Clinical proteomics最新文献

{"title":"The relationship between serum astroglial and neuronal markers and AQP4 and MOG autoantibodies","authors":"Miyo K. Chatanaka, Lisa M. Avery, Maria D. Pasic, Shanthan Sithravadivel, Dalia Rotstein, Catherine Demos, Rachel Cohen, Taron Gorham, Mingyue Wang, Martin Stengelin, Anu Mathew, George Sigal, Jacob Wohlstadter, Ioannis Prassas, Eleftherios P. Diamandis","doi":"10.1186/s12014-024-09466-9","DOIUrl":"https://doi.org/10.1186/s12014-024-09466-9","url":null,"abstract":"Certain demyelinating disorders, such as neuromyelitis optica spectrum disorder (NMOSD) and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) exhibit serum autoantibodies against aquaporin-4 (αAQP4) and myelin oligodendrocyte glycoprotein (αMOG). The variability of the autoantibody presentation warrants further research into subtyping each case. To elucidate the relationship between astroglial and neuronal protein concentrations in the peripheral circulation with occurrence of these autoantibodies, 86 serum samples were analyzed using immunoassays. The protein concentration of glial fibrillary acidic protein (GFAP), neurofilament light chain (NFL) and tau protein was measured in 3 groups of subcategories of suspected NMOSD: αAQP4 positive (n = 20), αMOG positive (n = 32) and αMOG/αAQP4 seronegative (n = 34). Kruskal-Wallis analysis, univariate predictor analysis, and multivariate logistic regression with ROC curves were performed. GFAP and NFL concentrations were significantly elevated in the αAQP4 positive group (p = 0.003; p = 0.042, respectively), and tau was elevated in the αMOG/αAQP4 seronegative group (p < 0.001). A logistic regression model to classify serostatus was able to separate αAQP4 seropositivity using GFAP + tau, and αMOG seropositivity using tau. The areas under the ROC curves (AUCs) were 0.77 and 0.72, respectively. Finally, a combined seropositivity versus negative status logistic regression model was generated, with AUC = 0.80. The 3 markers can univariately and multivariately classify with moderate accuracy the samples with seropositivity and seronegativity for αAQP4 and αMOG.","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140595945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simultaneous targeted and discovery-driven clinical proteotyping using hybrid-PRM/DIA","authors":"Sandra Goetze, Audrey van Drogen, Jonas B. Albinus, Kyle L. Fort, Tejas Gandhi, Damiano Robbiani, Véronique Laforte, Lukas Reiter, Mitchell P. Levesque, Yue Xuan, Bernd Wollscheid","doi":"10.1186/s12014-024-09478-5","DOIUrl":"https://doi.org/10.1186/s12014-024-09478-5","url":null,"abstract":"Clinical samples are irreplaceable, and their transformation into searchable and reusable digital biobanks is critical for conducting statistically empowered retrospective and integrative research studies. Currently, mainly data-independent acquisition strategies are employed to digitize clinical sample cohorts comprehensively. However, the sensitivity of DIA is limited, which is why selected marker candidates are often additionally measured targeted by parallel reaction monitoring. Here, we applied the recently co-developed hybrid-PRM/DIA technology as a new intelligent data acquisition strategy that allows for the comprehensive digitization of rare clinical samples at the proteotype level. Hybrid-PRM/DIA enables enhanced measurement sensitivity for a specific set of analytes of current clinical interest by the intelligent triggering of multiplexed parallel reaction monitoring (MSxPRM) in combination with the discovery-driven digitization of the clinical biospecimen using DIA. Heavy-labeled reference peptides were utilized as triggers for MSxPRM and monitoring of endogenous peptides. We first evaluated hybrid-PRM/DIA in a clinical context on a pool of 185 selected proteotypic peptides for tumor-associated antigens derived from 64 annotated human protein groups. We demonstrated improved reproducibility and sensitivity for the detection of endogenous peptides, even at lower concentrations near the detection limit. Up to 179 MSxPRM scans were shown not to affect the overall DIA performance. Next, we applied hybrid-PRM/DIA for the integrated digitization of biobanked melanoma samples using a set of 30 AQUA peptides against 28 biomarker candidates with relevance in molecular tumor board evaluations of melanoma patients. Within the DIA-detected approximately 6500 protein groups, the selected marker candidates such as UFO, CDK4, NF1, and PMEL could be monitored consistently and quantitatively using MSxPRM scans, providing additional confidence for supporting future clinical decision-making. Combining PRM and DIA measurements provides a new strategy for the sensitive and reproducible detection of protein markers from patients currently being discussed in molecular tumor boards in combination with the opportunity to discover new biomarker candidates.","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140603271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Proteomic signature associated with chronic kidney disease (CKD) progression identified by data-independent acquisition mass spectrometry","authors":"Carlos R. Ramírez Medina, Ibrahim Ali, Ivona Baricevic-Jones, Aghogho Odudu, Moin A. Saleem, Anthony D. Whetton, Philip A. Kalra, Nophar Geifman","doi":"10.1186/s12014-024-09471-y","DOIUrl":"https://doi.org/10.1186/s12014-024-09471-y","url":null,"abstract":"<p><b>Correction to: Clinical Proteomics (2023) 20:19</b></p><p><b>https://doi.org/10.1186/s12014-023-09405-0</b></p><p>In this article the affiliation details for authors Ivona Baricevic-Jones and Philip A Kalra were incorrectly given as “School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, UK” but should have been “Salford Royal Hospital, Northern Care Alliance Foundation Trust, Salford, UK”.</p><p>The original article has been corrected.</p><ol data-track-component=\"outbound reference\"><li data-counter=\"1.\"><p>Ram?rez Medina C.R., Ali I., Baricevic-Jones I, et al. Proteomic signature associated with chronic kidney disease (CKD) progression identified by data-independent acquisition mass spectrometry. Clin Proteom. 2023;20:19. https://doi.org/10.1186/s12014-023-09405-0</p></li></ol><p>Download references<svg aria-hidden=\"true\" focusable=\"false\" height=\"16\" role=\"img\" width=\"16\"><use xlink:href=\"#icon-eds-i-download-medium\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"></use></svg></p><span>Author notes</span><ol><li><p>Philip A. Kalra and Nophar Geifman have equal senior authorship.</p></li></ol><h3>Authors and Affiliations</h3><ol><li><p>Stoller Biomarker Discovery Centre, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK</p><p>Carlos R. Ramírez Medina, Ivona Baricevic-Jones &amp; Anthony D. Whetton</p></li><li><p>Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, UK</p><p>Ibrahim Ali, Ivona Baricevic-Jones &amp; Philip A. Kalra</p></li><li><p>Division of Cardiovascular Sciences, The University of Manchester, Manchester, UK</p><p>Aghogho Odudu</p></li><li><p>Bristol Renal and Children’s Renal Unit, Bristol Medical School, University of Bristol, Bristol, UK</p><p>Moin A. Saleem</p></li><li><p>School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK</p><p>Anthony D. Whetton &amp; Nophar Geifman</p></li><li><p>School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK</p><p>Nophar Geifman</p></li></ol><span>Authors</span><ol><li><span>Carlos R. Ramírez Medina</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Ibrahim Ali</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Ivona Baricevic-Jones</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Aghogho Odudu</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Moin A. Saleem</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Anthony D. Whetton</sp","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140313660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative proteomic analysis of HER2 protein expression in PDAC tumors","authors":"Jamie Randall, Allison L. Hunt, Aratara Nutcharoen, Laura Johnston, Safae Chouraichi, Hongkun Wang, Arthur Winer, Raymond Wadlow, Jasmine Huynh, Justin Davis, Brian Corgiat, Nicholas W. Bateman, John F. Deeken, Emanuel F. Petricoin, Thomas P. Conrads, Timothy L. Cannon","doi":"10.1186/s12014-024-09476-7","DOIUrl":"https://doi.org/10.1186/s12014-024-09476-7","url":null,"abstract":"Metastatic pancreatic adenocarcinoma (PDAC) is the third leading cause of cancer-related death in the United States, with a 5-year survival rate of only 11%, necessitating identification of novel treatment paradigms. Tumor tissue specimens from patients with PDAC, breast cancer, and other solid tumor malignancies were collected and tumor cells were enriched using laser microdissection (LMD). Reverse phase protein array (RPPA) analysis was performed on enriched tumor cell lysates to quantify a 32-protein/phosphoprotein biomarker panel comprising known anticancer drug targets and/or cancer-related total and phosphorylated proteins, including HER2Total, HER2Y1248, and HER3Y1289. RPPA analysis revealed significant levels of HER2Total in PDAC patients at abundances comparable to HER2-positive (IHC 3+) and HER2-low (IHC 1+ /2+ , FISH−) breast cancer tissues, for which HER2 screening is routinely performed. These data support a critical unmet need for routine clinical evaluation of HER2 expression in PDAC patients and examination of the utility of HER2-directed antibody–drug conjugates in these patients.","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140166454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human tear film protein sampling using soft contact lenses.","authors":"Robert K Roden, Nathan Zuniga, Joshua C Wright, David H Parkinson, Fangfang Jiang, Leena M Patil, Rebecca S Burlett, Alyssa A Nitz, Joshua J Rogers, Jarett T Pittman, Kenneth L Virgin, P Christine Ackroyd, Samuel H Payne, John C Price, Kenneth A Christensen","doi":"10.1186/s12014-024-09475-8","DOIUrl":"10.1186/s12014-024-09475-8","url":null,"abstract":"<p><strong>Background: </strong>Human tear protein biomarkers are useful for detecting ocular and systemic diseases. Unfortunately, existing tear film sampling methods (Schirmer strip; SS and microcapillary tube; MCT) have significant drawbacks, such as pain, risk of injury, sampling difficulty, and proteomic disparities between methods. Here, we present an alternative tear protein sampling method using soft contact lenses (SCLs).</p><p><strong>Results: </strong>We optimized the SCL protein sampling in vitro and performed in vivo studies in 6 subjects. Using Etafilcon A SCLs and 4M guanidine-HCl for protein removal, we sampled an average of 60 ± 31 µg of protein per eye. We also performed objective and subjective assessments of all sampling methods. Signs of irritation post-sampling were observed with SS but not with MCT and SCLs. Proteomic analysis by mass spectrometry (MS) revealed that all sampling methods resulted in the detection of abundant tear proteins. However, smaller subsets of unique and shared proteins were identified, particularly for SS and MCT. Additionally, there was no significant intrasubject variation between MCT and SCL sampling.</p><p><strong>Conclusions: </strong>These experiments demonstrate that SCLs are an accessible tear-sampling method with the potential to surpass current methods in sampling basal tears.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10936081/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140118971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neat plasma proteomics: getting the best out of the worst.","authors":"Ines Metatla, Kevin Roger, Cerina Chhuon, Sara Ceccacci, Manuel Chapelle, Pierre-Olivier Schmit, Vadim Demichev, Ida Chiara Guerrera","doi":"10.1186/s12014-024-09477-6","DOIUrl":"10.1186/s12014-024-09477-6","url":null,"abstract":"<p><p>Plasma proteomics holds immense potential for clinical research and biomarker discovery, serving as a non-invasive \"liquid biopsy\" for tissue sampling. Mass spectrometry (MS)-based proteomics, thanks to improvement in speed and robustness, emerges as an ideal technology for exploring the plasma proteome for its unbiased and highly specific protein identification and quantification. Despite its potential, plasma proteomics is still a challenge due to the vast dynamic range of protein abundance, hindering the detection of less abundant proteins. Different approaches can help overcome this challenge. Conventional depletion methods face limitations in cost, throughput, accuracy, and off-target depletion. Nanoparticle-based enrichment shows promise in compressing dynamic range, but cost remains a constraint. Enrichment strategies for extracellular vesicles (EVs) can enhance plasma proteome coverage dramatically, but current methods are still too laborious for large series. Neat plasma remains popular for its cost-effectiveness, time efficiency, and low volume requirement. We used a test set of 33 plasma samples for all evaluations. Samples were digested using S-Trap and analyzed on Evosep One and nanoElute coupled to a timsTOF Pro using different elution gradients and ion mobility ranges. Data were mainly analyzed using library-free searches using DIA-NN. This study explores ways to improve proteome coverage in neat plasma both in MS data acquisition and MS data analysis. We demonstrate the value of sampling smaller hydrophilic peptides, increasing chromatographic separation, and using library-free searches. Additionally, we introduce the EV boost approach, that leverages on the extracellular vesicle fraction to enhance protein identification in neat plasma samples. Globally, our optimized analysis workflow allows the quantification of over 1000 proteins in neat plasma with a 24SPD throughput. We believe that these considerations can be of help independently of the LC-MS platform used.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10935919/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140109554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proteomics of prostate cancer serum and plasma using low and high throughput approaches","authors":"Ghaith M. Hamza, Rekha Raghunathan, Stephanie Ashenden, Bairu Zhang, Eric Miele, Andrew F. Jarnuczak","doi":"10.1186/s12014-024-09461-0","DOIUrl":"https://doi.org/10.1186/s12014-024-09461-0","url":null,"abstract":"Despite progress, MS-based proteomics in biofluids, especially blood, faces challenges such as dynamic range and throughput limitations in biomarker and disease studies. In this work, we used cutting-edge proteomics technologies to construct label-based and label-free workflows, capable of quantifying approximately 2,000 proteins in biofluids. With 70µL of blood and a single depletion strategy, we conducted an analysis of a homogenous cohort (n = 32), comparing medium-grade prostate cancer patients (Gleason score: 7(3 + 4); TNM stage: T2cN0M0, stage IIB) to healthy donors. The results revealed dozens of differentially expressed proteins in both plasma and serum. We identified the upregulation of Prostate Specific Antigen (PSA), a well-known biomarker for prostate cancer, in the serum of cancer cohort. Further bioinformatics analysis highlighted noteworthy proteins which appear to be differentially secreted into the bloodstream, making them good candidates for further exploration.","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140106666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Using phosphoproteomics data to understand cellular signaling: a comprehensive guide to bioinformatics resources","authors":"Sara R. Savage, Bing Zhang","doi":"10.1186/s12014-024-09473-w","DOIUrl":"https://doi.org/10.1186/s12014-024-09473-w","url":null,"abstract":"<p>Correction to: Clinical Proteomics (2023) 17:27</p><p>https://doi.org/10.1186/s12014-020-09290-x</p><p>In the main text, under the section heading “Knowledge bases of kinases and phosphatases“, 6th paragraph, the 3rd sentence that reads as “DEPOD used data from HuPho as a starting point and therefore contains much of the same information [19]” should have read as “DEPOD also includes pathways, substrates, and links to orthologs in addition to interacting partners and upstream kinases [19]”. The original article has been corrected.</p><ul data-track-component=\"outbound reference\"><li><p>Savage, S.R., Zhang, B. Using phosphoproteomics data to understand cellular signaling: a comprehensive guide to bioinformatics resources. Clin Proteom. 2020;17:27. https://doi.org/10.1186/s12014-020-09290-x.</p></li></ul><p>Download references<svg aria-hidden=\"true\" focusable=\"false\" height=\"16\" role=\"img\" width=\"16\"><use xlink:href=\"#icon-eds-i-download-medium\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"></use></svg></p><h3>Authors and Affiliations</h3><ol><li><p>Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA</p><p>Sara R. Savage</p></li><li><p>Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA</p><p>Sara R. Savage &amp; Bing Zhang</p></li><li><p>Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA</p><p>Bing Zhang</p></li></ol><span>Authors</span><ol><li><span>Sara R. Savage</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Bing Zhang</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li></ol><h3>Corresponding author</h3><p>Correspondence to Bing Zhang.</p><h3>Publisher’s Note</h3><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p><p>The online version of the original article can be found at https://doi.org/10.1186/s12014-020-09290-x</p><p><b>Open Access</b> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140055498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Closing the gaps in patient management of dyslipidemia: stepping into cardiovascular precision diagnostics with apolipoprotein profiling.","authors":"Esther Reijnders, Arnoud van der Laarse, L Renee Ruhaak, Christa M Cobbaert","doi":"10.1186/s12014-024-09465-w","DOIUrl":"10.1186/s12014-024-09465-w","url":null,"abstract":"<p><p>In persons with dyslipidemia, a high residual risk of cardiovascular disease remains despite lipid lowering therapy. Current cardiovascular risk prediction mainly focuses on low-density lipoprotein cholesterol (LDL-c) levels, neglecting other contributing risk factors. Moreover, the efficacy of LDL-c lowering by statins resulting in reduced cardiovascular risk is only partially effective. Secondly, from a metrological viewpoint LDL-c falls short as a reliable measurand. Both direct and calculated LDL-c tests produce inaccurate test results at the low end under aggressive lipid lowering therapy. As LDL-c tests underperform both clinically and metrologically, there is an urging need for molecularly defined biomarkers. Over the years, apolipoproteins have emerged as promising biomarkers in the context of cardiovascular disease as they are the functional workhorses in lipid metabolism. Among these, apolipoprotein B (ApoB), present on all atherogenic lipoprotein particles, has demonstrated to clinically outperform LDL-c. Other apolipoproteins, such as Apo(a) - the characteristic apolipoprotein of the emerging risk factor lipoprotein(a) -, and ApoC-III - an inhibitor of triglyceride-rich lipoprotein clearance -, have attracted attention as well. To support personalized medicine, we need to move to molecularly defined risk markers, like the apolipoproteins. Molecularly defined diagnosis and molecularly targeted therapy require molecularly measured biomarkers. This review provides a summary of the scientific validity and (patho)physiological role of nine serum apolipoproteins, Apo(a), ApoB, ApoC-I, ApoC-II, ApoC-III, ApoE and its phenotypes, ApoA-I, ApoA-II, and ApoA-IV, in lipid metabolism, their association with cardiovascular disease, and their potential as cardiovascular risk markers when measured in a multiplex apolipoprotein panel.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10908091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140012359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proteomic analysis of plasma proteins from patients with cardiac rupture after acute myocardial infarction using TMT-based quantitative proteomics approach.","authors":"Jingyuan Hou, Qiaoting Deng, Xiaohong Qiu, Sudong Liu, Youqian Li, Changjing Huang, Xianfang Wang, Qunji Zhang, Xunwei Deng, Zhixiong Zhong, Wei Zhong","doi":"10.1186/s12014-024-09474-9","DOIUrl":"10.1186/s12014-024-09474-9","url":null,"abstract":"<p><strong>Background: </strong>Cardiac rupture (CR) is a rare but catastrophic mechanical complication of acute myocardial infarction (AMI) that seriously threatens human health. However, the reliable biomarkers for clinical diagnosis and the underlying signaling pathways insights of CR has yet to be elucidated.</p><p><strong>Methods: </strong>In the present study, a quantitative approach with tandem mass tag (TMT) labeling and liquid chromatography-tandem mass spectrometry was used to characterize the differential protein expression profiles of patients with CR. Plasma samples were collected from patients with CR (n = 37), patients with AMI (n = 47), and healthy controls (n = 47). Candidate proteins were selected for validation by multiple reaction monitoring (MRM) and enzyme-linked immunosorbent assay (ELISA).</p><p><strong>Results: </strong>In total, 1208 proteins were quantified and 958 differentially expressed proteins (DEPs) were identified. The difference in the expression levels of the DEPs was more noticeable between the CR and Con groups than between the AMI and Con groups. Bioinformatics analysis showed most of the DEPs to be involved in numerous crucial biological processes and signaling pathways, such as RNA transport, ribosome, proteasome, and protein processing in the endoplasmic reticulum, as well as necroptosis and leukocyte transendothelial migration, which might play essential roles in the complex pathological processes associated with CR. MRM analysis confirmed the accuracy of the proteomic analysis results. Four proteins i.e., C-reactive protein (CRP), heat shock protein beta-1 (HSPB1), vinculin (VINC) and growth/differentiation factor 15 (GDF15), were further validated via ELISA. By receiver operating characteristic (ROC) analysis, combinations of these four proteins distinguished CR patients from AMI patients with a high area under the curve (AUC) value (0.895, 95% CI, 0.802-0.988, p < 0.001).</p><p><strong>Conclusions: </strong>Our study highlights the value of comprehensive proteomic characterization for identifying plasma proteome changes in patients with CR. This pilot study could serve as a valid foundation and initiation point for elucidation of the mechanisms of CR, which might aid in identifying effective diagnostic biomarkers in the future.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10908035/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140012360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}