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Post-translational modifications (PTMs) greatly expand the function and potential for regulation of protein activity, and O-glycosylation is among the most abundant and diverse PTMs. Initiation of O-GalNAc glycosylation is regulated by 20 distinct GalNAc-transferases (GalNAc-Ts), and deficiencies in individual GalNAc-Ts are associated with human disease, causing subtle but distinct phenotypes in model organisms. Here, we generate a set of isogenic keratinocyte cell lines lacking either of the three dominant and differentially expressed GalNAc-Ts. Through the ability of keratinocytes to form epithelia, we investigate the phenotypic consequences of the loss of individual GalNAc-Ts. Moreover, we probe the cellular responses through global transcriptomic, differential glycoproteomic, and differential phosphoproteomic analyses. We demonstrate that loss of individual GalNAc-T isoforms causes distinct epithelial phenotypes through their effect on specific biological pathways; GalNAc-T1 targets are associated with components of the endomembrane system, GalNAc-T2 targets with cell-ECM adhesion, and GalNAc-T3 targets with epithelial differentiation. Thus, GalNAc-T isoforms serve specific roles during human epithelial tissue formation.
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http://dx.doi.org/10.15252/embr.201948885 | DOI Listing |
Clin Epigenetics
September 2025
Department of Psychiatry and Psychotherapy, Philipps University Marburg, Marburg, Germany.
Background: Work-related stress is a well-established contributor to mental health decline, particularly in the context of burnout, a state of prolonged exhaustion. Epigenetic clocks, which estimate biological age based on DNA methylation (DNAm) patterns, have been proposed as potential biomarkers of chronic stress and its impact on biological aging and health. However, their role in mediating the relationship between work-related stress, physiological stress markers, and burnout remains unclear.
View Article and Find Full Text PDFGenome Biol
September 2025
Department of Evolutionary Genetics, Max-Planck Institute for Evolutionary Biology, Plön, Germany.
Background: Most RNA-seq datasets harbor genes with extreme expression levels in some samples. Such extreme outliers are usually treated as technical errors and are removed from the data before further statistical analysis. Here we focus on the patterns of such outlier gene expression to investigate whether they provide insights into the underlying biology.
View Article and Find Full Text PDFImmun Ageing
September 2025
Department of Biomedical Data Sciences, Molecular Epidemiology, LUMC, Leiden, The Netherlands.
The MetaboHealth score is an indicator of physiological frailty in middle aged and older individuals. The aim of the current study was to explore which molecular pathways co-vary with the MetaboHealth score. Using a Luminex cytokine assay and liquid chromatography-mass spectrometry-based proteomics we explored the plasma proteins associating with the difference in 100 extreme scoring individuals selected from two large population cohorts, the Leiden Longevity Study (LLS) and the Rotterdam Study (RS), and discordant monozygotic twin pairs from the Netherlands Twin Register (NTR).
View Article and Find Full Text PDFLipids Health Dis
September 2025
The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
Background: The CRP-albumin-lymphocyte (CALLY) index has potential clinical value as a novel marker integrating inflammatory, nutritional and immune status in the development of colorectal polyps. This study examined whether gender factors influence the association between CALLY and colorectal polyps; in addition to elucidating whether metabolic pathways mediate this relationship.
Methods: This is a cross-sectional study including 5409 adult health screening participants who completed colonoscopy.
Metabolomics
September 2025
Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
Introduction: Initially developed for transcriptomics data, pathway analysis (PA) methods can introduce biases when applied to metabolomics data, especially if input parameters are not chosen with care. This is particularly true for exometabolomics data, where there can be many metabolic steps between the measured exported metabolites in the profile and internal disruptions in the organism. However, evaluating PA methods experimentally is practically impossible when the sample's "true" metabolic disruption is unknown.
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