1. Depression patient-derived cortical neurons reveal potential biomarkers for antidepressant response
- Author
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Vitaly Lerner, Talia Cohen Solal, Dana Kroitorou, Shiri Ron, Barbara Corneo, Yishai Avior, Claudia Albeldas, Erez Nitzan, and Daphna Laifenfeld
- Subjects
0301 basic medicine ,Stem cells ,Bioinformatics ,Patient response ,Predictive markers ,Article ,lcsh:RC321-571 ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,0302 clinical medicine ,Medicine ,Humans ,Clinical genetics ,lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry ,Biological Psychiatry ,Depression (differential diagnoses) ,Bupropion ,Neurons ,Depressive Disorder, Major ,business.industry ,Depression ,Cortical neurons ,medicine.disease ,Antidepressive Agents ,Psychiatry and Mental health ,030104 developmental biology ,Treatment Outcome ,Lymphoblastoid cell ,Potential biomarkers ,Antidepressant ,Major depressive disorder ,business ,030217 neurology & neurosurgery ,Biomarkers ,medicine.drug - Abstract
Major depressive disorder is highly prevalent worldwide and has been affecting an increasing number of people each year. Current first line antidepressants show merely 37% remission, and physicians are forced to use a trial-and-error approach when choosing a single antidepressant out of dozens of available medications. We sought to identify a method of testing that would provide patient-specific information on whether a patient will respond to a medication using in vitro modeling. Patient-derived lymphoblastoid cell lines from the Sequenced Treatment Alternatives to Relieve Depression study were used to rapidly generate cortical neurons and screen them for bupropion effects, for which the donor patients showed remission or non-remission. We provide evidence for biomarkers specific for bupropion response, including synaptic connectivity and morphology changes as well as specific gene expression alterations. These biomarkers support the concept of personalized antidepressant treatment based on in vitro platforms and could be utilized as predictors to patient response in the clinic.
- Published
- 2021