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基于L1000的连接图谱药物筛选方法及其在COVID-19中的应用

Drug Screening Approach Using L1000-Based Connectivity Map and Its Application to COVID-19.

作者信息

Asano Takaharu, Chelvanambi Sarvesh, Decano Julius L, Whelan Mary C, Aikawa Elena, Aikawa Masanori

机构信息

Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.

Center for Excellence in Vascular Biology, Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.

出版信息

Front Cardiovasc Med. 2022 Mar 24;9:842641. doi: 10.3389/fcvm.2022.842641. eCollection 2022.


DOI:10.3389/fcvm.2022.842641
PMID:35402570
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8989014/
Abstract

Conventional drug screening methods search for a limited number of small molecules that directly interact with the target protein. This process can be slow, cumbersome and has driven the need for developing new drug screening approaches to counter rapidly emerging diseases such as COVID-19. We propose a pipeline for drug repurposing combining drug candidate identification followed by characterization of these candidates. We first identified a gene target of interest, the entry receptor for the SARS-CoV-2 virus, angiotensin converting enzyme 2 (ACE2). Next, we employed a gene expression profile database, L1000-based Connectivity Map to query gene expression patterns in lung epithelial cells, which act as the primary site of SARS-CoV-2 infection. Using gene expression profiles from 5 different lung epithelial cell lines, we computationally identified 17 small molecules that were predicted to decrease ACE2 expression. We further performed a streamlined validation in the normal human epithelial cell line BEAS-2B to demonstrate that these compounds can indeed decrease ACE2 surface expression and to profile cell health and viability upon drug treatment. This proposed pipeline combining drug compound identification and expression and viability characterization in relevant cell types can aid in the repurposing of FDA-approved drugs to combat rapidly emerging diseases.

摘要

传统的药物筛选方法是寻找有限数量的能直接与靶蛋白相互作用的小分子。这个过程可能缓慢、繁琐,因此催生了开发新的药物筛选方法以应对如新冠病毒疾病等迅速出现的疾病的需求。我们提出了一个药物重新利用流程,包括药物候选物鉴定以及随后对这些候选物的表征。我们首先确定了一个感兴趣的基因靶点,即严重急性呼吸综合征冠状病毒2(SARS-CoV-2)病毒的进入受体血管紧张素转换酶2(ACE2)。接下来,我们利用一个基因表达谱数据库,即基于L1000的连通性图谱,来查询肺上皮细胞中的基因表达模式,肺上皮细胞是SARS-CoV-2感染的主要部位。利用来自5种不同肺上皮细胞系的基因表达谱,我们通过计算确定了17种预计会降低ACE2表达的小分子。我们进一步在正常人上皮细胞系BEAS-2B中进行了简化验证,以证明这些化合物确实能降低ACE2的表面表达,并分析药物处理后细胞的健康状况和活力。这种将药物化合物鉴定与相关细胞类型中的表达及活力表征相结合的提议流程,有助于重新利用美国食品药品监督管理局(FDA)批准的药物来对抗迅速出现的疾病。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/889a/8989014/c89ef448b831/fcvm-09-842641-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/889a/8989014/fe28f55a0926/fcvm-09-842641-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/889a/8989014/e73ae934f021/fcvm-09-842641-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/889a/8989014/39794a8ad7c4/fcvm-09-842641-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/889a/8989014/f96502a23398/fcvm-09-842641-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/889a/8989014/e5d621f496fa/fcvm-09-842641-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/889a/8989014/c89ef448b831/fcvm-09-842641-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/889a/8989014/fe28f55a0926/fcvm-09-842641-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/889a/8989014/e73ae934f021/fcvm-09-842641-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/889a/8989014/39794a8ad7c4/fcvm-09-842641-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/889a/8989014/f96502a23398/fcvm-09-842641-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/889a/8989014/e5d621f496fa/fcvm-09-842641-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/889a/8989014/c89ef448b831/fcvm-09-842641-g0006.jpg

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[3]
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[4]
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[5]
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[6]
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[7]
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[8]
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