Proteomics and phosphoproteomics profiling of the co-formulation of type I and II interferons, HeberFERON, in the glioblastoma-derived cell line U-87 MG
Notice bibliographique
Résumé
Abstract HeberFERON is a co-formulation of Interferon (IFN)-α2b and IFN-γ in synergic proportions, with a demonstrated effect on skin cancer and other solid tumors. It has antiproliferative effects over glioblastoma multiform (GBM) clones and cell lines in culture, including U-87 MG. Omics studies in U-87 MG showed distinctive expression patterns compared to individual IFNs. Kinase signaling pathways dysregulation can also contribute to HeberFERON effects. Here, we report the first label-free quantitative proteomic and phosphoproteomic analyses to evaluate changes induced by HeberFERON after 72h incubation of U-87 MG cell line. LC-MS/MS analysis identified 7627 proteins with a fold change >2 (p<0.05); 122 and 211 were down- and up-regulated by HeberFERON, respectively. We identified 23549 peptides (5692 proteins) and 8900 phosphopeptides, 412 of these phosphopeptides (359 proteins) were differentially modified with fold change >2 (p<0.05). Proteomic enrichment analysis showed IFN signaling and its control, together to direct and indirect antiviral mechanisms were the main modulated processes. Enrichment analysis of phosphoproteome pointed to the cell cycle, cytoskeleton organization, translation and RNA splicing, autophagy, and DNA repair as biological processes represented. There is a high interconnection of phosphoproteins in a molecular network, where mTOR occupies a centric hub. HeberFERON regulates many phosphosites newly reported or with no clear association to kinases. Of interest is phosphosites increasing phosphorylation were mainly modified by CDK and ERK kinases, thus new cascades regulations can be determining the antiproliferation outcome. Our results contribute to a better mechanistic understanding of HeberFERON in the context of GBM. Significance of the Study HeberFERON is a co-formulation of IFN-α2b and -γ in synergic proportion, registered for skin basal cell carcinoma treatment, also demonstrating clinical effect over solid tumors, including GBM. GBM is a very lethal tumor, protected by the blood-brain barrier (BBB), highly mutated in proliferative signaling pathways with little treatment success. Interferons have been widely used in cancer; they pass BBB and act at JAK/STAT, PI3K/AKT/mTOR, and MAPKs cascades. We observed antiproliferative effects over GBM clones and cell lines in culture. U-87 MG is used as a model to understand the HeberFERON mechanism of action in GBM. We completed the first proteomic and label-free quantitative phosphoproteomic analysis after incubation of U-87 MG cell line with HeberFERON for 72h. The main contribution of this article is the description of phosphosites regulated in proteins participating in cell cycle, cytoskeleton organization, translation, autophagy, and DNA repair in a highly interconnected molecular network, where mTOR occupies a centric hub. Together with reported phosphosites, we described new ones and others with no associated kinases. Increased phosphorylation is mainly accounted by CDK and ERK kinases pointing to possibly new cascades regulations. This knowledge will contribute to the functional understanding of HeberFERON in GBM joined to general regulatory mechanisms in cancer cells.
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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 | 0,000 | 0,000 |
| 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) | 0,000 | 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 ».