Life Science, Northwest University, Xi'an, Shaanxi, China
| | - Zengqi Tan
- College of Life Science, Northwest University, Xi'an, Shaanxi, China
| | - Enci Kang
- Xi'an Gaoxin No.1 High School International Division, Xi'an, Shaanxi, China
| | - Peng Xue
- Guangzhou National Laboratory, Guangzhou, Guangdong, China
| | - Xiang Li
- College of Life Science, Northwest University, Xi'an, Shaanxi, China
| | - Feng Guan
- College of Life Science, Northwest University, Xi'an, Shaanxi, China.
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Fang H, Greening DW. An Optimized Data-Independent Acquisition Strategy for Comprehensive Analysis of Human Plasma Proteome. Methods Mol Biol 2023; 2628:93-107. [PMID: 36781781 DOI: 10.1007/978-1-0716-2978-9_7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
Abstract
Cartography of the plasma proteome remains technically challenging, primarily due to the abundance and dynamic range of plasma proteins and their concentrations, exceeding ten orders of magnitude, including low-abundant tissue-derived proteins in the pg/mL range. Data-independent acquisition mass spectrometry (DIA-MS) has seen advances in unbiased mass spectrometry-based proteomic analysis of the plasma proteome. Here, we describe a comprehensive proteomic workflow of human plasma from clinically relevant sample (10 μL) that includes anti-protein immunodepletion and highly sensitive sample preparation workflow, with optimized scheduled isolation DIA-MS and deep learning analysis. This approach results in over 960 proteins quantified from a single-shot analysis of broad dynamic range, across 8 orders of magnitude (8.2 ng/L to 0.67 g/L). We further compare data-dependent acquisition (DDA) MS to highlight the advantage in protein quantification and inter-sample variation. These developments have provided streamlined identification of the human plasma proteome, including low-abundant tissue-enriched proteins, and applications toward understanding the plasma proteome.
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Affiliation(s)
- Haoyun Fang
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.,Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, VIC, Australia
| | - David W Greening
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia. .,Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, VIC, Australia. .,Central Clinical School, Monash University, Melbourne, VIC, Australia. .,Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Melbourne, VIC, Australia.
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Casavant EP, Liang J, Sankhe S, Mathews WR, Anania VG. Using SILAC to Develop Quantitative Data-Independent Acquisition (DIA) Proteomic Methods. Methods Mol Biol 2023; 2603:245-257. [PMID: 36370285 DOI: 10.1007/978-1-0716-2863-8_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Proteins are integral to biological systems and functions. Identifying and quantifying proteins can therefore offer systems-wide insights into protein-protein interactions, cellular signaling, and biological pathway activity. The use of quantitative proteomics has become a method of choice for identifying and quantifying proteins in complex matrices. Proteomics allows researchers to survey hundreds to thousands of proteins in a less biased manner than classical antibody-based protein capture strategies. Typically, discovery approaches have used data-dependent acquisition (DDA) methods, but this approach suffers from stochasticity that compromises quantitation. Recent developments in data-independent acquisition (DIA) proteomics workflows enable proteomic profiling of thousands of samples with increased peak picking consistency making it an excellent candidate for discovering and assessing biomarkers in clinical samples. However, quantitation of peptides from DIA datasets is computationally intensive, and guidelines on how to establish DIA methods are lacking. Method development and optimization require novel tools to visualize and filter DIA datasets appropriately. Here, a protocol and novel script workflow for the optimization of quantitative DIA methods using stable isotope labeling of amino acids in culture (SILAC) are presented. This protocol includes steps for cell growth and labeling, peptide digestion and preparation, and optimization of quantitative DIA methods. In addition, important steps for (1) computational analysis to identify and quantify peptides, (2) data visualizations to identify the linear abundance ranges for all peptides in the sample, and (3) descriptions of how to find high confidence quantitation abundance thresholds are described herein.
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Muselius B, Durand SL, Geddes-McAlister J. Proteomics of Cryptococcus neoformans: From the Lab to the Clinic. Int J Mol Sci 2021; 22:12390. [PMID: 34830272 PMCID: PMC8618913 DOI: 10.3390/ijms222212390] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 11/12/2021] [Accepted: 11/15/2021] [Indexed: 12/12/2022] Open
Abstract
Fungal pathogens cause an array of diseases by targeting both immunocompromised and immunocompetent hosts. Fungi overcome our current arsenal of antifungals through the emergence and evolution of resistance. In particular, the human fungal pathogen, Cryptococcus neoformans is found ubiquitously within the environment and causes severe disease in immunocompromised individuals around the globe with limited treatment options available. To uncover fundamental knowledge about this fungal pathogen, as well as investigate new detection and treatment strategies, mass spectrometry-based proteomics provides a plethora of tools and applications, as well as bioinformatics platforms. In this review, we highlight proteomics approaches within the laboratory to investigate changes in the cellular proteome, secretome, and extracellular vesicles. We also explore regulation by post-translational modifications and the impact of protein-protein interactions. Further, we present the development and comprehensive assessment of murine models of cryptococcal infection, which provide valuable tools to define the dynamic relationship between the host and pathogen during disease. Finally, we explore recent quantitative proteomics studies that begin to extrapolate the findings from the bench to the clinic for improved methods of fungal detection and monitoring. Such studies support a framework for personalized medical approaches to eradicate diseases caused by C. neoformans.
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Affiliation(s)
| | | | - Jennifer Geddes-McAlister
- Molecular and Cellular Biology Department, University of Guelph, Guelph, ON N1G 2W1, Canada; (B.M.); (S.-L.D.)
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Zhou Y, Tan Z, Xue P, Wang Y, Li X, Guan F. High-throughput, in-depth and estimated absolute quantification of plasma proteome using data-independent acquisition/mass spectrometry ("HIAP-DIA"). Proteomics 2021; 21:e2000264. [PMID: 33460299 DOI: 10.1002/pmic.202000264] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 01/07/2021] [Accepted: 01/07/2021] [Indexed: 01/01/2023]
Abstract
Mass spectrometry-based plasma proteomics has been demonstrated to be a useful tool capable of quantifying hundreds of proteins in a single LC-MS/MS experiment, for biomarker discovery or elucidation of disease mechanisms. We developed a novel data-independent acquisition (DIA)/MS-based workflow for high-throughput, in-depth and estimated absolute quantification of plasma proteins (termed HIAP-DIA), without depleting high-abundant proteins, in a single-shot experiment. In HIAP-DIA workflow, we generated an ultra-deep cumulative undepleted and depleted spectral library which contained 55,157 peptides and 5,328 proteins, optimized column length (50 cm) and gradient (90 min) of liquid chromatography instrumentation, optimized 50 DIA segments with average isolation window 17 Th, and selected reference proteins for estimated absolute quantification of all plasma proteins. A total of 606 proteins were quantified in triplicate, and 427 proteins were quantified with CV <20% in plasma proteome. R-squared value of overlapped 208 endogenous PQ500 estimated protein amounts from HIAP-DIA and absolute quantification with internal standards was 0.82, indicating high quantification accuracy of HIAP-DIA. As a pilot study, the HIAP-DIA approach described here was applied to a myelodysplastic syndromes (MDS) disease cohort. We achieved absolute quantification of 789 plasma proteins in 22 clinical plasma samples, spanning less than six orders of magnitude with quantification limit 10-20 ng/mL, and discovered 95 differentially expressed proteins providing insights into MDS pathophysiology.
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Affiliation(s)
- Yue Zhou
- The Key Laboratory of Carbohydrate Chemistry & Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, China
| | - Zengqi Tan
- College of Life Science, Northwest University, Xi'an, China
| | - Peng Xue
- Department of Biology, Institute of Molecular Systems Biology, Zürich, Switzerland
| | - Yi Wang
- Department of Hematology, Provincial People's Hospital, Xi'an, China
| | - Xiang Li
- College of Life Science, Northwest University, Xi'an, China
| | - Feng Guan
- The Key Laboratory of Carbohydrate Chemistry & Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, China.,College of Life Science, Northwest University, Xi'an, China
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Hu X, Wang JH, Chen XW. Exploiting arginine distributions for the selective and efficient depletion of arginine-rich plasma proteins. Chem Commun (Camb) 2020; 56:12375-12378. [PMID: 32930244 DOI: 10.1039/d0cc04744a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The number and arrangement of arginine (Arg) residues in protein chains contribute greatly to the selective capturing of proteins on a designed adsorbent consisting of organic phosphate functionalized fibrous SiO2 microspheres, and the efficient depletion of high abundance Arg-rich protein species from human plasma is achieved.
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Affiliation(s)
- Xue Hu
- Research Center for Analytical Sciences, Department of Chemistry, Northeastern University, Box 332, Shenyang 110819, China.
| | - Jian-Hua Wang
- Research Center for Analytical Sciences, Department of Chemistry, Northeastern University, Box 332, Shenyang 110819, China.
| | - Xu-Wei Chen
- Research Center for Analytical Sciences, Department of Chemistry, Northeastern University, Box 332, Shenyang 110819, China.
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