S183. ABNORMALITIES IN SERINE/THREONINE SIGNALING NETWORKS IN SEVERE NEUROPSYCHIATRIC ILLNESS: DEVELOPMENT OF A PIPELINE TO EXPLORE ABNORMALITIES OF KINASE ACTIVITY AND IDENTIFY NOVEL TREATMENT STRATEGIES FOR SCHIZOPHRENIA
Notice bibliographique
Résumé
Abstract Background Abnormalities of cellular signaling are well characterized in neuropsychiatric illnesses, including schizophrenia. Changes in signaling pathways reflect the underlying genetic, environmental, and epigenetic perturbations driving disease phenotypes. A shortcoming of most signaling studies is a focus on one or a few protein kinases at a time, a limitation since protein kinases work in networks with other kinases, phosphatases, and regulatory molecules to effect signaling events. We addressed this challenge by employing a kinome array platform that simultaneously measures protein kinase activity at hundreds of reporter peptide substrates. We then developed a novel bioinformatics pipeline to identify protein kinase nodes, signaling networks, upstream biological pathways, and drug candidates that “reverse” kinomic disease signatures. Methods Postmortem DLPFC brain samples from subjects with schizophrenia (n = 20 per group, 10 males and 10 females per group), were compared to age, PMI and pH matched control subjects (n = 20 per group, 10 males and 10 females per group) using the Pamgene12 serine/threonine kinome array chip. Samples were pooled by diagnosis and gender, and run in triplicate. The R-shiny app KRSA was created to automate assignment of kinases, perform permutation analyses, identify biological pathways, and connect to iLINCs for identification of drugs that reverse kinomic disease signatures. We also performed targeted confirmation studies using specific kinase activity assays, QPCR, and western blot analyses. Results We identified unique and common kinase nodes for each diagnostic group. Several of the nodes (for example AKT) are well characterized in schizophrenia, while others have not previously been identified (such as AMPK). We used AMPK KD cultures and AMPK KO brain tissues to demonstrate the validity if the kinome array for this protein kinase. We used standard kinase activity assays for AMPK and found decreased activity for AMPK (P < 0.05). We also found decreased expression of transcripts for the regulatory subunits of AMPK (P < 0.05). We identified several unique biological pathways, as well as candidate drugs, associated with the disease signature in schizophrenia. Discussion Our results confirm well characterized signaling defects in severe neuropsychiatric illness, and identify novel signaling nodes for further study. Confirmation studies for AMPK kinase show significant changes in expression and activity of this kinase, suggesting perturbation of energy sensing and production pathways in schizophrenia. Bioenergetic pathways may be targeted by myriad mechanisms, and we identified several drug candidates that might help restore this pathway in afflicted persons. Overall our novel workflow and pipeline provides a promising new avenue for understanding the complex signaling perturbations found in brain diseases and may provide new leads for developing treatments for schizophrenia and other cognitive disorders.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,003 |
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 ».