{"title":"Obituary: In Memory of Michael (Mike) J. Dunn (1946–2024)","authors":"","doi":"10.1002/pmic.202500077","DOIUrl":"https://doi.org/10.1002/pmic.202500077","url":null,"abstract":"","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"25 8","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article explores the ethical and societal issues in developing personalised medicine (PM) as part of the KidneySign project, which aims to mobilise translational big data to validate a proteomic signature of renal fibrosis with prognostic value. This research offers hope for improved management of chronic kidney disease, including diagnosis and treatment. This article examines how the human and social sciences can be mobilised within a biomedical research project to identify and prevent concomitant ethical, legal and social issues. This point of view defends a multidisciplinary approach to PM and artificial intelligence in medicine. Presenting theoretical and methodological contributions of social sciences in the case of KidneySign offers an opportunity to better understand the integration of these disciplines in biomedical research. It allows us to question the study protocol itself and to frame it through legal obligations, as well as potential legal consequences and challenges. Moreover, sociological assessments help identify key points and highlight the limits of the technophilic fantasy in the representations of patients and health professionals. The introduction of new technologies into medical research and practice requires special attention to ethics.
{"title":"Ethics of Personalised Medicine: Importance of the Multidisciplinary Approach in KidneySign Project.","authors":"Delphine Azéma, Flore Duranton, Àngel Argilés, Emmanuelle Rial-Sebbag","doi":"10.1002/pmic.202400176","DOIUrl":"https://doi.org/10.1002/pmic.202400176","url":null,"abstract":"<p><p>This article explores the ethical and societal issues in developing personalised medicine (PM) as part of the KidneySign project, which aims to mobilise translational big data to validate a proteomic signature of renal fibrosis with prognostic value. This research offers hope for improved management of chronic kidney disease, including diagnosis and treatment. This article examines how the human and social sciences can be mobilised within a biomedical research project to identify and prevent concomitant ethical, legal and social issues. This point of view defends a multidisciplinary approach to PM and artificial intelligence in medicine. Presenting theoretical and methodological contributions of social sciences in the case of KidneySign offers an opportunity to better understand the integration of these disciplines in biomedical research. It allows us to question the study protocol itself and to frame it through legal obligations, as well as potential legal consequences and challenges. Moreover, sociological assessments help identify key points and highlight the limits of the technophilic fantasy in the representations of patients and health professionals. The introduction of new technologies into medical research and practice requires special attention to ethics.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400176"},"PeriodicalIF":3.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143762625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Payman Nickchi, Uladzislau Vadadokhau, Mehdi Mirzaie, Marc Baumann, Amir A. Saei, Mohieddin Jafari
Posttranslational modifications (PTMs) are of significant interest in molecular biomedicine due to their crucial role in signal transduction across various cellular and organismal processes. Characterizing PTMs, distinguishing between functional and inert modifications, quantifying their occupancies, and understanding PTM crosstalk are challenging tasks in any biosystem. Studying each PTM often requires a specific, labor-intensive experimental design. Here, we present a PTM-centric proteome informatic pipeline for predicting relevant PTMs in mass spectrometry-based proteomics data without prior information. Once predicted, these in silico identified PTMs can be incorporated into a refined database search and compared to measured data. As a practical application, we demonstrate how this pipeline can be used to study glycoproteomics in oral squamous cell carcinoma based on the proteome profile of primary tumors. Subsequently, we experimentally identified cellular proteins that are differentially expressed in cells treated with multikinase inhibitors dasatinib and staurosporine using mass spectrometry-based proteomics. Computational enrichment analysis was then employed to determine the potential PTMs of differentially expressed proteins induced by both drugs. Finally, we conducted an additional round of database search with the predicted PTMs. Our pipeline successfully analyzed the enriched PTMs, and detected proteins not identified in the initial search. Our findings support the effectiveness of PTM-centric searching of MS data in proteomics based on computational enrichment analysis, and we propose integrating this approach into future proteomics search engines.
{"title":"Monitoring Functional Posttranslational Modifications Using a Data-Driven Proteome Informatic Pipeline","authors":"Payman Nickchi, Uladzislau Vadadokhau, Mehdi Mirzaie, Marc Baumann, Amir A. Saei, Mohieddin Jafari","doi":"10.1002/pmic.202400238","DOIUrl":"10.1002/pmic.202400238","url":null,"abstract":"<p>Posttranslational modifications (PTMs) are of significant interest in molecular biomedicine due to their crucial role in signal transduction across various cellular and organismal processes. Characterizing PTMs, distinguishing between functional and inert modifications, quantifying their occupancies, and understanding PTM crosstalk are challenging tasks in any biosystem. Studying each PTM often requires a specific, labor-intensive experimental design. Here, we present a PTM-centric proteome informatic pipeline for predicting relevant PTMs in mass spectrometry-based proteomics data without prior information. Once predicted, these in silico identified PTMs can be incorporated into a refined database search and compared to measured data. As a practical application, we demonstrate how this pipeline can be used to study glycoproteomics in oral squamous cell carcinoma based on the proteome profile of primary tumors. Subsequently, we experimentally identified cellular proteins that are differentially expressed in cells treated with multikinase inhibitors dasatinib and staurosporine using mass spectrometry-based proteomics. Computational enrichment analysis was then employed to determine the potential PTMs of differentially expressed proteins induced by both drugs. Finally, we conducted an additional round of database search with the predicted PTMs. Our pipeline successfully analyzed the enriched PTMs, and detected proteins not identified in the initial search. Our findings support the effectiveness of PTM-centric searching of MS data in proteomics based on computational enrichment analysis, and we propose integrating this approach into future proteomics search engines.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"25 8","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202400238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143655578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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