1
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Aguilan JT, Lim J, Racine-Brzostek S, Fischer J, Silvescu C, Cornett S, Nieves E, Mendu DR, Aliste CM, Semple S, Angeletti R, Weiss LM, Cole A, Prystowsky M, Pullman J, Sidoli S. Effect of dynamic exclusion and the use of FAIMS, DIA and MALDI-mass spectrometry imaging with ion mobility on amyloid protein identification. Clin Proteomics 2024; 21:47. [PMID: 38961380 PMCID: PMC11223398 DOI: 10.1186/s12014-024-09500-w] [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: 03/18/2024] [Accepted: 06/26/2024] [Indexed: 07/05/2024] Open
Abstract
Amyloidosis is a disease characterized by local and systemic extracellular deposition of amyloid protein fibrils where its excessive accumulation in tissues and resistance to degradation can lead to organ failure. Diagnosis is challenging because of approximately 36 different amyloid protein subtypes. Imaging methods like immunohistochemistry and the use of Congo red staining of amyloid proteins for laser capture microdissection combined with liquid chromatography tandem mass spectrometry (LMD/LC-MS/MS) are two diagnostic methods currently used depending on the expertise of the pathology laboratory. Here, we demonstrate a streamlined in situ amyloid peptide spatial mapping by Matrix Assisted Laser Desorption Ionization-Mass Spectrometry Imaging (MALDI-MSI) combined with Trapped Ion Mobility Spectrometry for potential transthyretin (ATTR) amyloidosis subtyping. While we utilized the standard LMD/LC-MS/MS workflow for amyloid subtyping of 31 specimens from different organs, we also evaluated the potential introduction in the MS workflow variations in data acquisition parameters like dynamic exclusion, or testing Data Dependent Acquisition combined with High-Field Asymmetric Waveform Ion Mobility Spectrometry (DDA FAIMS) versus Data Independent Acquisition (DIA) for enhanced amyloid protein identification at shorter acquisition times. We also demonstrate the use of Mascot's Error Tolerant Search and PEAKS de novo sequencing for the sequence variant analysis of amyloidosis specimens.
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Affiliation(s)
- Jennifer T Aguilan
- Laboratory for Macromolecular Analysis and Proteomics Facility, Albert Einstein College of Medicine, New York, 10461, USA
- Department of Pathology, Albert Einstein College of Medicine, New York, 10461, USA
- Montefiore Medical Center, Moses and Weiler Campus, New York, 10461, USA
| | - Jihyeon Lim
- Janssen Research and Development, Malvern, PA, USA
| | | | | | | | | | - Edward Nieves
- Laboratory for Macromolecular Analysis and Proteomics Facility, Albert Einstein College of Medicine, New York, 10461, USA
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Damodara Rao Mendu
- Clinical Chemistry Laboratory, Mount Sinai School of Medicine, New York, USA
| | - Carlos-Madrid Aliste
- Laboratory for Macromolecular Analysis and Proteomics Facility, Albert Einstein College of Medicine, New York, 10461, USA
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, New York, 10461, USA
| | | | - Ruth Angeletti
- Laboratory for Macromolecular Analysis and Proteomics Facility, Albert Einstein College of Medicine, New York, 10461, USA
| | - Louis M Weiss
- Department of Pathology, Albert Einstein College of Medicine, New York, 10461, USA
- Montefiore Medical Center, Moses and Weiler Campus, New York, 10461, USA
| | - Adam Cole
- Montefiore Medical Center, Moses and Weiler Campus, New York, 10461, USA
| | - Michael Prystowsky
- Department of Pathology, Albert Einstein College of Medicine, New York, 10461, USA
- Montefiore Medical Center, Moses and Weiler Campus, New York, 10461, USA
| | - James Pullman
- Montefiore Medical Center, Moses and Weiler Campus, New York, 10461, USA
| | - Simone Sidoli
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
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2
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Wang S, Wei D, Zhao Y, Pang X, Zhang Z. Development and validation of machine learning models for diagnosis and prognosis of cancer by urinary proteomics, based on the FLEMENGHO cohort. Am J Cancer Res 2024; 14:643-654. [PMID: 38455408 PMCID: PMC10915340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 12/25/2023] [Indexed: 03/09/2024] Open
Abstract
The current study aims to develop and validate machine learning (ML) models for the prediction of cancer status by the non-invasive urinary proteomic in a population-based cohort. In this retrospective study, urinary proteome profiles in 804 cases from the FLEMENGHO cohort were measured by mass spectrometry. After feature selection by LASSO on both clinical variables and urinary proteome profile, benchmark models by clinical variables were built with six different ML algorithms. Proteome-based models and combined models were built and compared with the benchmark models. The models' performance, i.e. area under the curve (AUC) was compared by Delong method. The 95% confidence interval was estimated by the bootstrapping method. The best-performing model was explained by Shapley Additive Explanations (SHAP) method. The predictive role of proteome biomarkers in longitudinal cancer diagnosis was also explored. A clinical model, based on age, blood sugar and blood lipid profile, yielded the best AUC of 0.75 (0.68-0.82), with 0.80 (0.72-0.91) for the proteome model based on 13 selected biomarkers and 0.83 (0.77-0.90) for the combined model (P=0.01 for comparison with clinical model). SHAP on the support vector machine in the combined setting showed that except for age, proteome biomarkers contribute to the final prediction of the model. After adjusting with clinical factors, three proteome biomarkers are independent risk factors for longitudinal cancer development. Urinary proteome profiling, together with fine-tuned machine learning algorithms, demonstrates the predictive potential for cancer diagnosis transparently.
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Affiliation(s)
- Shuncong Wang
- Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, Department of Cardiovascular Sciences, KU Leuven, University of Leuven, Campus Sint RafaëlKapucijnenvoer 7, Block H, Box 7001, BE-3000 Leuven, Belgium
- Biomedical Group, Campus Gasthuisberg, KU Leuven3000 Leuven, Belgium
| | - Dongmei Wei
- Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, Department of Cardiovascular Sciences, KU Leuven, University of Leuven, Campus Sint RafaëlKapucijnenvoer 7, Block H, Box 7001, BE-3000 Leuven, Belgium
| | - Yanling Zhao
- Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, Department of Cardiovascular Sciences, KU Leuven, University of Leuven, Campus Sint RafaëlKapucijnenvoer 7, Block H, Box 7001, BE-3000 Leuven, Belgium
| | - Xin Pang
- Faculty of Economics and Business, KU Leuven3000 Leuven, Belgium
| | - Zhenyu Zhang
- Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, Department of Cardiovascular Sciences, KU Leuven, University of Leuven, Campus Sint RafaëlKapucijnenvoer 7, Block H, Box 7001, BE-3000 Leuven, Belgium
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3
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Jordaens S, Zwaenepoel K, Tjalma W, Deben C, Beyers K, Vankerckhoven V, Pauwels P, Vorsters A. Urine biomarkers in cancer detection: A systematic review of preanalytical parameters and applied methods. Int J Cancer 2023; 152:2186-2205. [PMID: 36647333 DOI: 10.1002/ijc.34434] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/25/2022] [Accepted: 12/29/2022] [Indexed: 01/18/2023]
Abstract
The aim of this review was to explore the status of urine sampling as a liquid biopsy for noninvasive cancer research by reviewing used preanalytical parameters and protocols. We searched two main health sciences databases, PubMed and Web of Science. From all eligible publications (2010-2022), information was extracted regarding: (a) study population characteristics, (b) cancer type, (c) urine preanalytics, (d) analyte class, (e) isolation method, (f) detection method, (g) comparator used, (h) biomarker type, (i) conclusion and (j) sensitivity and specificity. The search query identified 7835 records, of which 924 unique publications remained after screening the title, abstract and full text. Our analysis demonstrated that many publications did not report information about the preanalytical parameters of their urine samples, even though several other studies have shown the importance of standardization of sample handling. Interestingly, it was noted that urine is used for many cancer types and not just cancers originating from the urogenital tract. Many different types of relevant analytes have been shown to be found in urine. Additionally, future considerations and recommendations are discussed: (a) the heterogeneous nature of urine, (b) the need for standardized practice protocols and (c) the road toward the clinic. Urine is an emerging liquid biopsy with broad applicability in different analytes and several cancer types. However, standard practice protocols for sample handling and processing would help to elaborate the clinical utility of urine in cancer research, detection and disease monitoring.
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Affiliation(s)
- Stephanie Jordaens
- Center for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp, Wilrijk, Belgium.,Novosanis NV, Wijnegem, Belgium
| | - Karen Zwaenepoel
- Center for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp, Wilrijk, Belgium.,Laboratory of Pathological Anatomy, Antwerp University Hospital (UZA), Edegem, Belgium
| | - Wiebren Tjalma
- Center for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp, Wilrijk, Belgium.,Multidisciplinary Breast Clinic, Gynecological Oncology Unit, Department of Obstetrics and Gynecology, Antwerp University Hospital (UZA), Edegem, Belgium
| | - Christophe Deben
- Center for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp, Wilrijk, Belgium
| | | | - Vanessa Vankerckhoven
- Novosanis NV, Wijnegem, Belgium.,Center for Evaluation of Vaccination (CEV), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, Belgium
| | - Patrick Pauwels
- Center for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp, Wilrijk, Belgium.,Laboratory of Pathological Anatomy, Antwerp University Hospital (UZA), Edegem, Belgium
| | - Alex Vorsters
- Center for Evaluation of Vaccination (CEV), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, Belgium
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4
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Dayon L, Cominetti O, Affolter M. Proteomics of Human Biological Fluids for Biomarker Discoveries: Technical Advances and Recent Applications. Expert Rev Proteomics 2022; 19:131-151. [PMID: 35466824 DOI: 10.1080/14789450.2022.2070477] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Biological fluids are routine samples for diagnostic testing and monitoring. Blood samples are typically measured because of their moderate collection invasiveness and high information content on health and disease. Several body fluids, such as cerebrospinal fluid (CSF), are also studied and suited to specific pathologies. Over the last two decades proteomics has quested to identify protein biomarkers but with limited success. Recent technologies and refined pipelines have accelerated the profiling of human biological fluids. AREAS COVERED We review proteomic technologies for the identification of biomarkers. Those are based on antibodies/aptamers arrays or mass spectrometry (MS), but new ones are emerging. Advances in scalability and throughput have allowed to better design studies and cope with the limited sample size that had until now prevailed due to technological constraints. With these enablers, plasma/serum, CSF, saliva, tears, urine, and milk proteomes have been further profiled; we provide a non-exhaustive picture of some recent highlights (mainly covering literature from last five years in the Scopus database) using MS-based proteomics. EXPERT OPINION While proteomics has been in the shadow of genomics for years, proteomic tools and methodologies have reached a certain maturity. They are better suited to discover innovative and robust biofluid biomarkers.
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Affiliation(s)
- Loïc Dayon
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, CH-1015 Lausanne, Switzerland.,Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Ornella Cominetti
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, CH-1015 Lausanne, Switzerland
| | - Michael Affolter
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, CH-1015 Lausanne, Switzerland
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5
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Tang X, Xiao X, Sun H, Zheng S, Xiao X, Guo Z, Liu X, Sun W. 96DRA-urine: A high throughput sample preparation method for urinary proteome analysis. J Proteomics 2022; 257:104529. [PMID: 35181559 DOI: 10.1016/j.jprot.2022.104529] [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: 03/01/2021] [Revised: 01/25/2022] [Accepted: 02/13/2022] [Indexed: 11/26/2022]
Abstract
Mass spectrometry (MS)-based urinary proteomics is increasingly used for clinical research. A critical step in urinary proteomic analysis comprises the implementation of a reliable sample preparation method with high yields of peptides and proteins. In this study, we developed a urinary sample preparation method, DRA-Urine (Direct reduction/alkylation in urine), which urinary proteins were directly reduced/alkylated in urine, and then precipitated by acetone, washed and digestion on an ultrafilter unit. The qualitative and quantitative comparison of different urinary sample preparation methods (in-solution methods and ultrafilter-assisted methods) showed that DRA-Urine could achieve better results. Adapting DRA-Urine protocol to a 96-well format, namely 96DRA-Urine, shortened the time for buffer change and improved sample preparation throughput. The results showed that 96DRA-Urine displayed similar proteomic performance to DRA-Urine. Finally, the 96DRA-Urine method was used in a label-free, small pilot biomarker discovery analysis for differential urinary proteome analysis of bladder cancer urine. The results showed that urinary proteins could differentiate bladder cancer (BCa) patients from healthy controls and distinguish high-grade BCa from low-grade BCa with area under the curve (AUC) values of 0.972 and 0.847, respectively. Consequently, the 96DRA-Urine method might be a high-throughput method for preparing body fluid samples used in clinical research but needs to be further verified.
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Affiliation(s)
- Xiaoyue Tang
- Core Facility of Instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, China; Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Xiaoping Xiao
- Core Facility of Instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, China; Cytology Lab, Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Peking Union Medical College, Beijing, China
| | - Haidan Sun
- Core Facility of Instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, China
| | - Shuxin Zheng
- Core Facility of Instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, China
| | - Xiaolian Xiao
- Core Facility of Instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, China
| | - Zhengguang Guo
- Core Facility of Instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, China
| | - Xiaoyan Liu
- Core Facility of Instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, China
| | - Wei Sun
- Core Facility of Instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, China.
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6
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Zhao L, Zhang Y, Liu F, Yang H, Zhong Y, Wang Y, Li S, Su Q, Tang L, Bai L, Ren H, Zou Y, Wang S, Zheng S, Xu H, Li L, Zhang J, Chai Z, Cooper ME, Tong N. Urinary complement proteins and risk of end-stage renal disease: quantitative urinary proteomics in patients with type 2 diabetes and biopsy-proven diabetic nephropathy. J Endocrinol Invest 2021; 44:2709-2723. [PMID: 34043214 PMCID: PMC8572220 DOI: 10.1007/s40618-021-01596-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 05/18/2021] [Indexed: 02/05/2023]
Abstract
PURPOSE To investigate the association between urinary complement proteins and renal outcome in biopsy-proven diabetic nephropathy (DN). METHODS Untargeted proteomic and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses and targeted proteomic analysis using parallel reaction-monitoring (PRM)-mass spectrometry was performed to determine the abundance of urinary complement proteins in healthy controls, type 2 diabetes mellitus (T2DM) patients, and patients with T2DM and biopsy-proven DN. The abundance of each urinary complement protein was individually included in Cox proportional hazards models for predicting progression to end-stage renal disease (ESRD). RESULTS Untargeted proteomic and functional analysis using the KEGG showed that differentially expressed urinary proteins were primarily associated with the complement and coagulation cascades. Subsequent urinary complement proteins quantification using PRM showed that urinary abundances of C3, C9, and complement factor H (CFAH) correlated negatively with annual estimated glomerular filtration rate (eGFR) decline, while urinary abundances of C5, decay-accelerating factor (DAF), and CD59 correlated positively with annual rate of eGFR decline. Furthermore, higher urinary abundance of CFAH and lower urinary abundance of DAF were independently associated with greater risk of progression to ESRD. Urinary abundance of CFAH and DAF had a larger area under the curve (AUC) than that of eGFR, proteinuria, or any pathological parameter. Moreover, the model that included CFAH or DAF had a larger AUC than that with only clinical or pathological parameters. CONCLUSION Urinary abundance of complement proteins was significantly associated with ESRD in patients with T2DM and biopsy-proven DN, indicating that therapeutically targeting the complement pathway may alleviate progression of DN.
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Affiliation(s)
- L Zhao
- Division of Nephrology, Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China
- Division of General Practice, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Y Zhang
- Key Laboratory of Transplant Engineering and Immunology, MOH, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China
- West China-Washington Mitochondria and Metabolism Research Center, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China
- Frontiers Science Center for Disease-Related Molecular Network, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China
| | - F Liu
- Division of Nephrology, Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China.
- Division of General Practice, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
- Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
| | - H Yang
- Key Laboratory of Transplant Engineering and Immunology, MOH, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China.
- West China-Washington Mitochondria and Metabolism Research Center, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China.
- Frontiers Science Center for Disease-Related Molecular Network, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China.
| | - Y Zhong
- Key Laboratory of Transplant Engineering and Immunology, MOH, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China
- West China-Washington Mitochondria and Metabolism Research Center, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China
- Frontiers Science Center for Disease-Related Molecular Network, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China
| | - Y Wang
- Division of Nephrology, Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China
- Division of General Practice, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - S Li
- Division of General Practice, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Q Su
- Division of General Practice, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - L Tang
- Histology and Imaging Platform, Core Facility of West China Hospital, Chengdu, Sichuan, China
| | - L Bai
- Histology and Imaging Platform, Core Facility of West China Hospital, Chengdu, Sichuan, China
| | - H Ren
- Division of Nephrology, Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China
- Division of General Practice, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Y Zou
- Division of Nephrology, Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China
- Division of General Practice, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - S Wang
- Key Laboratory of Transplant Engineering and Immunology, MOH, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China
- West China-Washington Mitochondria and Metabolism Research Center, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China
- Frontiers Science Center for Disease-Related Molecular Network, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China
| | - S Zheng
- Key Laboratory of Transplant Engineering and Immunology, MOH, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China
- West China-Washington Mitochondria and Metabolism Research Center, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China
- Frontiers Science Center for Disease-Related Molecular Network, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China
| | - H Xu
- Division of Pathology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - L Li
- Division of Pathology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - J Zhang
- Histology and Imaging Platform, Core Facility of West China Hospital, Chengdu, Sichuan, China
| | - Z Chai
- Department of Diabetes, Central Clinical School, Monash University, Melbourne, Australia
| | - M E Cooper
- Department of Diabetes, Central Clinical School, Monash University, Melbourne, Australia
| | - N Tong
- Division of Endocrinology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
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Shao D, Huang L, Wang Y, Cui X, Li Y, Wang Y, Ma Q, Du W, Cui J. HBFP: a new repository for human body fluid proteome. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6395039. [PMID: 34642750 PMCID: PMC8516408 DOI: 10.1093/database/baab065] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 09/23/2021] [Accepted: 09/28/2021] [Indexed: 12/15/2022]
Abstract
Body fluid proteome has been intensively studied as a primary source for disease
biomarker discovery. Using advanced proteomics technologies, early research
success has resulted in increasingly accumulated proteins detected in different
body fluids, among which many are promising biomarkers. However, despite a
handful of small-scale and specific data resources, current research is clearly
lacking effort compiling published body fluid proteins into a centralized and
sustainable repository that can provide users with systematic analytic tools. In
this study, we developed a new database of human body fluid proteome (HBFP) that
focuses on experimentally validated proteome in 17 types of human body fluids.
The current database archives 11 827 unique proteins reported by 164
scientific publications, with a maximal false discovery rate of 0.01 on both the
peptide and protein levels since 2001, and enables users to query, analyze and
download protein entries with respect to each body fluid. Three unique features
of this new system include the following: (i) the protein annotation page
includes detailed abundance information based on relative qualitative measures
of peptides reported in the original references, (ii) a new score is calculated
on each reported protein to indicate the discovery confidence and (iii) HBFP
catalogs 7354 proteins with at least two non-nested uniquely mapping peptides of
nine amino acids according to the Human Proteome Project Data Interpretation
Guidelines, while the remaining 4473 proteins have more than two unique peptides
without given sequence information. As an important resource for human protein
secretome, we anticipate that this new HBFP database can be a powerful tool that
facilitates research in clinical proteomics and biomarker discovery. Database URL:https://bmbl.bmi.osumc.edu/HBFP/
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Affiliation(s)
- Dan Shao
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, 122E Avery Hall, 1144 T St., Lincoln, NE 68588, USA.,Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, Changchun 130012, China.,Department of Computer Science and Technology, Changchun University, 6543 Weixing Road, Changchun 130022, China
| | - Lan Huang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Yan Wang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Xueteng Cui
- Department of Computer Science and Technology, Changchun University, 6543 Weixing Road, Changchun 130022, China
| | - Yufei Li
- Department of Computer Science and Technology, Changchun University, 6543 Weixing Road, Changchun 130022, China
| | - Yao Wang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, 310G Lincoln tower, 1800 cannon drive, Columbus, OH 43210, USA
| | - Wei Du
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Juan Cui
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, 122E Avery Hall, 1144 T St., Lincoln, NE 68588, USA
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Xiao Q, Zhang F, Xu L, Yue L, Kon OL, Zhu Y, Guo T. High-throughput proteomics and AI for cancer biomarker discovery. Adv Drug Deliv Rev 2021; 176:113844. [PMID: 34182017 DOI: 10.1016/j.addr.2021.113844] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 06/13/2021] [Accepted: 06/15/2021] [Indexed: 02/08/2023]
Abstract
Biomarkers are assayed to assess biological and pathological status. Recent advances in high-throughput proteomic technology provide opportunities for developing next generation biomarkers for clinical practice aided by artificial intelligence (AI) based techniques. We summarize the advances and limitations of cancer biomarkers based on genomic and transcriptomic analysis, as well as classical antibody-based methodologies. Then we review recent progresses in mass spectrometry (MS)-based proteomics in terms of sample preparation, peptide fractionation by liquid chromatography (LC) and mass spectrometric data acquisition. We highlight applications of AI techniques in high-throughput clinical studies as compared with clinical decisions based on singular features. This review sets out our approach for discovering clinical biomarkers in studies using proteomic big data technology conjoined with computational and statistical methods.
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Wu Q, Fenton RA. Urinary proteomics for kidney dysfunction: insights and trends. Expert Rev Proteomics 2021; 18:437-452. [PMID: 34187288 DOI: 10.1080/14789450.2021.1950535] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Introduction: Kidney dysfunction poses a high burden on patients and health care systems. Early detection and accurate prediction of kidney disease progression remains a major challenge. Compared to existing clinical parameters, urinary proteomics has the potential to reveal molecular alterations within the kidney that may alter its function before the onset of clinical symptoms. Thus, urinary proteomics has greater prognostic potential for assessment of kidney dysfunction progression.Areas covered: Advances in urinary proteomics for major causes of kidney dysfunction are discussed. The application of urinary extracellular vesicles for studying kidney dysfunction are discussed. Technological advances in urinary proteomics are discussed. The literature was identified using a database search for titles containing 'proteom*' and 'urin*' and published within the past 5 years. Retrieved literature was manually filtered to retain kidney dysfunctions-related studies.Expert opinion: Despite major advances, diagnosis by urinary proteomics has not been fully applied in any clinical settings. This could be attributed to the complex nature of kidney diseases, in addition to the constraints on study power and feasibility of incorporating mass spectrometry techniques in daily routine analysis. Nevertheless, we are confident that advances in urinary proteomics will soon provide superior insights into kidney disease beyond existing clinical parameters.
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Affiliation(s)
- Qi Wu
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Robert A Fenton
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
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10
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Huang L, Shao D, Wang Y, Cui X, Li Y, Chen Q, Cui J. Human body-fluid proteome: quantitative profiling and computational prediction. Brief Bioinform 2021; 22:315-333. [PMID: 32020158 PMCID: PMC7820883 DOI: 10.1093/bib/bbz160] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 08/22/2019] [Accepted: 10/18/2019] [Indexed: 12/15/2022] Open
Abstract
Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein-protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.
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Affiliation(s)
- Lan Huang
- College of Computer Science and Technology in the Jilin University
| | - Dan Shao
- College of Computer Science and Technology in the Jilin University
- College of Computer Science and Technology in Changchun University
| | - Yan Wang
- College of Computer Science and Technology in the Jilin University
| | - Xueteng Cui
- College of Computer Science and Technology in the Changchun University
| | - Yufei Li
- College of Computer Science and Technology in the Changchun University
| | - Qian Chen
- College of Computer Science and Technology in the Jilin University
| | - Juan Cui
- Department of Computer Science and Engineering in the University of Nebraska-Lincoln
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11
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Yin Z, Du M, Chen D, Zhang W, Huang W, Wu X, Yan S. Rapid structural discrimination of IgG antibodies by multicharge-state collision-induced unfolding. RSC Adv 2021; 11:36502-36510. [PMID: 35494361 PMCID: PMC9043582 DOI: 10.1039/d1ra06486j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 11/06/2021] [Indexed: 11/29/2022] Open
Abstract
Immunoglobulin G (IgG) antibodies are an important class of biotherapeutics that target various diseases, such as cancers, neurodegenerative disorders, and autoimmune diseases, yet rapid discrimination of IgG antibodies remains a great challenge due to heterogeneity, flexibility, and large size. Herein, we demonstrate a simplified multicharge-state collision-induced unfolding (CIU) method for rapid differentiation of four IgG isotypes that differ in terms of the numbers and patterns of disulfide bonds, bypassing tedious single charge-state selection in advance. The results presented herein reveal that gas-phase unfolding behaviors have a strong dependence on charge states outside IgG surfaces; therefore, multicharge-state CIU analysis of IgG subtypes could offer a great opportunity to gain deeper insights into their gas-phase structural differentiation. The full discrimination of IgG antibody isoforms that possess different disulfide bond numbers and even subtle disulfide bonding patterns can be achieved based on their charge-dependent gas-phase unfolding behaviors and root-mean square deviation in CIU difference spectra. Taken together, the incorporation of all charge states observed in a native ion mobility-mass spectrometry (IM-MS) experiment to CIU analysis could make this strategy sensitive to more subtle structural discrepancies, facilitating the rapid discrimination and evaluation of innovative structurally similar biotherapeutic candidates with unexplored functions. A simplified multicharge-state collision-induced unfolding (CIU) method was proposed for rapid differentiation of IgG isotypes that differ in terms of the numbers and patterns of disulfide bonds.![]()
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Affiliation(s)
- Zhibin Yin
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Mingyi Du
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Key Laboratory of Natural Pesticide and Chemical Biology of the Ministry of Education, South China Agricultural University, Guangzhou, 510642, China
| | - Dong Chen
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Key Laboratory of Natural Pesticide and Chemical Biology of the Ministry of Education, South China Agricultural University, Guangzhou, 510642, China
| | - Wenyang Zhang
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Wenjie Huang
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Xinzhou Wu
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Key Laboratory of Natural Pesticide and Chemical Biology of the Ministry of Education, South China Agricultural University, Guangzhou, 510642, China
| | - Shijuan Yan
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
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12
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Clark DJ, Zhang H. Proteomic approaches for characterizing renal cell carcinoma. Clin Proteomics 2020; 17:28. [PMID: 32742246 PMCID: PMC7391522 DOI: 10.1186/s12014-020-09291-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 05/15/2020] [Indexed: 12/24/2022] Open
Abstract
Renal cell carcinoma is among the top 15 most commonly diagnosed cancers worldwide, comprising multiple sub-histologies with distinct genomic, proteomic, and clinicopathological features. Proteomic methodologies enable the detection and quantitation of protein profiles associated with the disease state and have been explored to delineate the dysregulated cellular processes associated with renal cell carcinoma. In this review we highlight the reports that employed proteomic technologies to characterize tissue, blood, and urine samples obtained from renal cell carcinoma patients. We describe the proteomic approaches utilized and relate the results of studies in the larger context of renal cell carcinoma biology. Moreover, we discuss some unmet clinical needs and how emerging proteomic approaches can seek to address them. There has been significant progress to characterize the molecular features of renal cell carcinoma; however, despite the large-scale studies that have characterized the genomic and transcriptomic profiles, curative treatments are still elusive. Proteomics facilitates a direct evaluation of the functional modules that drive pathobiology, and the resulting protein profiles would have applications in diagnostics, patient stratification, and identification of novel therapeutic interventions.
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Affiliation(s)
- David J. Clark
- Department of Pathology, The Johns Hopkins University, Baltimore, MD 21231 USA
| | - Hui Zhang
- Department of Pathology, The Johns Hopkins University, Baltimore, MD 21231 USA
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13
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Huang P, Kong Q, Gao W, Chu B, Li H, Mao Y, Cai Z, Xu R, Tian R. Spatial proteome profiling by immunohistochemistry-based laser capture microdissection and data-independent acquisition proteomics. Anal Chim Acta 2020; 1127:140-148. [PMID: 32800117 DOI: 10.1016/j.aca.2020.06.049] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 06/18/2020] [Accepted: 06/20/2020] [Indexed: 12/11/2022]
Abstract
Understanding the tumor heterogeneity through spatially resolved proteome profiling is important for biomedical research and clinical application. Laser capture microdissection (LCM) is a powerful technology for exploring local cell populations without losing spatial information. Conventionally, tissue sections are stained with hematoxylin and eosin (H&E) for cell-type identification before LCM. However, it generally requires experienced pathologists to distinguish different cell types, which limits the application of LCM to broad cancer research field. Here, we designed an immunohistochemistry (IHC)-based workflow for cell type-resolved proteome analysis of tissue samples. Firstly, targeted cell type was marked by IHC using antibody targeting cell-type specific marker to improve accuracy and efficiency of LCM. Secondly, to increase protein recovery from chemically crosslinked IHC tissues, we optimized a decrosslinking procedure to seamlessly combine with the integrated spintip-based sample preparation technology SISPROT. This newly developed approach, termed IHC-SISPROT, has comparable performance as H&E staining-based proteomic analysis. High sensitivity and reproducibility of IHC-SISPROT were achieved by combining with data independent acquisition proteomics. More than 3500 proteins were identified from only 0.2 mm2 and 12 μm thickness of hepatocellular carcinoma (HCC) tissue section. Furthermore, using 5 mm2 and 12 μm thickness of HCC tissue section, 6660 and 6052 protein groups were quantified from cancer cells and cancer-associated fibroblasts (CAFs) by the IHC-SISPROT workflow. Bioinformatic analysis revealed the enrichment of cell type-specific ligands and receptors and potentially new communications between cancer cells and CAFs by these signaling proteins. Therefore, IHC-SISPROT is a sensitive and accurate proteomic approach for spatial profiling of cell type-specific proteome from tissues.
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Affiliation(s)
- Peiwu Huang
- Department of Chemistry, Southern University of Science and Technology, Shenzhen, 518055, China; State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, SAR, China
| | - Qian Kong
- Department of Chemistry, Southern University of Science and Technology, Shenzhen, 518055, China; State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, SAR, China
| | - Weina Gao
- Department of Chemistry, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Bizhu Chu
- Department of Chemistry, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Hua Li
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, SAR, China; SUSTech Core Research Facilities, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Yiheng Mao
- Department of Chemistry, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, SAR, China
| | - Ruilian Xu
- Shenzhen People's Hospital, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, 518020, China
| | - Ruijun Tian
- Department of Chemistry, Southern University of Science and Technology, Shenzhen, 518055, China; Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen, 518055, China.
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14
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Na Nakorn P, Pannengpetch S, Isarankura-Na-Ayudhya P, Thippakorn C, Lawung R, Sathirapongsasuti N, Kitiyakara C, Sritara P, Vathesatogkit P, Isarankura-Na-Ayudhya C. Roles of kininogen-1, basement membrane specific heparan sulfate proteoglycan core protein, and roundabout homolog 4 as potential urinary protein biomarkers in diabetic nephropathy. EXCLI JOURNAL 2020; 19:872-891. [PMID: 32665774 PMCID: PMC7355151 DOI: 10.17179/excli2020-1396] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 06/15/2020] [Indexed: 12/24/2022]
Abstract
Diabetic nephropathy, a major complication of diabetes mellitus (DM), is increasing worldwide and the large majority of patients have type 2 DM. Microalbuminuria has been used as a diagnostic marker of diabetic nephropathy. But owing to its insufficient sensitivity and specificity, other biomarkers are being sought. In addition, the pathophysiology of diabetic nephropathy is not fully understood and declines in renal function occur even without microalbuminuria. In this study, we investigated urinary proteins from three study groups (controls, and type 2 diabetic subjects with or without microalbuminuria). Non-targeted label-free Nano-LC QTOF analysis was conducted to discover underlying mechanisms and protein networks, and targeted label-free Nano-LC QTOF with SWATH was performed to qualify discovered protein candidates. Twenty-eight proteins were identified as candidates and functionally analyzed via String DB, gene ontology and pathway analysis. Four predictive mechanisms were analyzed: i) response to stimulus, ii) platelet activation, signaling and aggregation, iii) ECM-receptor interaction, and iv) angiogenesis. These mechanisms can provoke kidney dysfunction in type 2 diabetic patients via endothelial cell damage and glomerulus structural alteration. Based on these analyses, three proteins (kininogen-1, basement membrane-specific heparan sulfate proteoglycan core protein, and roundabout homolog 4) were proposed for further study as potential biomarkers. Our findings provide insights that may improve methods for both prevention and diagnosis of diabetic nephropathy.
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Affiliation(s)
- Piyada Na Nakorn
- Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
| | - Supitcha Pannengpetch
- Center for Research and Innovation, Faculty of Medical Technology, Mahidol University, Nakornpathom, Thailand
| | | | - Chadinee Thippakorn
- Center for Research and Innovation, Faculty of Medical Technology, Mahidol University, Nakornpathom, Thailand
| | - Ratana Lawung
- Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
| | - Nuankanya Sathirapongsasuti
- Section for Translational Medicine, Research Center, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Chagriya Kitiyakara
- Department of Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Piyamitr Sritara
- Department of Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Prin Vathesatogkit
- Department of Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
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15
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Oros D, Ceprnja M, Zucko J, Cindric M, Hozic A, Skrlin J, Barisic K, Melvan E, Uroic K, Kos B, Starcevic A. Identification of pathogens from native urine samples by MALDI-TOF/TOF tandem mass spectrometry. Clin Proteomics 2020; 17:25. [PMID: 32581661 PMCID: PMC7310424 DOI: 10.1186/s12014-020-09289-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 06/17/2020] [Indexed: 12/11/2022] Open
Abstract
Background Reliable high-throughput microbial pathogen identification in human urine samples is crucial for patients with cystitis symptoms. Currently employed methods are time-consuming and could lead to unnecessary or inadequate antibiotic treatment. Purpose of this study was to assess the potential of mass spectrometry for uropathogen identification from a native urine sample. Methods In total, 16 urine samples having more than 105 CFU/mL were collected from clinical outpatients. These samples were analysed using standard urine culture methods, followed by 16S rRNA gene sequencing serving as control and here described culture-independent MALDI-TOF/TOF MS method being tested. Results Here we present advantages and disadvantages of bottom-up proteomics, using MALDI-TOF/TOF tandem mass spectrometry, for culture-independent identification of uropathogens (e.g. directly from urine samples). The direct approach provided reliable identification of bacteria at the genus level in monobacterial samples. Taxonomic identifications obtained by proteomics were compared both to standard urine culture test used in clinics and genomic test based on 16S rRNA sequencing. Conclusions Our findings indicate that mass spectrometry has great potential as a reliable high-throughput tool for microbial pathogen identification in human urine samples. In this case, the MALDI-TOF/TOF, was used as an analytical tool for the determination of bacteria in urine samples, and the results obtained emphasize high importance of storage conditions and sample preparation method impacting reliability of MS2 data analysis. The proposed method is simple enough to be utilized in existing clinical settings and is highly suitable for suspected single organism infectious etiologies. Further research is required in order to identify pathogens in polymicrobial urine samples.
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Affiliation(s)
- Damir Oros
- Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, Zagreb University, 10000 Zagreb, Croatia
| | - Marina Ceprnja
- Biochemical Laboratory, Special Hospital Agram, Polyclinic Zagreb, 10000 Zagreb, Croatia
| | - Jurica Zucko
- Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, Zagreb University, 10000 Zagreb, Croatia
| | - Mario Cindric
- Division of Molecular Medicine, Ruder Boskovic Institute, Zagreb, Croatia
| | - Amela Hozic
- Division of Molecular Medicine, Ruder Boskovic Institute, Zagreb, Croatia
| | - Jasenka Skrlin
- Department for Clinical Microbiology and Hospital Infection, University Hospital Dubrava, 10000 Zagreb, Croatia
| | - Karmela Barisic
- Faculty of Pharmacy and Biochemistry, Zagreb University, Zagreb, Croatia
| | - Ena Melvan
- Department of Biological Science, Faculty of Science, Macquarie University, Sydney, Australia
| | - Ksenija Uroic
- Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, Zagreb University, 10000 Zagreb, Croatia
| | - Blazenka Kos
- Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, Zagreb University, 10000 Zagreb, Croatia
| | - Antonio Starcevic
- Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, Zagreb University, 10000 Zagreb, Croatia
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Lin L, Zheng J, Zheng F, Cai Z, Yu Q. Advancing serum peptidomic profiling by data-independent acquisition for clear-cell renal cell carcinoma detection and biomarker discovery. J Proteomics 2020; 215:103671. [DOI: 10.1016/j.jprot.2020.103671] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 12/28/2019] [Accepted: 01/26/2020] [Indexed: 12/20/2022]
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LI WF, YAN DW, JIN Y, LI HY, MA M, WU ZZ. Application of Mass Spectrometry in Analysis of Non-Enzymatic Glycation Proteins in Diabetic Blood. CHINESE JOURNAL OF ANALYTICAL CHEMISTRY 2019. [DOI: 10.1016/s1872-2040(19)61197-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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