Biology subjects

Schepmoes, A.

Publications and source records attributed to Schepmoes, A..

4 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↗

bioRxiv2026

Assessing extracellular vesicle proteins as predictive biomarkers for developing type 1 diabetes

Plasma extracellular vesicles (EVs) are considered excellent sources for biomarker discovery since they carry signatures of their cellular origin and disease processes. In this paper, we evaluate the potential of plasma EV proteomics analysis for identifying predictive biomarkers of developing type 1 diabetes (T1D), which results from autoimmune destruction of insulin-producing {beta} cells in the islet. We used strong anion exchange beads (Mag-Net) to capture plasma EVs from 19 donors with islet autoimmunity (diagnosed by circulating autoantibodies against islet proteins - AAB+) vs. 17 control individuals and analyzed their protein cargo by mass spectrometry. The analysis identified and quantified 5,480 proteins, a 3.2-fold increase in proteome coverage compared to our previous T1D biomarker proteomics study that used whole plasma depleted of the 14 most abundant proteins. The Mag-Net approach also detected 1,306 out of the 1,717 proteins (76%) that we previously verified as EV proteins. Statistical tests revealed 448 proteins to be differentially abundant in AAB+ vs control volunteers, including 69 previously verified EV proteins. A functional-enrichment analysis resulted in overrepresentation of 25 pathways among the differentially abundant proteins, including pathways related to autoimmune response and lipid metabolism. The capacity of this data to predict AAB+ was tested with a machine learning analysis using a random forest model, resulting in a receiver operating characteristic-area under the curve of 0.81. Overall, our study indicates that plasma EV proteomics analysis can be an exciting approach for studying biomarkers for developing T1D. Significance of the studyType 1 diabetes (T1D) is a disease characterized by the bodys inability to produce insulin and consequently, to control blood glucose levels. Despite the initial trigger being unclear, the disease development process involves an autoimmune response to the islets of Langerhans, resulting in the death of insulin-producing {beta} cells. There is no cure for the disease, and treatment relies on exogenous administration of insulin. Therefore, preventive therapies that block the autoimmune process are attractive for treating T1D. In fact, anti-CD3 antibody (Teplizumab) delays the onset of T1D by 2 years by targeting T cells. Predictive biomarkers for developing T1D are needed to aid the development and implementation of new therapies and to identify the initial trigger and mechanisms of the islet autoimmune process. In this paper, we assess the potential of plasma extracellular vesicle (EV) proteomics analysis for identifying predictive biomarkers of T1D. Our results show excellent potential of the approach, opening opportunities to perform broader studies to identify biomarkers for developing T1D.

systems biology↗

bioRxiv2025

TOR inhibition drives accumulation of amino acids through transcriptional activation in algae

Cellular homeostasis is maintained by the balance between energy production and breakdown and is fundamental to all forms of life. The conserved, ancient target of rapamycin (TOR) kinase is a central metabolic regulator in eukaryotes that integrates carbon and nitrogen to maintain homeostasis and promote growth and development through protein synthesis. While TOR regulatory mechanisms of amino acid accumulation are well known in yeast and mammals, they remain unknown in photosynthetic organisms. Here, we developed the unicellular green alga Chromochloris zofingiensis as a simpler model system for understanding TOR function. Multiomics experiments showed that TOR inhibition leads to an increase in amino acid levels independent of hexokinase-mediated glucose signaling. We observed upregulation of selective amino acid biosynthesis pathways at the transcript and protein levels as potential mechanisms driving the increase in amino acids. Transcriptomics and proteomics experiments identified a basic helix-loop-helix (bHLH) transcription factor with rapid upregulation during TOR inhibition. DAP-seq analysis demonstrated that bHLH can bind directly to the promoters of amino acid biosynthesis genes, potentially regulating their transcription in response to TOR inhibition. We found high conservation of the bHLH-binding motif in the genomes of other green algae and plants, suggesting a conserved regulatory mechanism for amino acid biosynthesis across Viridiplantae. Phosphoproteomics experiments also revealed novel conserved targets that are not currently recognized as part of the TOR pathway. Altogether, our findings elucidate the transcriptional regulation of amino acid metabolism and explain how TOR regulates nitrogen metabolism to support growth and development in photosynthetic organisms. Significance StatementCarbon and nitrogen metabolism play key roles in enhancing plant yield and reducing fertilizer use. Thus, improving nitrogen utilization can significantly boost crop productivity and algal biotechnology. From yeast to plants to mammals, the protein target of rapamycin (TOR) kinase is an essential metabolic regulator. Here, we developed the unicellular green alga Chromochloris zofingiensis as a simpler system to study conserved mechanisms in TOR signaling. Using a multiomics approach, we showed transcriptional regulation of amino acid accumulation upon TOR inhibition and identified a transcription factor with evolutionarily conserved DNA binding sites in nitrogen metabolism genes. We also discovered novel conserved targets of TOR. Our study demonstrates the role of TOR in regulating nitrogen metabolism to support growth and development in photosynthetic organisms.

plant biology↗

bioRxiv2022

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and fibromyalgia are indistinguishable by their cerebrospinal fluid proteomes

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and fibromyalgia have overlapping neurologic symptoms particularly disabling fatigue. This has given rise to the question whether they are distinct central nervous system (CNS) entities or is one an extension of the other. To investigate this, we used unbiased quantitative mass spectrometry-based proteomics to examine the most proximal fluid to the brain, cerebrospinal fluid (CSF). This was to ascertain if the proteome profile of one was the same or different from the other. We examined two separate groups of ME/CFS, one with (n=15) and one without (n=15) fibromyalgia. We quantified a total of 2,083 proteins using immunoaffinity depletion, tandem mass tag isobaric labeling and offline two-dimensional liquid chromatography coupled to tandem mass spectrometry, including 1,789 that were quantified in all the CSF samples. ANOVA analysis did not yield any proteins with an adjusted p-value < 0.05. This supports the notion that ME/CFS and fibromyalgia as currently defined are not distinct entities.

neuroscience↗