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Li G, Stampas A, Komatsu Y, Gao X, Huard J, Pan S. Proteomics in orthopedic research: Recent studies and their translational implications. J Orthop Res 2024; 42:1631-1640. [PMID: 38897819 DOI: 10.1002/jor.25917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 05/10/2024] [Accepted: 06/01/2024] [Indexed: 06/21/2024]
Abstract
Proteomics is a growing field that offers insights into various aspects of disease processes and therapy responses. Within the field of orthopedics, there are a variety of diseases that have a poor prognosis due to a lack of targeted curative therapy or disease modifying therapy. Other diseases have been difficult to manage in part due to lack of clinical biomarkers that offer meaningful insight into disease progression or severity. As an emerging technology, proteomics has been increasingly applied in studying bone biology and an assortment of orthopedics related diseases, such as osteoarthritis, osteosarcoma and bone tumors, osteoporosis, traumatic bone injury, spinal cord injury, hip and knee arthroplasty, and fragile healing. These efforts range from mechanistic studies for elucidating novel insights in tissue activity and metabolism to identification of candidate biomarkers for diagnosis, prognosis, and targeted treatment. The knowledge gained from these proteomic and functional studies has provided unique perspectives in studying orthopedic diseases. In this review, we seek to report on the current state of the proteomic study in the field of orthopedics, overview the advances in clinically applicable discoveries, and discuss the opportunities that may guide us for future research.
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Affiliation(s)
- George Li
- School of Medicine, Texas A&M University, Bryan, Texas, USA
| | - Argyrios Stampas
- Department of Physical Medicine and Rehabilitation, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA
- Department of Physical Medicine and Rehabilitation, TIRR Memorial Hermann Hospital, Houston, Texas, USA
| | - Yoshihiro Komatsu
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA
- Graduate Program in Genetics & Epigenetics, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA
| | - Xueqin Gao
- Linda and Mitch Hart Center for Regenerative and Personalized Medicine, Steadman Philippon Research Institute, Vail, Colorado, USA
| | - Johnny Huard
- Linda and Mitch Hart Center for Regenerative and Personalized Medicine, Steadman Philippon Research Institute, Vail, Colorado, USA
| | - Sheng Pan
- Graduate Program in Genetics & Epigenetics, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA
- The Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, Texas, USA
- Department of Integrative Biology and Pharmacology, University of Texas Health Science Center at Houston, Houston, Texas, USA
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2
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Jiang Y, Meyer JG. Rapid Plasma Proteome Profiling via Nanoparticle Protein Corona and Direct Infusion Mass Spectrometry. J Proteome Res 2024. [PMID: 39007500 DOI: 10.1021/acs.jproteome.4c00302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Noninvasive detection of protein biomarkers in plasma is crucial for clinical purposes. Liquid chromatography-mass spectrometry (LC-MS) is the gold standard technique for plasma proteome analysis, but despite recent advances, it remains limited by throughput, cost, and coverage. Here, we introduce a new hybrid method that integrates direct infusion shotgun proteome analysis (DISPA) with nanoparticle (NP) protein corona enrichment for high-throughput and efficient plasma proteomic profiling. We realized over 280 protein identifications in 1.4 min collection time, which enables a potential throughput of approximately 1000 samples daily. The identified proteins are involved in valuable pathways, and 44 of the proteins are FDA-approved biomarkers. The robustness and quantitative accuracy of this method were evaluated across multiple NPs and concentrations with a mean coefficient of variation of 17%. Moreover, different protein corona profiles were observed among various NPs based on their distinct surface modifications, and all NP protein profiles exhibited deeper coverage and better quantification than neat plasma. Our streamlined workflow merges coverage and throughput with precise quantification, leveraging both DISPA and NP protein corona enrichment. This underscores the significant potential of DISPA when paired with NP sample preparation techniques for plasma proteome studies.
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Affiliation(s)
- Yuming Jiang
- Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
| | - Jesse G Meyer
- Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
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3
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Karpov OA, Stotland A, Raedschelders K, Chazarin B, Ai L, Murray CI, Van Eyk JE. Proteomics of the heart. Physiol Rev 2024; 104:931-982. [PMID: 38300522 DOI: 10.1152/physrev.00026.2023] [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: 07/03/2023] [Revised: 12/25/2023] [Accepted: 01/14/2024] [Indexed: 02/02/2024] Open
Abstract
Mass spectrometry-based proteomics is a sophisticated identification tool specializing in portraying protein dynamics at a molecular level. Proteomics provides biologists with a snapshot of context-dependent protein and proteoform expression, structural conformations, dynamic turnover, and protein-protein interactions. Cardiac proteomics can offer a broader and deeper understanding of the molecular mechanisms that underscore cardiovascular disease, and it is foundational to the development of future therapeutic interventions. This review encapsulates the evolution, current technologies, and future perspectives of proteomic-based mass spectrometry as it applies to the study of the heart. Key technological advancements have allowed researchers to study proteomes at a single-cell level and employ robot-assisted automation systems for enhanced sample preparation techniques, and the increase in fidelity of the mass spectrometers has allowed for the unambiguous identification of numerous dynamic posttranslational modifications. Animal models of cardiovascular disease, ranging from early animal experiments to current sophisticated models of heart failure with preserved ejection fraction, have provided the tools to study a challenging organ in the laboratory. Further technological development will pave the way for the implementation of proteomics even closer within the clinical setting, allowing not only scientists but also patients to benefit from an understanding of protein interplay as it relates to cardiac disease physiology.
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Affiliation(s)
- Oleg A Karpov
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Aleksandr Stotland
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Koen Raedschelders
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Blandine Chazarin
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Lizhuo Ai
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Christopher I Murray
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Jennifer E Van Eyk
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
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4
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Wang S, Collins A, Prakash A, Fexova S, Papatheodorou I, Jones AR, Vizcaíno JA. Integrated Proteomics Analysis of Baseline Protein Expression in Pig Tissues. J Proteome Res 2024; 23:1948-1959. [PMID: 38717300 PMCID: PMC11165573 DOI: 10.1021/acs.jproteome.3c00741] [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: 11/06/2023] [Revised: 02/16/2024] [Accepted: 04/18/2024] [Indexed: 06/13/2024]
Abstract
The availability of an increasingly large amount of public proteomics data sets presents an opportunity for performing combined analyses to generate comprehensive organism-wide protein expression maps across different organisms and biological conditions. Sus scrofa, a domestic pig, is a model organism relevant for food production and for human biomedical research. Here, we reanalyzed 14 public proteomics data sets from the PRIDE database coming from pig tissues to assess baseline (without any biological perturbation) protein abundance in 14 organs, encompassing a total of 20 healthy tissues from 128 samples. The analysis involved the quantification of protein abundance in 599 mass spectrometry runs. We compared protein expression patterns among different pig organs and examined the distribution of proteins across these organs. Then, we studied how protein abundances were compared across different data sets and studied the tissue specificity of the detected proteins. Of particular interest, we conducted a comparative analysis of protein expression between pig and human tissues, revealing a high degree of correlation in protein expression among orthologs, particularly in brain, kidney, heart, and liver samples. We have integrated the protein expression results into the Expression Atlas resource for easy access and visualization of the protein expression data individually or alongside gene expression data.
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Affiliation(s)
- Shengbo Wang
- European
Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Andrew Collins
- Institute
of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Ananth Prakash
- European
Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
- Open
Targets, Wellcome Genome
Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Silvie Fexova
- European
Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Irene Papatheodorou
- European
Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
- Open
Targets, Wellcome Genome
Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Andrew R. Jones
- Institute
of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Juan Antonio Vizcaíno
- European
Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
- Open
Targets, Wellcome Genome
Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
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5
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Fiala J, Schuster D, Ollivier S, Pengelley S, Lubeck M, Busch F, Jankevics A, Raether O, Greisch JF, Heck AJR. Protein-Centric Analysis of Personalized Antibody Repertoires Using LC-MS-Based Fab-Profiling on a timsTOF. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024; 35:1292-1300. [PMID: 38662593 PMCID: PMC11157643 DOI: 10.1021/jasms.4c00076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/15/2024] [Accepted: 04/17/2024] [Indexed: 06/06/2024]
Abstract
Endogenous antibodies, or immunoglobulins (Igs), abundantly present in body fluids, represent some of the most challenging samples to analyze, largely due to the immense variability in their sequences and concentrations. It has been estimated that our body can produce billions of different Ig proteins with different isotypes, making their individual analysis seemingly impossible. However, recent advances in protein-centric proteomics using LC-MS coupled to Orbitrap mass analyzers to profile intact Fab fragments formed by selective cleavage at the IgG-hinge revealed that IgG repertoires may be less diverse, albeit unique for each donor. Serum repertoires seem to be dominated by a few hundred clones that cumulatively make up 50-95% of the total IgG content. Enabling such analyses required careful optimization of the chromatography and mass analysis, as all Fab analytes are highly alike in mass (46-51 kDa) and sequence. To extend the opportunities of this mass-spectrometry-based profiling of antibody repertoires, we here report the optimization and evaluation of an alternative MS platform, namely, the timsTOF, for antibody repertoire profiling. The timsTOF mass analyzer has gained traction in recent years for peptide-centric proteomics and found wide applicability in plasma proteomics, affinity proteomics, and HLA peptidomics, to name a few. However, for protein-centric analysis, this platform has been less explored. Here, we demonstrate that the timsTOF platform can be adapted to perform protein-centric LC-MS-based profiling of antibody repertoires. In a side-by-side comparison of the timsTOF and the Orbitrap we demonstrate that the extracted serum antibody repertoires are alike qualitatively and quantitatively, whereby in particular the sensitivity of the timsTOF platform excels. Future incorporation of advanced top-down capabilities on the timsTOF may make this platform a very valuable alternative for protein-centric proteomics and top-down proteomics and thus also for personalized antibody repertoire profiling.
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Affiliation(s)
- Jan Fiala
- Biomolecular
Mass Spectrometry & Proteomics, Bijvoet Center for Biomolecular
Research & Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584
CH Utrecht, The
Netherlands
- Netherlands
Proteomics Center, Padualaan
8, 3584 CH Utrecht, The Netherlands
| | - Dina Schuster
- Biomolecular
Mass Spectrometry & Proteomics, Bijvoet Center for Biomolecular
Research & Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584
CH Utrecht, The
Netherlands
- Netherlands
Proteomics Center, Padualaan
8, 3584 CH Utrecht, The Netherlands
| | - Simon Ollivier
- Biomolecular
Mass Spectrometry & Proteomics, Bijvoet Center for Biomolecular
Research & Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584
CH Utrecht, The
Netherlands
- Netherlands
Proteomics Center, Padualaan
8, 3584 CH Utrecht, The Netherlands
| | - Stuart Pengelley
- Bruker
Daltonics GmbH & Co. KG, Fahrenheitstrasse 4, 28359 Bremen, Germany
| | - Markus Lubeck
- Bruker
Daltonics GmbH & Co. KG, Fahrenheitstrasse 4, 28359 Bremen, Germany
| | - Florian Busch
- Bruker
Switzerland AG, 8117 Fällanden, Zurich Switzerland
| | - Andris Jankevics
- Biomolecular
Mass Spectrometry & Proteomics, Bijvoet Center for Biomolecular
Research & Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584
CH Utrecht, The
Netherlands
- Netherlands
Proteomics Center, Padualaan
8, 3584 CH Utrecht, The Netherlands
| | - Oliver Raether
- Bruker
Daltonics GmbH & Co. KG, Fahrenheitstrasse 4, 28359 Bremen, Germany
| | | | - Albert J. R. Heck
- Biomolecular
Mass Spectrometry & Proteomics, Bijvoet Center for Biomolecular
Research & Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584
CH Utrecht, The
Netherlands
- Netherlands
Proteomics Center, Padualaan
8, 3584 CH Utrecht, The Netherlands
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6
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Yasar S, Yagin FH, Melekoglu R, Ardigò LP. Integrating proteomics and explainable artificial intelligence: a comprehensive analysis of protein biomarkers for endometrial cancer diagnosis and prognosis. Front Mol Biosci 2024; 11:1389325. [PMID: 38894711 PMCID: PMC11184912 DOI: 10.3389/fmolb.2024.1389325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 05/16/2024] [Indexed: 06/21/2024] Open
Abstract
Endometrial cancer, which is the most common gynaecological cancer in women after breast, colorectal and lung cancer, can be diagnosed at an early stage. The first aim of this study is to classify age, tumor grade, myometrial invasion and tumor size, which play an important role in the diagnosis and prognosis of endometrial cancer, with machine learning methods combined with explainable artificial intelligence. 20 endometrial cancer patients proteomic data obtained from tumor biopsies taken from different regions of EC tissue were used. The data obtained were then classified according to age, tumor size, tumor grade and myometrial invasion. Then, by using three different machine learning methods, explainable artificial intelligence was applied to the model that best classifies these groups and possible protein biomarkers that can be used in endometrial prognosis were evaluated. The optimal model for age classification was XGBoost with AUC (98.8%), for tumor grade classification was XGBoost with AUC (98.6%), for myometrial invasion classification was LightGBM with AUC (95.1%), and finally for tumor size classification was XGBoost with AUC (94.8%). By combining the optimal models and the SHAP approach, possible protein biomarkers and their expressions were obtained for classification. Finally, EWRS1 protein was found to be common in three groups (age, myometrial invasion, tumor size). This article's findings indicate that models have been developed that can accurately classify factors including age, tumor grade, and myometrial invasion all of which are critical for determining the prognosis of endometrial cancer as well as potential protein biomarkers associated with these factors. Furthermore, we were able to provide an analysis of how the quantities of the proteins suggested as biomarkers varied throughout the classes by combining the SHAP values with these ideal models.
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Affiliation(s)
- Seyma Yasar
- Department of Biostatistics, and Medical Informatics, Medicine Faculty, Inonu University, Malatya, Türkiye
| | - Fatma Hilal Yagin
- Department of Biostatistics, and Medical Informatics, Medicine Faculty, Inonu University, Malatya, Türkiye
| | - Rauf Melekoglu
- Department of Obstetrics and Gynecology, Faculty of Medicine, Inonu University, Malatya, Türkiye
| | - Luca Paolo Ardigò
- Department of Teacher Education, NLA University College, Oslo, Norway
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7
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Stillger MN, Li MJ, Hönscheid P, von Neubeck C, Föll MC. Advancing rare cancer research by MALDI mass spectrometry imaging: Applications, challenges, and future perspectives in sarcoma. Proteomics 2024; 24:e2300001. [PMID: 38402423 DOI: 10.1002/pmic.202300001] [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: 06/08/2023] [Revised: 02/10/2024] [Accepted: 02/12/2024] [Indexed: 02/26/2024]
Abstract
MALDI mass spectrometry imaging (MALDI imaging) uniquely advances cancer research, by measuring spatial distribution of endogenous and exogenous molecules directly from tissue sections. These molecular maps provide valuable insights into basic and translational cancer research, including tumor biology, tumor microenvironment, biomarker identification, drug treatment, and patient stratification. Despite its advantages, MALDI imaging is underutilized in studying rare cancers. Sarcomas, a group of malignant mesenchymal tumors, pose unique challenges in medical research due to their complex heterogeneity and low incidence, resulting in understudied subtypes with suboptimal management and outcomes. In this review, we explore the applicability of MALDI imaging in sarcoma research, showcasing its value in understanding this highly heterogeneous and challenging rare cancer. We summarize all MALDI imaging studies in sarcoma to date, highlight their impact on key research fields, including molecular signatures, cancer heterogeneity, and drug studies. We address specific challenges encountered when employing MALDI imaging for sarcomas, and propose solutions, such as using formalin-fixed paraffin-embedded tissues, and multiplexed experiments, and considerations for multi-site studies and digital data sharing practices. Through this review, we aim to spark collaboration between MALDI imaging researchers and clinical colleagues, to deploy the unique capabilities of MALDI imaging in the context of sarcoma.
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Affiliation(s)
- Maren Nicole Stillger
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Center, Freiburg, Germany
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Mujia Jenny Li
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Center, Freiburg, Germany
- Institute for Pharmaceutical Sciences, University of Freiburg, Freiburg, Germany
| | - Pia Hönscheid
- Institute of Pathology, Faculty of Medicine, University Hospital Carl Gustav Carus at the Technische Universität Dresden, Dresden, Germany
- National Center for Tumor Diseases, Partner Site Dresden, German Cancer Research Center Heidelberg, Dresden, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Cläre von Neubeck
- Department of Particle Therapy, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Melanie Christine Föll
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Center, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
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8
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Munro V, Kelly V, Messner CB, Kustatscher G. Cellular control of protein levels: A systems biology perspective. Proteomics 2024; 24:e2200220. [PMID: 38012370 DOI: 10.1002/pmic.202200220] [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: 07/23/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 11/29/2023]
Abstract
How cells regulate protein levels is a central question of biology. Over the past decades, molecular biology research has provided profound insights into the mechanisms and the molecular machinery governing each step of the gene expression process, from transcription to protein degradation. Recent advances in transcriptomics and proteomics have complemented our understanding of these fundamental cellular processes with a quantitative, systems-level perspective. Multi-omic studies revealed significant quantitative, kinetic and functional differences between the genome, transcriptome and proteome. While protein levels often correlate with mRNA levels, quantitative investigations have demonstrated a substantial impact of translation and protein degradation on protein expression control. In addition, protein-level regulation appears to play a crucial role in buffering protein abundances against undesirable mRNA expression variation. These findings have practical implications for many fields, including gene function prediction and precision medicine.
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Affiliation(s)
- Victoria Munro
- Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, UK
| | - Van Kelly
- Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, UK
| | - Christoph B Messner
- Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Georg Kustatscher
- Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, UK
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9
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D'Ermo G, Audebert S, Camoin L, Planer-Friedrich B, Casiot-Marouani C, Delpoux S, Lebrun R, Guiral M, Schoepp-Cothenet B. Quantitative proteomics reveals the Sox system's role in sulphur and arsenic metabolism of phototroph Halorhodospira halophila. Environ Microbiol 2024; 26:e16655. [PMID: 38897608 DOI: 10.1111/1462-2920.16655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 05/07/2024] [Indexed: 06/21/2024]
Abstract
The metabolic process of purple sulphur bacteria's anoxygenic photosynthesis has been primarily studied in Allochromatium vinosum, a member of the Chromatiaceae family. However, the metabolic processes of purple sulphur bacteria from the Ectothiorhodospiraceae and Halorhodospiraceae families remain unexplored. We have analysed the proteome of Halorhodospira halophila, a member of the Halorhodospiraceae family, which was cultivated with various sulphur compounds. This analysis allowed us to reconstruct the first comprehensive sulphur-oxidative photosynthetic network for this family. Some members of the Ectothiorhodospiraceae family have been shown to use arsenite as a photosynthetic electron donor. Therefore, we analysed the proteome response of Halorhodospira halophila when grown under arsenite and sulphide conditions. Our analyses using ion chromatography-inductively coupled plasma mass spectrometry showed that thioarsenates are chemically formed under these conditions. However, they are more extensively generated and converted in the presence of bacteria, suggesting a biological process. Our quantitative proteomics revealed that the SoxAXYZB system, typically dedicated to thiosulphate oxidation, is overproduced under these growth conditions. Additionally, two electron carriers, cytochrome c551/c5 and HiPIP III, are also overproduced. Electron paramagnetic resonance spectroscopy suggested that these transporters participate in the reduction of the photosynthetic Reaction Centre. These results support the idea of a chemically and biologically formed thioarsenate being oxidized by the Sox system, with cytochrome c551/c5 and HiPIP III directing electrons towards the Reaction Centre.
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Affiliation(s)
- Giulia D'Ermo
- Aix-Marseille Université, CNRS, BIP-UMR 7281, Marseille, France
| | - Stéphane Audebert
- Aix-Marseille Université, Inserm, CNRS, Institut Paoli-Calmettes, CRCM, Marseille Protéomique, Marseille, France
| | - Luc Camoin
- Aix-Marseille Université, Inserm, CNRS, Institut Paoli-Calmettes, CRCM, Marseille Protéomique, Marseille, France
| | - Britta Planer-Friedrich
- Environmental Geochemistry, Bayreuth Centre for Ecology and Environmental Research (BAYCEER), University of Bayreuth, Bayreuth, Germany
| | | | - Sophie Delpoux
- Laboratoire HydroSciences Montpellier, Univ. Montpellier, CNRS, IRD, Montpellier, France
| | - Régine Lebrun
- Aix-Marseille Université, CNRS, IMM-FR3479, Marseille Protéomique, Marseille, France
| | - Marianne Guiral
- Aix-Marseille Université, CNRS, BIP-UMR 7281, Marseille, France
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10
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Teyssonnière EM, Trébulle P, Muenzner J, Loegler V, Ludwig D, Amari F, Mülleder M, Friedrich A, Hou J, Ralser M, Schacherer J. Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast. Proc Natl Acad Sci U S A 2024; 121:e2319211121. [PMID: 38696467 PMCID: PMC11087752 DOI: 10.1073/pnas.2319211121] [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: 11/02/2023] [Accepted: 03/25/2024] [Indexed: 05/04/2024] Open
Abstract
Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein coexpression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.
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Affiliation(s)
- Elie Marcel Teyssonnière
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Pauline Trébulle
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7BN, United Kingdom
| | - Julia Muenzner
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Victor Loegler
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Daniela Ludwig
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
- Core Facility High-Throughput Mass Spectrometry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Fatma Amari
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
- Core Facility High-Throughput Mass Spectrometry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Michael Mülleder
- Core Facility High-Throughput Mass Spectrometry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Anne Friedrich
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Jing Hou
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Markus Ralser
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7BN, United Kingdom
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
- Max Planck Institute for Molecular Genetics, Berlin14195, Germany
| | - Joseph Schacherer
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
- Institut Universitaire de France, Paris75000, France
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11
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Babur O, Emili A, Aslan JE. Platelet proteomics emerges from the womb: mass spectrometry insights into neonatal platelet biology. J Thromb Haemost 2024; 22:1313-1315. [PMID: 38670684 DOI: 10.1016/j.jtha.2024.01.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 01/27/2024] [Indexed: 04/28/2024]
Affiliation(s)
- Ozgun Babur
- Department of Computer Science, University of Massachusetts, Boston, Massachusetts, USA
| | - Andrew Emili
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA
| | - Joseph E Aslan
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA; Knight Cardiovascular Institute, Oregon Health & Science University, Portland, Oregon, USA; Department of Chemical Physiology and Biochemistry, Oregon Health & Science University, Portland, Oregon, USA.
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Bourganou MV, Chatzopoulos DC, Lianou DT, Tsangaris GT, Fthenakis GC, Katsafadou AI. Scientometrics Evaluation of Published Scientific Papers on the Use of Proteomics Technologies in Mastitis Research in Ruminants. Pathogens 2024; 13:324. [PMID: 38668279 PMCID: PMC11053840 DOI: 10.3390/pathogens13040324] [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: 03/06/2024] [Revised: 03/30/2024] [Accepted: 04/10/2024] [Indexed: 04/29/2024] Open
Abstract
The objective of this study was the presentation of quantitative characteristics regarding the scientific content and bibliometric details of the relevant publications. In total, 156 papers were considered. Most papers presented original studies (n = 135), and fewer were reviews (n = 21). Most original articles (n = 101) referred to work involving cattle. Most original articles described work related to the diagnosis (n = 72) or pathogenesis (n = 62) of mastitis. Most original articles included field work (n = 75), whilst fewer included experimental (n = 31) or laboratory (n = 30) work. The tissue assessed most frequently in the studies was milk (n = 59). Milk was assessed more frequently in studies on the diagnosis (61.1% of relevant studies) or pathogenesis (30.6%) of the infection, but mammary tissue was assessed more frequently in studies on the treatment (31.0%). In total, 47 pathogens were included in the studies described; most were Gram-positive bacteria (n = 34). The three bacteria most frequently included in the studies were Staphylococcus aureus (n = 55 articles), Escherichia coli (n = 31) and Streptococcus uberis (n = 19). The proteomics technology employed more often in the respective studies was liquid chromatography-tandem mass spectrometry (LC-MS/MS), either on its own (n = 56) or in combination with other technologies (n = 40). The median year of publication of articles involving bioinformatics or LC-MS/MS and bioinformatics was the most recent: 2022. The 156 papers were published in 78 different journals, most frequently in the Journal of Proteomics (n = 16 papers) and the Journal of Dairy Science (n = 12). The median number of cited references in the papers was 48. In the papers, there were 1143 co-authors (mean: 7.3 ± 0.3 co-authors per paper, median: 7, min.-max.: 1-19) and 742 individual authors. Among them, 15 authors had published at least seven papers (max.: 10). Further, there were 218 individual authors who were the first or last authors in the papers. Most papers were submitted for open access (n = 79). The median number of citations received by the 156 papers was 12 (min.-max.: 0-339), and the median yearly number of citations was 2.0 (min.-max.: 0.0-29.5). The h-index of the papers was 33, and the m-index was 2. The increased number of cited references in papers and international collaboration in the respective study were the variables associated with most citations to published papers. This is the first ever scientometrics evaluation of proteomics studies, the results of which highlighted the characteristics of published papers on mastitis and proteomics. The use of proteomics in mastitis research has focused on the elucidation of pathogenesis and diagnosis of the infection; LC-MS/MS has been established as the most frequently used proteomics technology, although the use of bioinformatics has also emerged recently as a useful tool.
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Affiliation(s)
- Maria V. Bourganou
- Faculty of Public and One Health, University of Thessaly, 43100 Karditsa, Greece; (M.V.B.); (D.C.C.)
| | - Dimitris C. Chatzopoulos
- Faculty of Public and One Health, University of Thessaly, 43100 Karditsa, Greece; (M.V.B.); (D.C.C.)
| | - Daphne T. Lianou
- Veterinary Faculty, University of Thessaly, 43100 Karditsa, Greece; (D.T.L.)
| | - George Th. Tsangaris
- Proteomics Research Unit, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece;
| | - George C. Fthenakis
- Veterinary Faculty, University of Thessaly, 43100 Karditsa, Greece; (D.T.L.)
| | - Angeliki I. Katsafadou
- Faculty of Public and One Health, University of Thessaly, 43100 Karditsa, Greece; (M.V.B.); (D.C.C.)
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Mukherjee A, Abraham S, Singh A, Balaji S, Mukunthan KS. From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies. Mol Biotechnol 2024:10.1007/s12033-024-01133-6. [PMID: 38565775 DOI: 10.1007/s12033-024-01133-6] [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: 12/27/2023] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Abstract
In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.
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Affiliation(s)
- Arnab Mukherjee
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Suzanna Abraham
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Akshita Singh
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - S Balaji
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - K S Mukunthan
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
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14
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Verma VS, Pandey A, Jha AK, Badwaik HKR, Alexander A, Ajazuddin. Polyethylene Glycol-Based Polymer-Drug Conjugates: Novel Design and Synthesis Strategies for Enhanced Therapeutic Efficacy and Targeted Drug Delivery. Appl Biochem Biotechnol 2024:10.1007/s12010-024-04895-6. [PMID: 38519751 DOI: 10.1007/s12010-024-04895-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/04/2024] [Indexed: 03/25/2024]
Abstract
Due to their potential to enhance therapeutic results and enable targeted drug administration, polymer-drug conjugates that use polyethylene glycol (PEG) as both the polymer and the linker for drug conjugation have attracted much research. This study seeks to investigate recent developments in the design and synthesis of PEG-based polymer-drug conjugates, emphasizing fresh ideas that fill in existing knowledge gaps and satisfy the increasing need for more potent drug delivery methods. Through an extensive review of the existing literature, this study identifies key challenges and proposes innovative strategies for future investigations. The paper presents a comprehensive framework for designing and synthesizing PEG-based polymer-drug conjugates, including rational molecular design, linker selection, conjugation methods, and characterization techniques. To further emphasize the importance and adaptability of PEG-based polymer-drug conjugates, prospective applications are highlighted, including cancer treatment, infectious disorders, and chronic ailments.
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Affiliation(s)
- Vinay Sagar Verma
- Faculty of Pharmaceutical Sciences, Shri Shankaracharya Technical Campus, Junwani, Bhilai, 490020, Chhattisgarh, India
- Rungta College of Pharmaceutical Sciences and Research, Kohka, Bhilai, Durg, Chhattisgarh, 490023, India
| | - Aakansha Pandey
- Faculty of Pharmaceutical Sciences, Shri Shankaracharya Technical Campus, Junwani, Bhilai, 490020, Chhattisgarh, India
| | - Arvind Kumar Jha
- Shri Shankaracharya Professional University, Junwani, Bhilai, 490020, Chhattisgarh, India
| | - Hemant Kumar Ramchandra Badwaik
- Shri Shankaracharya College of Pharmaceutical Sciences, Junwani, Bhilai, 490020, Chhattisgarh, India.
- Shri Shankaracharya Institute of Pharmaceutical Sciences and Research, Shri Shankaracharya Technical Campus, Junwani, Bhilai, 490020, Chhattisgarh, India.
| | - Amit Alexander
- Department of Pharmaceuticals, National Institute of Pharmaceutical Education and Research, Ministry of Chemical and Fertilizers, Guwahati, 781101, Assam, India
| | - Ajazuddin
- Rungta College of Pharmaceutical Sciences and Research, Kohka, Bhilai, Durg, Chhattisgarh, 490023, India.
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15
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Teyssonniere EM, Shichino Y, Mito M, Friedrich A, Iwasaki S, Schacherer J. Translation variation across genetic backgrounds reveals a post-transcriptional buffering signature in yeast. Nucleic Acids Res 2024; 52:2434-2445. [PMID: 38261993 PMCID: PMC10954453 DOI: 10.1093/nar/gkae030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 12/21/2023] [Accepted: 01/11/2024] [Indexed: 01/25/2024] Open
Abstract
Gene expression is known to vary among individuals, and this variability can impact the phenotypic diversity observed in natural populations. While the transcriptome and proteome have been extensively studied, little is known about the translation process itself. Here, we therefore performed ribosome and transcriptomic profiling on a genetically and ecologically diverse set of natural isolates of the Saccharomyces cerevisiae yeast. Interestingly, we found that the Euclidean distances between each profile and the expression fold changes in each pairwise isolate comparison were higher at the transcriptomic level. This observation clearly indicates that the transcriptional variation observed in the different isolates is buffered through a phenomenon known as post-transcriptional buffering at the translation level. Furthermore, this phenomenon seemed to have a specific signature by preferentially affecting essential genes as well as genes involved in complex-forming proteins, and low transcribed genes. We also explored the translation of the S. cerevisiae pangenome and found that the accessory genes related to introgression events displayed similar transcription and translation levels as the core genome. By contrast, genes acquired through horizontal gene transfer events tended to be less efficiently translated. Together, our results highlight both the extent and signature of the post-transcriptional buffering.
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Affiliation(s)
| | - Yuichi Shichino
- RNA Systems Biochemistry Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama 351-0198, Japan
| | - Mari Mito
- RNA Systems Biochemistry Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama 351-0198, Japan
| | - Anne Friedrich
- Université de Strasbourg, CNRS, GMGM UMR, 7156 Strasbourg, France
| | - Shintaro Iwasaki
- RNA Systems Biochemistry Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama 351-0198, Japan
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR, 7156 Strasbourg, France
- Institut Universitaire de France (IUF), Paris, France
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16
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Jiang Y, Meyer JG. 1.4 min Plasma Proteome Profiling via Nanoparticle Protein Corona and Direct Infusion Mass Spectrometry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.06.579213. [PMID: 38370692 PMCID: PMC10871276 DOI: 10.1101/2024.02.06.579213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Non-invasive detection of protein biomarkers in plasma is crucial for clinical purposes. Liquid chromatography mass spectrometry (LC-MS) is the gold standard technique for plasma proteome analysis, but despite recent advances, it remains limited by throughput, cost, and coverage. Here, we introduce a new hybrid method, which integrates direct infusion shotgun proteome analysis (DISPA) with nanoparticle (NP) protein coronas enrichment for high throughput and efficient plasma proteomic profiling. We realized over 280 protein identifications in 1.4 minutes collection time, which enables a potential throughput of approximately 1,000 samples daily. The identified proteins are involved in valuable pathways and 44 of the proteins are FDA approved biomarkers. The robustness and quantitative accuracy of this method were evaluated across multiple NPs and concentrations with a mean coefficient of variation at 17%. Moreover, different protein corona profiles were observed among various nanoparticles based on their distinct surface modifications, and all NP protein profiles exhibited deeper coverage and better quantification than neat plasma. Our streamlined workflow merges coverage and throughput with precise quantification, leveraging both DISPA and NP protein corona enrichments. This underscores the significant potential of DISPA when paired with NP sample preparation techniques for plasma proteome studies.
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Affiliation(s)
- Yuming Jiang
- Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Jesse G. Meyer
- Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
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17
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Fu L, Guldiken N, Remih K, Karl AS, Preisinger C, Strnad P. Serum/Plasma Proteome in Non-Malignant Liver Disease. Int J Mol Sci 2024; 25:2008. [PMID: 38396688 PMCID: PMC10889128 DOI: 10.3390/ijms25042008] [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: 12/22/2023] [Revised: 01/31/2024] [Accepted: 02/03/2024] [Indexed: 02/25/2024] Open
Abstract
The liver is the central metabolic organ and produces 85-90% of the proteins found in plasma. Accordingly, the plasma proteome is an attractive source of liver disease biomarkers that reflects the different cell types present in this organ, as well as the processes such as responses to acute and chronic injury or the formation of an extracellular matrix. In the first part, we summarize the biomarkers routinely used in clinical evaluations and their biological relevance in the different stages of non-malignant liver disease. Later, we describe the current proteomic approaches, including mass spectrometry and affinity-based techniques, that allow a more comprehensive assessment of the liver function but also require complex data processing. The many approaches of analysis and interpretation and their potential caveats are delineated. While these advances hold the promise to transform our understanding of liver diseases and support the development and validation of new liver-related drugs, an interdisciplinary collaboration is needed.
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Affiliation(s)
- Lei Fu
- Department of Internal Medicine III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany; (L.F.); (N.G.); (K.R.); (A.S.K.)
| | - Nurdan Guldiken
- Department of Internal Medicine III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany; (L.F.); (N.G.); (K.R.); (A.S.K.)
| | - Katharina Remih
- Department of Internal Medicine III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany; (L.F.); (N.G.); (K.R.); (A.S.K.)
| | - Anna Sophie Karl
- Department of Internal Medicine III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany; (L.F.); (N.G.); (K.R.); (A.S.K.)
| | - Christian Preisinger
- Proteomics Facility, Interdisciplinary Centre for Clinical Research (IZKF), Medical School, RWTH Aachen University, Pauwelsstraße 30, 52074 Aachen, Germany;
| | - Pavel Strnad
- Department of Internal Medicine III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany; (L.F.); (N.G.); (K.R.); (A.S.K.)
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18
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G Jagadeeshaprasad M, Zeng J, Zheng N. LC-MS bioanalysis of protein biomarkers and protein therapeutics in formalin-fixed paraffin-embedded tissue specimens. Bioanalysis 2024; 16:245-258. [PMID: 38226835 DOI: 10.4155/bio-2023-0210] [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: 01/17/2024] Open
Abstract
Formalin-fixed paraffin-embedded (FFPE) is a form of preservation and preparation for biopsy specimens. FFPE tissue specimens are readily available as part of oncology studies because they are often collected for disease diagnosis or confirmation. FFPE tissue specimens could be extremely useful for retrospective studies on protein biomarkers because the samples preserved in FFPE blocks could be stable for decades. However, LC-MS bioanalysis of FFPE tissues poses significant challenges. In this Perspective, we review the benefits and recent developments in LC-MS approach for targeted protein biomarker and protein therapeutic analysis using FFPE tissues and their clinical and translational applications. We believe that LC-MS bioanalysis of protein biomarkers in FFPE tissue specimens represents a great potential for its clinical applications.
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Affiliation(s)
| | - Jianing Zeng
- Department of Protein Sciences & Mass Spectrometry, Translational Medicine, Bristol Myers Squibb, Princeton, NJ 08543, USA
| | - Naiyu Zheng
- Department of Protein Sciences & Mass Spectrometry, Translational Medicine, Bristol Myers Squibb, Princeton, NJ 08543, USA
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19
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Brunnsåker D, Kronström F, Tiukova IA, King RD. Interpreting protein abundance in Saccharomyces cerevisiae through relational learning. Bioinformatics 2024; 40:btae050. [PMID: 38273672 PMCID: PMC10868306 DOI: 10.1093/bioinformatics/btae050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 01/16/2024] [Accepted: 01/23/2024] [Indexed: 01/27/2024] Open
Abstract
MOTIVATION Proteomic profiles reflect the functional readout of the physiological state of an organism. An increased understanding of what controls and defines protein abundances is of high scientific interest. Saccharomyces cerevisiae is a well-studied model organism, and there is a large amount of structured knowledge on yeast systems biology in databases such as the Saccharomyces Genome Database, and highly curated genome-scale metabolic models like Yeast8. These datasets, the result of decades of experiments, are abundant in information, and adhere to semantically meaningful ontologies. RESULTS By representing this knowledge in an expressive Datalog database we generated data descriptors using relational learning that, when combined with supervised machine learning, enables us to predict protein abundances in an explainable manner. We learnt predictive relationships between protein abundances, function and phenotype; such as α-amino acid accumulations and deviations in chronological lifespan. We further demonstrate the power of this methodology on the proteins His4 and Ilv2, connecting qualitative biological concepts to quantified abundances. AVAILABILITY AND IMPLEMENTATION All data and processing scripts are available at the following Github repository: https://github.com/DanielBrunnsaker/ProtPredict.
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Affiliation(s)
- Daniel Brunnsåker
- Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg 412 96, Sweden
| | - Filip Kronström
- Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg 412 96, Sweden
| | - Ievgeniia A Tiukova
- Department of Life Sciences, Chalmers University of Technology, Gothenburg 412 96, Sweden
- Department of Industrial Biotechnology, KTH Royal Institute of Technology, Stockholm 106 91, Sweden
| | - Ross D King
- Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg 412 96, Sweden
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, United Kingdom
- The Alan Turing Institute, London NW1 2DB, United Kingdom
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20
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Szyrwiel L, Gille C, Mülleder M, Demichev V, Ralser M. Fast proteomics with dia-PASEF and analytical flow-rate chromatography. Proteomics 2024; 24:e2300100. [PMID: 37287406 DOI: 10.1002/pmic.202300100] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/22/2023] [Accepted: 05/22/2023] [Indexed: 06/09/2023]
Abstract
Increased throughput in proteomic experiments can improve accessibility of proteomic platforms, reduce costs, and facilitate new approaches in systems biology and biomedical research. Here we propose combination of analytical flow rate chromatography with ion mobility separation of peptide ions, data-independent acquisition, and data analysis with the DIA-NN software suite, to achieve high-quality proteomic experiments from limited sample amounts, at a throughput of up to 400 samples per day. For instance, when benchmarking our workflow using a 500-μL/min flow rate and 3-min chromatographic gradients, we report the quantification of 5211 proteins from 2 μg of a mammalian cell-line standard at high quantitative accuracy and precision. We further used this platform to analyze blood plasma samples from a cohort of COVID-19 inpatients, using a 3-min chromatographic gradient and alternating column regeneration on a dual pump system. The method delivered a comprehensive view of the COVID-19 plasma proteome, allowing classification of the patients according to disease severity and revealing plasma biomarker candidates.
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Affiliation(s)
- Lukasz Szyrwiel
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Christoph Gille
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Core Facility High-Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Michael Mülleder
- Core Facility High-Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Vadim Demichev
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Ralser
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Max Planck Institute for Molecular Genetics, Berlin, Germany
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21
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Akenroye A, Nopsopon T, Cho L, Moll M, Weiss ST. Lower myostatin and higher MUC1 levels are associated with better response to mepolizumab and omalizumab in asthma: a protein-protein interaction analyses. Respir Res 2023; 24:305. [PMID: 38057814 PMCID: PMC10698971 DOI: 10.1186/s12931-023-02620-1] [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: 11/13/2023] [Accepted: 11/28/2023] [Indexed: 12/08/2023] Open
Abstract
INTRODUCTION Biomarkers are needed to inform the choice of biologic therapy in patients with asthma given the increasing number of biologics. We aimed to identify proteins associated with response to omalizumab and mepolizumab. METHODS Aptamer-based proteomic profiling (SomaScan) was used to assess 1437 proteins from 51 patients with moderate to severe asthma who received omalizumab (n = 29) or mepolizumab (n = 22). Response was defined as the change in asthma-related exacerbations in the 12 months following therapy initiation. All models were adjusted for age, sex, and pre-treatment exacerbation rate. Additionally, body mass index was included in the omalizumab model and eosinophil count in the mepolizumab model. We evaluated the association between molecular signatures and response using negative binomial regression correcting for the false discovery rate (FDR) and gene set enrichment analyses (GSEA) to identify associated pathways. RESULTS Over two-thirds of patients were female. The average age for omalizumab patients was 42 years and 57 years for mepolizumab. At baseline, the average exacerbation rate was 1.5/year for omalizumab and 2.4/year for mepolizumab. Lower levels of LOXL2 (unadjusted p: 1.93 × 10E-05, FDR-corrected: 0.028) and myostatin (unadjusted: 3.87 × 10E-05, FDR-corrected: 0.028) were associated with better response to mepolizumab. Higher levels of CD9 antigen (unadjusted: 5.30 × 10E-07, FDR-corrected: 0.0006) and MUC1 (unadjusted: 1.15 × 10E-06, FDR-corrected: 0.0006) were associated with better response to omalizumab, and LTB4R (unadjusted: 1.12 × 10E-06, FDR-corrected: 0.0006) with worse response. Protein-protein interaction network modeling showed an enrichment of the TNF- and NF-kB signaling pathways for patients treated with mepolizumab and multiple pathways involving MAPK, including the FcER1 pathway, for patients treated with omalizumab. CONCLUSIONS This study provides novel fundamental data on proteins associated with response to mepolizumab or omalizumab in severe asthma and warrants further validation as potential biomarkers for therapy selection.
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Affiliation(s)
- Ayobami Akenroye
- Division of Allergy and Clinical Immunology, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA, 02115, USA.
- Channing Division of Network Medicine, Brigham and Women's Hospital, 60 Fenwood Road, BostonBoston, MA, 02115, USA.
| | - Tanawin Nopsopon
- Division of Allergy and Clinical Immunology, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA, 02115, USA
| | - Laura Cho
- Division of Allergy and Clinical Immunology, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA, 02115, USA
| | - Matthew Moll
- Channing Division of Network Medicine, Brigham and Women's Hospital, 60 Fenwood Road, BostonBoston, MA, 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA, 02115, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham and Women's Hospital, 60 Fenwood Road, BostonBoston, MA, 02115, USA
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22
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Zhang X, Li X, Xiong X. Applying proteomics in metabolic dysfunction-associated steatotic liver disease: From mechanism to biomarkers. Clin Res Hepatol Gastroenterol 2023; 47:102230. [PMID: 37931846 DOI: 10.1016/j.clinre.2023.102230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/13/2023] [Accepted: 10/18/2023] [Indexed: 11/08/2023]
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD), which represents the most common cause of liver disease, is emerging as a major health problem around the world. However, the molecular events that underline the pathogenesis and the progression of MASLD remain to be fully elucidated. Advanced stages of MASLD is strongly associated with liver-related outcomes and overall mortality. Despite this, highly accurate, sensitive, and non-invasive diagnostic tools are currently not aviailable, yet no FDA approved drugs for MASLD. The advance of proteomics has enable the study of protein expression, post-translational modifications (PTMs), subcellular distribution, and interactions. In this review, we discuss insights gained from the recent proteomics studies that shed new light on the pathogenesis, diagnosis and potential theraputic targets of MASLD.
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Affiliation(s)
- Xiaofu Zhang
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, 180 Fenlin Road, Xuhui District, Shanghai 200032, China
| | - Xiaoying Li
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, 180 Fenlin Road, Xuhui District, Shanghai 200032, China
| | - Xuelian Xiong
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, 180 Fenlin Road, Xuhui District, Shanghai 200032, China.
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23
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Sathe G, Sapkota GP. Proteomic approaches advancing targeted protein degradation. Trends Pharmacol Sci 2023; 44:786-801. [PMID: 37778939 DOI: 10.1016/j.tips.2023.08.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 08/18/2023] [Accepted: 08/25/2023] [Indexed: 10/03/2023]
Abstract
Targeted protein degradation (TPD) is an emerging modality for research and therapeutics. Most TPD approaches harness cellular ubiquitin-dependent proteolytic pathways. Proteolysis-targeting chimeras (PROTACs) and molecular glue (MG) degraders (MGDs) represent the most advanced TPD approaches, with some already used in clinical settings. Despite these advances, TPD still faces many challenges, pertaining to both the development of effective, selective, and tissue-penetrant degraders and understanding their mode of action. In this review, we focus on progress made in addressing these challenges. In particular, we discuss the utility and application of recent proteomic approaches as indispensable tools to enable insights into degrader development, including target engagement, degradation selectivity, efficacy, safety, and mode of action.
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Affiliation(s)
- Gajanan Sathe
- MRC Protein Phosphorylation and Ubiquitylation Unit, School of Life Sciences, University of Dundee, Dundee DD1 5EH, UK.
| | - Gopal P Sapkota
- MRC Protein Phosphorylation and Ubiquitylation Unit, School of Life Sciences, University of Dundee, Dundee DD1 5EH, UK.
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24
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Teyssonnière E, Trébulle P, Muenzner J, Loegler V, Ludwig D, Amari F, Mülleder M, Friedrich A, Hou J, Ralser M, Schacherer J. Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.18.558197. [PMID: 37781592 PMCID: PMC10541136 DOI: 10.1101/2023.09.18.558197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein co-expression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3.6%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship. Highlights At the level of individual genes, the abundance of transcripts and proteins is weakly correlated within a species ( ρ = 0.165). While the proteome is not imprinted by population structure, co-expression patterns recapitulate the cellular functional landscapeWild populations exhibit a higher abundance of respiration-related proteins compared to domesticated populationsLoci that influence protein abundance differ from those that impact transcript levels, likely because of protein turnover.
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25
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Ribeiro HC, Zandonadi FDS, Sussulini A. An overview of metabolomic and proteomic profiling in bipolar disorder and its clinical value. Expert Rev Proteomics 2023; 20:267-280. [PMID: 37830362 DOI: 10.1080/14789450.2023.2267756] [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: 07/03/2023] [Accepted: 09/12/2023] [Indexed: 10/14/2023]
Abstract
INTRODUCTION Bipolar disorder (BD) is a complex psychiatric disease characterized by alternating mood episodes. As for any other psychiatric illness, currently there is no biochemical test that is able to support diagnosis or therapeutic decisions for BD. In this context, the discovery and validation of biomarkers are interesting strategies that can be achieved through proteomics and metabolomics. AREAS COVERED In this descriptive review, a literature search including original articles and systematic reviews published in the last decade was performed with the objective to discuss the results of BD proteomic and metabolomic profiling analyses and indicate proteins and metabolites (or metabolic pathways) with potential clinical value. EXPERT OPINION A large number of proteins and metabolites have been reported as potential BD biomarkers; however, most studies do not reach biomarker validation stages. An effort from the scientific community should be directed toward the validation of biomarkers and the development of simplified bioanalytical techniques or protocols to determine them in biological samples, in order to translate proteomic and metabolomic findings into clinical routine assays.
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Affiliation(s)
- Henrique Caracho Ribeiro
- Laboratory of Bioanalytics and Integrated Omics (LaBIOmics), Department of Analytical Chemistry, Institute of Chemistry, Universidade Estadual de Campinas(UNICAMP), Campinas, SP, Brazil
| | - Flávia da Silva Zandonadi
- Laboratory of Bioanalytics and Integrated Omics (LaBIOmics), Department of Analytical Chemistry, Institute of Chemistry, Universidade Estadual de Campinas(UNICAMP), Campinas, SP, Brazil
| | - Alessandra Sussulini
- Laboratory of Bioanalytics and Integrated Omics (LaBIOmics), Department of Analytical Chemistry, Institute of Chemistry, Universidade Estadual de Campinas(UNICAMP), Campinas, SP, Brazil
- Instituto Nacional de Ciência e Tecnologia de Bioanalítica (INCTBio), Institute of Chemistry, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, Brazil
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26
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Romano A, Rižner TL, Werner HMJ, Semczuk A, Lowy C, Schröder C, Griesbeck A, Adamski J, Fishman D, Tokarz J. Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review. Front Oncol 2023; 13:1120178. [PMID: 37091170 PMCID: PMC10118013 DOI: 10.3389/fonc.2023.1120178] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/06/2023] [Indexed: 04/09/2023] Open
Abstract
Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.
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Affiliation(s)
- Andrea Romano
- Department of Gynaecology, Maastricht University Medical Centre (MUMC), Maastricht, Netherlands
- GROW – School for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
- *Correspondence: Andrea Romano, ; Tea Lanišnik Rižner,
| | - Tea Lanišnik Rižner
- Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- *Correspondence: Andrea Romano, ; Tea Lanišnik Rižner,
| | - Henrica Maria Johanna Werner
- Department of Gynaecology, Maastricht University Medical Centre (MUMC), Maastricht, Netherlands
- GROW – School for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
| | - Andrzej Semczuk
- Department of Gynaecology, Lublin Medical University, Lublin, Poland
| | | | | | | | - Jerzy Adamski
- Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Dmytro Fishman
- Institute of Computer Science, University of Tartu, Tartu, Estonia
- Quretec Ltd., Tartu, Estonia
| | - Janina Tokarz
- Institute for Diabetes and Cancer, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
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27
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Wang Y, Xi W, Zhang X, Bi X, Liu B, Zheng X, Chi X. CTSB promotes sepsis-induced acute kidney injury through activating mitochondrial apoptosis pathway. Front Immunol 2023; 13:1053754. [PMID: 36713420 PMCID: PMC9880165 DOI: 10.3389/fimmu.2022.1053754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 12/28/2022] [Indexed: 01/15/2023] Open
Abstract
Background Acute kidney injury is a common and severe complication of sepsis. Sepsis -induced acute kidney injury(S-AKI) is an independent risk factor for mortality among sepsis patients. However, the mechanisms of S-AKI are complex and poorly understand. Therefore, exploring the underlying mechanisms of S-AKI may lead to the development of therapeutic targets. Method A model of S-AKI was established in male C57BL/6 mice using cecal ligation and puncture (CLP). The data-independent acquisition (DIA)-mass spectrometry-based proteomics was used to explore the protein expression changes and analyze the key proteomics profile in control and CLP group. The methodology was also used to identify the key proteins and pathways. S-AKI in vitro was established by treating the HK-2 cells with lipopolysaccharide (LPS). Subsequently, the effect and mechanism of Cathepsin B (CTSB) in inducing apoptosis in HK-2 cells were observed and verified. Results The renal injury scores, serum creatinine, blood urea nitrogen, and kidney injury molecule 1 were higher in septic mice than in non-septic mice. The proteomic analysis identified a total of 449 differentially expressed proteins (DEPs). GO and KEGG analysis showed that DEPs were mostly enriched in lysosomal-related cell structures and pathways. CTSB and MAPK were identified as key proteins in S-AKI. Electron microscopy observed enlarged lysosomes, swelled and ruptured mitochondria, and cytoplasmic vacuolization in CLP group. TUNEL staining and CTSB activity test showed that the apoptosis and CTSB activity were higher in CLP group than in control group. In HK-2 cell injury model, the CTSB activity and mRNA expression were increased in LPS-treated cells. Acridine orange staining showed that LPS caused lysosomal membrane permeabilization (LMP). CA074 as an inhibitor of CTSB could effectively inhibit CTSB activity. CCK8 and Annexin V/PI staining results indicated that CA074 reversed LPS-induced apoptosis of HK-2 cells. The JC-1 and western blot results showed that LPS inhibited mitochondrial membrane potential and activated mitochondrial apoptosis pathway, which could be reversed by CA074. Conclusions LMP and CTSB contribute to pathogenesis of S-AKI. LPS treatment induced HK-2 cell injury by activating mitochondrial apoptosis pathway. Inhibition of CTSB might be a new therapeutic strategy to alleviate sepsis-induced acute kidney injury.
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Affiliation(s)
- Yuting Wang
- Department of Anesthesiology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Wenjie Xi
- Department of Anesthesiology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Xinyi Zhang
- Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Xinwen Bi
- Department of Anesthesiology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Boyang Liu
- Department of Anesthesiology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Xiaoming Zheng
- Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China,*Correspondence: Xiaoming Zheng, ; Xinjin Chi,
| | - Xinjin Chi
- Department of Anesthesiology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China,*Correspondence: Xiaoming Zheng, ; Xinjin Chi,
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