Systematic evaluation of Copper(II)-loaded immobilized metal affinity chromatography for selective enrichment of copper-binding species in human serum and plasma
Why this work is in the frame
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Bibliographic record
Abstract
Copper is essential in a host of biological processes, and disruption of its homeostasis is associated with diseases including neurodegeneration and metabolic disorders. Extracellular copper shifts in its speciation between healthy and disease states, and identifying molecular components involved in these perturbations could widen the panel of biomarkers for copper status. While there have been exciting advances in approaches for studying the extracellular proteome with mass spectrometry-based methods, the typical workflows disrupt metal-protein interactions due to the lability of these bonds either during sample preparation or in gas-phase environments. We sought to develop and apply a workflow to enrich for and identify protein populations with copper-binding propensities in extracellular fluids using an immobilized metal affinity chromatography (IMAC) resin. The strategy was optimized using human serum to allow for maximum quantity and diversity of protein enrichment. Protein populations could be differentiated based on protein load on the resin, likely on account of differences in abundance and affinity. The enrichment workflow was applied to plasma samples from patients with Wilson's disease and protein IDs and differential abundancies relative to healthy subjects were compared to those yielded from a traditional proteomic workflow. While the IMAC workflow preserved differential abundance and protein ID information from the traditional workflow, it identified several additional proteins being differentially abundant including those involved in lipid metabolism, immune system, and antioxidant pathways. Our results suggest the potential for this IMAC workflow to identify new proteins as potential biomarkers in copper-associated disease states.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it