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  1. Article ; Online: The Dynamic and Crucial Role of the Arginine Methylproteome in Myoblast Cell Differentiation

    Nikolaos A. Papanikolaou / Marios Nikolaidis / Grigorios D. Amoutzias / Ariadni Fouza / Maria Papaioannou / Akhilesh Pandey / Athanasios G. Papavassiliou

    International Journal of Molecular Sciences, Vol 24, Iss 2124, p

    2023  Volume 2124

    Abstract: Protein arginine methylation is an extensive and functionally significant post-translational modification. However, little is known about its role in differentiation at the systems level. Using stable isotope labeling by amino acids in cell culture ( ... ...

    Abstract Protein arginine methylation is an extensive and functionally significant post-translational modification. However, little is known about its role in differentiation at the systems level. Using stable isotope labeling by amino acids in cell culture (SILAC) proteomics of whole proteome analysis in proliferating or five-day differentiated mouse C2C12 myoblasts, followed by high-resolution mass spectrometry, biochemical assays, and specific immunoprecipitation of mono- or dimethylated arginine peptides, we identified several protein families that were differentially methylated on arginine. Our study is the first to reveal global changes in the arginine mono- or dimethylation of proteins in proliferating myoblasts and differentiated myocytes and to identify enriched protein domains and novel short linear motifs (SLiMs). Our data may be crucial for dissecting the links between differentiation and cancer growth.
    Keywords arginine ; C2C12 ; differentiation ; methylproteome ; myoblast ; SILAC ; Biology (General) ; QH301-705.5 ; Chemistry ; QD1-999
    Subject code 571
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Exploring Perturbations in Peripheral B Cell Memory Subpopulations Early after Kidney Transplantation Using Unsupervised Machine Learning

    Ariadni Fouza / Anneta Tagkouta / Maria Daoudaki / Maria Stangou / Asimina Fylaktou / Konstantinos Bougioukas / Aliki Xochelli / Lampros Vagiotas / Efstratios Kasimatis / Vasiliki Nikolaidou / Lemonia Skoura / Aikaterini Papagianni / Nikolaos Antoniadis / Georgios Tsoulfas

    Journal of Clinical Medicine, Vol 12, Iss 6331, p

    2023  Volume 6331

    Abstract: Background: B cells have a significant role in transplantation. We examined the distribution of memory subpopulations (MBCs) and naïve B cell (NBCs) phenotypes in patients soon after kidney transplantation. Unsupervised machine learning cluster analysis ... ...

    Abstract Background: B cells have a significant role in transplantation. We examined the distribution of memory subpopulations (MBCs) and naïve B cell (NBCs) phenotypes in patients soon after kidney transplantation. Unsupervised machine learning cluster analysis is used to determine the association between the cellular phenotypes and renal function. Methods: MBC subpopulations and NBCs from 47 stable renal transplant recipients were characterized by flow cytometry just before (T0) and 6 months after (T6) transplantation. T0 and T6 measurements were compared, and clusters of patients with similar cellular phenotypic profiles at T6 were identified. Two clusters, clusters 1 and 2, were formed, and the glomerular filtration rate was estimated (eGFR) for these clusters. Results: A significant increase in NBC frequency was observed between T0 and T6, with no statistically significant differences in the MBC subpopulations. Cluster 1 was characterized by a predominance of the NBC phenotype with a lower frequency of MBCs, whereas cluster 2 was characterized by a high frequency of MBCs and a lower frequency of NBCs. With regard to eGFR, cluster 1 showed a higher value compared to cluster 2. Conclusions: Transplanted kidney patients can be stratified into clusters based on the combination of heterogeneity of MBC phenotype, NBCs and eGFR using unsupervised machine learning.
    Keywords B lymphocytes ; B cell subsets ; kidney transplantation ; Medicine ; R
    Subject code 610
    Language English
    Publishing date 2023-10-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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