A37 EXPLORING THE PROTEOMICS DIFFERENCES IN CROHN’S DISEASE PATIENTS
Bibliographic record
Abstract
Abstract Background Canada has the highest prevalence rate of Crohn’s disease (CD) in North America. In Alberta, the yearly cost of anti-inflammatory drugs can be more than $25,000 per person; however, half of the patients do not respond to medication. CD is characterized by lesions in the small intestine due to inflammation, promoting diarrhea and abdominal pain. Prolonged chronic inflammation results in fibrotic strictures that are resistant to anti-inflammatory therapies and promote narrowing of the luminal space that ultimately require surgery. Currently, there is no biomarker to distinguish between the inflammatory or stricturing phenotype. Aims AIM 1: Profile serum samples from CD patients using a label-free shotgun-proteomics. AIM 2: Identify signatures and biomarkers that distinguish inflammatory and fibrotic strictures using a bioinformatics approach. Methods Serum samples from 15 CD patients with strictures and 15 CD patients without strictures (inflammatory phenotype), as diagnosed by ultrasound imaging, were analyzed by a standard shotgun-proteomics approach. Briefly, 200 µg of serum proteins were processed in a label-free protocol in combination with the filter-aided sample preparation (FASP) method. Liquid chromatography-tandem mass spectrometry was performed on an Orbitrap Fusion Lumos. Protein identification was accomplished by MaxQuant at a 1% false-discovery rate. Statistical significance was determined by the MSstats package, in the R software. To identify the biological significance of disturbed pathways, it was characterized by the protein-protein interactions and pathway enrichment analysis using String-DB and Metascape. Results It was identified a statistically significant protein panel between the two phenotypes. Proteins identified in the strictured group include JAK1 (Tyrosine-protein kinase), CD5 antigen-like protein (regulates inflammatory gene expression in Th17 cells), and neogenin (cell adhesion). Of the inflammatory patients, there was a significant elevation of PFK/FBPase 2 (synthesis and degradation of fructose 2,6-bisphosphate), vinculin (cell-matrix adhesion) and MMP-16/MT3-MMP (matrix metalloproteinase). Conclusions The identification of a distinct signature between both phenotypes provide important biological information about the disease progression and are a good sign that a biomarker discovery platform will be capable to differentiate between inflammatory and fibrostenotic strictures from serum samples of CD patients. Funding Agencies CAG, CIHRNSERC
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".