Uncovering Novel Substrates and Functions for the Calcineurin Phosphatase in Human Cells
Notice bibliographique
Résumé
Protein phosphatases play essential roles in every signaling pathway; however, systems‐level understanding of phosphatase signaling networks is lacking due to the inherent challenges associated with proteome‐wide identification of their substrates. Calcineurin (CN) is the conserved Ca 2+ /calmodulin‐activated protein phosphatase and target of the widely prescribed immunosuppressant drugs, FK506 and Cyclosporin A. CN is ubiquitously expressed and plays critical roles in the immune, nervous, skeletal and cardiovascular systems, as well as during development. However, only 50 substrates are currently attributed to this phosphatase. CN utilizes conserved docking surfaces to interact with substrates via Short Linear Motifs (SLiMs) termed PxIxIT and LxVP, which occur preferentially in intrinsically disordered domains and are challenging to identify due to sequence degeneracy and low affinity for CN. We are applying novel experimental and computational approaches to systematically identify CN‐interacting SLiMs within the human proteome with the goal of ultimately establishing the human CN signaling network. We directly identified novel CN‐binding sequences by performing unbiased peptide phage display selections with human CN using a library containing all predicted disordered regions in the human proteome. These SLiM sequences directly identified many novel candidate CN substrates, including the nucleoporin, NUP153. We have shown that NUP153 is directly dephosphorylated by CN in vitro and contains a conserved PxIxIT sequence that is required for interaction with CN in vivo . Furthermore, a NUP153 PxIxIT mutant is dephosphorylated less efficiently by CN in vitro and shows altered dephosphorylation in vivo . To further expand our identification of novel CN substrates, we employed two additional approaches: 1. A Position‐Specific Scoring Matrix (PSSM) generated using the novel CN‐binding SLiMs identified by phage display and 2. Proximity‐dependent biotinylation (BioID) followed by MS analysis in HEK293 cells. Both of these methods identified several nuclear pore components in addition to NUP153 as high confidence CN interactors, indicating a previously uncharacterized role for CN in regulating nuclear pore structure and/or function. In addition to nuclear pore proteins, these combined experimental and computational approaches have identified a host of novel candidate substrates for CN including ion channels, kinases, transcription factors and receptors. The significant overlap between these two datasets underscores the strength of these independent approaches in identifying novel CN substrates in human cells. Together, these studies suggest new points of cross‐talk between CN and other signaling pathways in human cells and will ultimately allow us to establish the first comprehensive signaling network for this critical Ca 2+ ‐dependent regulator of human health. Support or Funding Information F32GM120916‐01 to CP WigingtonR01GM119336‐01 to MS Cyert
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 ».