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Drug Discovery Platform Targeting M. tuberculosis with Human Embryonic Stem Cell-Derived Macrophages.

Hyo-Won Han | Hyang-Hee Seo | Hye-Yeong Jo | Hyeong-Jun Han | Virgínia C A Falcão | Vincent Delorme | Jinyeong Heo | David Shum | Jang-Hoon Choi | Jin-Moo Lee | Seung Hun Lee | Hye-Ryeon Heo | Seok-Ho Hong | Mi-Hyun Park | Rajesh K Thimmulappa | Jung-Hyun Kim
Stem cell reports | 2019

A major limitation in anti-tuberculosis drug screening is the lack of reliable and scalable models for homogeneous human primary macrophage cells of non-cancer origin. Here we report a modified protocol for generating homogeneous populations of macrophage-like cells from human embryonic stem cells. The induced macrophages, referred to as iMACs, presented similar transcriptomic profiles and characteristic immunological features of classical macrophages and were permissive to viral and bacterial infection, in particular Mycobacterium tuberculosis (Mtb). More importantly, iMAC production was amenable to scale up. To evaluate iMAC efficiency in high-throughput anti-tuberculosis drug screening, we performed a phenotypic screening against intracellular Mtb, involving a library of 3,716 compounds that included FDA-approved drugs and other bioactive compounds. Our primary screen identified 120 hits, which were validated in a secondary screen by dose-intracellular and -extracellular Mtb assays. Our confirmatory studies identified a novel anti-Mtb compound, 10-DEBC, also showing activity against drug-resistant strains.

Pubmed ID: 31680058

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PRISM (tool)

RRID:SCR_005375

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.

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