Enrichment of Phosphorylated Peptides with Metal–Organic Framework Nanosheets for Serum Profiling of Diabetes and Phosphoproteomics Analysis
Bibliographic record
Abstract
Capturing phosphopeptides from complicated biological samples is essential for the discovery of new post-translational modification sites and disease diagnostics. Although several two-dimensional (2-D) materials have been used for phosphopeptides capturing, metal–organic framework (MOF) nanosheets have not been reported. The Ti-based MOF nanosheets have well-defined 2-D morphology, high density of active sites, large surface area, and an ultrathin structure. Phosphopeptides can be efficiently extracted and superior detection limits of 0.1 fmol μL–1 can be achieved even for an extremely low molar ratio of phosphoprotein/nonphosphoprotein (1:10000) mixtures. The selectivity over nonphosphopeptides can be enhanced further by pretreatment with a 10 mM salt solution (β-glycerophosphate disodium, NaCl, or KCl). The performance of 2-D Ti-based MOF nanosheets is much better than Zr-based MOF (Zr-BTB) nanosheets or any other Ti-based 3-D MOF counterpart, such as MIL-125 and NH2-MIL-125. The nanosheets were used for in situ isotope labeling for abnormally regulated phosphopeptides analysis from serum samples of type 2 diabetes patients. The relative quantitative results showed that three of the phosphorylated fibrinogen peptides A (FPA, DpSGEGDFLAEGGGV, DpSGEGDFLAEGGGVR, and ADpSGEGDFLAEGGGVR) were down-regulated, while the other isoform (ADpSGEGDFLAEGGGV) was up-regulated in the serum samples of type 2 diabetes patients compared with those of healthy volunteers. Finally, proteomics analysis showed selective enrichment of phosphopeptides with 2-D Ti-based MOF nanosheets from real samples, including tryptic digests of mouse brain neocortex lysate, mouse spinal cord lysate, and mouse testis lysate, followed by LC-MS/MS analysis. Total numbers of 2601, 3208, and 2866 phosphopeptides were successfully identified from the three samples, respectively. The 2-D Ti-based MOF nanosheets significantly improved sample preparation for mass spectrometric analysis in phosphopeptides and phosphoproteomics research.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".