Biology subjects

Sagendorf, T.

Publications and source records attributed to Sagendorf, T..

3 recordsLinked to original sources

bioRxiv2026

Detection of Melanoma Using Deep Serum Proteome Profiling and Machine Learning

Early detection determines melanoma outcomes, yet current screening relies on visual inspection that misses molecular changes preceding clinical diagnosis. To identify serum protein signatures of melanoma development, we leveraged the Department of Defense Serum Repository, analyzing 390 longitudinal serum samples from 73 melanoma cases and matched controls across four timepoints (4 and 2 years prior, diagnosis, and 2 years post-diagnosis) using data-independent acquisition mass spectrometry with Seer Proteograph nanoparticle enrichment. We quantified 3,364 proteins and applied machine-learning strategies combining cross-sectional case-control comparisons with longitudinal tracking of within-individual changes. Cross-sectional analysis at diagnosis achieved AUC 0.823 (95% CI: 0.723-0.923), identifying eight consensus features selected in >50% of cross-validation folds: PRSS1, GSK3B, FAM20C, CES1, CCL14, EPHA10, LMAN2, and ITIH1. These signatures, spanning proteases, immune activation, and extracellular matrix remodeling, demonstrate proof-of-concept for serum-based melanoma detection and provide candidate biomarkers for validation.

Cancer Biology↗

bioRxiv2025

Ampk alpha2 T172 Activation Dictates Exercise Performance and Energy Transduction in Skeletal Muscle

AMPK (5'-AMP-activated protein kinase) is an energetic sensor for metabolic regulation and integration. Here, we employed CRISPR/Cas9 to generate non-activatable Ampk knock-in (KI) mice with mutation of threonine 172 phosphorylation site to alanine, circumventing the limitations of previous genetic interventions that disrupt the protein stoichiometry. KI mice of Ampk2, but not Ampk1, demonstrated phenotypic changes with increased fat-to-lean mass, impaired endurance exercise capacity, and diminished mitochondrial maximal respiration and conductance in skeletal muscle. Integrated temporal multi-omic analysis (proteomics/phosphoproteomics/metabolomics) in skeletal muscle at rest and during exercise establishes a pleiotropic yet imperative role of Ampk2 T172 activation for glycolytic and oxidative metabolism, mitochondrial respiration, and contractile function. Importantly, there is a significant overlap of skeletal muscle proteomic changes in Ampk2 T172A KI mice with that of type 2 diabetic patients. Our findings suggest that Ampk2 T172 activation is critical for exercise performance and energy transduction in skeletal muscle and may serve as a therapeutic target for type 2 diabetes.

molecular biology↗

bioRxiv2025

Characterization of cytokine treatment on human pancreatic islets by top-down proteomics

Type 1 diabetes (T1D) results from autoimmune-mediated destruction of insulin-producing {beta} cells in the pancreatic islet. This process is modulated by pro-inflammatory cytokine signaling, which have been previously shown to alter protein expression in ex vivo islets. Herein, we applied top-down proteomics to globally evaluate proteoforms from human islets treated with proinflammatory cytokines (interferon-{gamma} and interleukin-1{beta}). We measured 1636 unique proteoforms across 6 donors and two time points (control and 24-hours post-treatment) and observed consistent changes in abundance across the glicentin-related pancreatic polypeptide (GRPP) and major proglucagon fragment regions of glucagon, as well as the LF-19/catestatin and vasostatin-1/2 region of chromogranin-A. We also observe several proteoforms that increase after cytokine-treatment or are exclusively observed after cytokine-treatment including forms of beta-2 Microglobulin (B2M), high-mobility group N2 protein (HMGN2), and chemokine (C-X-C motif) ligands (CXCL). Together, our quantitative results provide a baseline proteoform profile for human islets and identify several proteoforms that may serve as interesting candidate markers for T1D progression or therapeutic intervention. Statement of significance of the studyThis work applies a top-down proteomics workflow for the characterization and label-free quantification of proteoforms from human islets in the context of inflammation. The workflow is optimized for challenges unique to the islet proteome including high disulfide-linkage content and frequent truncation events, resulting in many proteoforms <5 kDa. There are limited examples of top-down proteomics characterization of human islets, thus this study provides a baseline characterization of the proteoforms of major hormones including insulin, glucagon, chromogranin-A and -B, and somatostatin. The quantitative results of proteoform abundances before and after cytokine treatment, which mimics the proinflammatory environment during T1D progression, enables the discovery of interesting proteoform candidates as potential markers of T1D progression.

biochemistry↗