Patient iPSC-derived neurons reveal mechanisms underlying antidepressant response: a potential diagnostic tool

作者
S. Shohat Koren,D. Kroitorou,Claudia Albeldas,Aleksandra Kugel,Nadav Askari,T. Cohen Solal,D. Laifenfeld
出处
期刊:European Psychiatry [Cambridge University Press]
卷期号:66 (S1): S92-S93
标识
DOI:10.1192/j.eurpsy.2023.274
摘要

Introduction Depression is a leading cause of disability worldwide despite dozens of approved antidepressants. There are currently no clear guidelines to assist the physician in their choice of drug, with existing tools limited to pharmacogenetics that have shown suboptimal response prediction outcomes resulting in a subscription process that is largely a trial and error one. Consequently, the majority of depressed patients do not respond to their first prescribed antidepressant, with >30% not responding to subsequent drugs. We report here on molecular readouts from an in vitro-based platform that provides patient-specific information on antidepressant mechanisms using cortical neurons derived individually from each patient. Objectives To assess gene expression differences in prefrontal cortex neurons derived from responders and non-responders to two commonly used antidepressants, the selective serotonin reuptake inhibitor Citalopram and the atypical antidepressant Bupropion. Methods Patient-derived lymphoblastoid cell lines from the Sequenced Treatment Alternatives to Relieve Depression (STARD) study with known response to Citalopram or Bupropion were reprogrammed and then differentiated to cortical neurons. Differential gene expression analysis was preformed to identify genes that are differentially expressed between drug responders and non-responders. Results Significant differential expression was shown in 359 genes between Bupropion responders and non-responders (Fig1A) and 12 genes between Citalopram responders and non-responders (Fig1B). Clustering on the differentially expressed genes showed high agreement with the known response to both drugs (Fig1). Functional enrichment analysis revealed biologically relevant pathways that differ between responders and non-responders in Bupropion versus Citalopram. Image: Figure 1. Heatmap of the expression of genes that show significant differential expression between neurons derived from Bupropion (A) and Citalopram (B) responders and non-responders. Color is the scaled gene expression; lines are genes and columns are samples. Column side colors represent the known response of the patient. Colum and line dendrograms are unsupervised hierarchical clustering. Conclusions Gene expression patterns of neurons derived from patients with depression differ according to their response to two common antidepressants from different groups. The identification of distinct drug response dependent expression patterns in derived neurons can help elucidate mechanisms underlying antidepressant activity, supporting new drug development and response prediction. Disclosure of Interest None Declared

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
户户得振完成签到,获得积分10
2秒前
健忘的水池完成签到 ,获得积分10
2秒前
3秒前
gun去学习完成签到,获得积分10
4秒前
朴素如之完成签到,获得积分10
4秒前
文静冰露完成签到,获得积分10
4秒前
无23223发布了新的文献求助10
5秒前
5秒前
5秒前
5秒前
Akim应助Judith采纳,获得10
6秒前
jmlx发布了新的文献求助10
7秒前
hhh完成签到,获得积分10
7秒前
7秒前
edge发布了新的文献求助10
7秒前
8秒前
8秒前
fcyyc完成签到,获得积分10
8秒前
传统的松鼠完成签到 ,获得积分10
9秒前
molihuakai应助不安的雪萍采纳,获得30
9秒前
9秒前
9秒前
杜凯敏完成签到,获得积分20
9秒前
孔德阳完成签到,获得积分10
10秒前
edge发布了新的文献求助10
10秒前
领导范儿应助辛勤的咩采纳,获得10
10秒前
柠七完成签到,获得积分20
11秒前
科研通AI6.4应助安静乐瑶采纳,获得10
11秒前
fcyyc发布了新的文献求助10
11秒前
12秒前
kg5g发布了新的文献求助10
12秒前
12秒前
赵赶超应助SSS采纳,获得10
12秒前
13秒前
乔治韦斯莱完成签到 ,获得积分10
14秒前
14秒前
柠好发布了新的文献求助10
14秒前
clyde凌丫完成签到 ,获得积分10
15秒前
17秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Reducing Compassion Fatigue, Secondary Traumatic Stress and Burnout 600
Comparative Elite Sport Development Systems, Structures and Public Policy 600
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Auslegungsgeschichte 500
Cosmos as Art Object: Studies in Plato's Timaeus and Other Dialogues 500
What is the Future of Psychotherapy in Digital Age? Technology, AI Bots, and Psychotherapy after Covid 444
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7636785
求助须知:如何正确求助?哪些是违规求助? 9210552
关于积分的说明 19756125
捐赠科研通 7204274
什么是DOI,文献DOI怎么找? 3275534
关于科研通互助平台的介绍 2437291
邀请新用户注册赠送积分活动 2272660