A38 INVESTIGATION OF POST-TRANSLATIONAL MODIFICATIONS IN SERUM OF CROHN’S DISEASE PATIENTS USING A PROTEOMICS APPROACH
Notice bibliographique
Résumé
Abstract Background Canada has one of the highest prevalences of Crohn’s disease (CD) worldwide. More specifically, fibrostenotic CD is a phenotype with prolonged chronic inflammation and fibrotic strictures often resistant to anti-inflammatory therapies and characterized by luminal narrowing that ultimately requires surgery. Proteins play an essential role in disease pathogenesis, and post-translational modifications (PTMs) can alter their properties. PTMs have been frequently implicated in human diseases. However, they have yet to be explored in the context of CD, which could lead to new avenues for a better understanding of disease mechanisms and the discovery of biomarkers. Purpose Identify post-translational modifications in serum proteins of CD patients. Method Serum samples from patients with strictures or inflammatory phenotype (without strictures) (n=4 per group), as diagnosed by intestinal ultrasound, were analyzed using a shotgun-proteomics approach. Protein identification and PTM prediction were performed with FragPipe. Identified mass shifts determined by an open search in FragPipe were mapped to possible PTMs and confirmed via unimod.org. Statistical significance analysis was performed with MSstatsPTM. Result(s) The prediction analysis identified 363 potential modification sites, including artifacts and chemical derivatives. The addition of all potential PTMs in the analysis would lead to false positives; therefore, it was selected five of the most abundant mass shifts mapped to true PTMs: cysteine oxidation, serine methylation, and three modifications of the protein n-termini (formaldehyde adduct, carbamylation, and formylation). Standard proteomics analysis identified 3635 unique peptides and 317 unique proteins. The addition of the predicted PTMs increased the number of peptides by 9.8%, reaching 3994 unique sequences. Interestingly, a very subtle increase was observed on the protein level, where only two additional proteins were identified. Of the PTMs identified, methylation of a serine residue on the variable chain of immunoglobulin (IGLV1-47) was statistically enriched in inflammatory samples (5.74 fold change, adj. p-value = 0.041). The variable chain participates in the antigen recognition process, and modification of its amino acids could impact antibody specificity. Additionally, structuring patients showed two modifications on thrombin: oxidation of cysteine and methylation of serine. Thrombin was previously shown to be elevated in CD patients compared to healthy controls. As both modifications were not present in inflammatory patients, they constitute potential biomarkers for specific diagnosis of the structuring disease. Conclusion(s) The observed gain in peptide identification demonstrates the diversification promoted by PTMs and exhibits their importance in proteomics studies. Even though the identified modifications require further validation, they can shed light on new players of CD pathogenesis and suggest novel biomarkers for disease diagnosis. Disclosure of Interest None Declared
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».