Identification of Inhibitors of triacylglyceride accumulation in muscle cells: comparing HTS results from 1536-well plate-based and high-content platforms.
Identifieur interne : 000384 ( Main/Exploration ); précédent : 000383; suivant : 000385Identification of Inhibitors of triacylglyceride accumulation in muscle cells: comparing HTS results from 1536-well plate-based and high-content platforms.
Auteurs : Eliot Sugarman [États-Unis] ; Ada Koo ; Eigo Suyama ; Manuel E. Ruidiaz ; Susanne Heynen-Genel ; Kevin H. Nguyen ; Stefan Vasile ; Mangala M. Soundarapandian ; Rick B. Vega ; Daniel P. Kelly ; Layton H. Smith ; Siobhan MalanySource :
- Journal of biomolecular screening ; 2014.
English descriptors
- KwdEn :
- Cell Culture Techniques, Cell Line, Drug Discovery (methods), Drug Evaluation, Preclinical (methods), High-Throughput Screening Assays, Humans, Lipid Metabolism (drug effects), Muscle Fibers, Skeletal (drug effects), Muscle Fibers, Skeletal (metabolism), Reproducibility of Results, Triglycerides (metabolism).
- MESH :
- chemical , metabolism : Triglycerides.
- drug effects : Lipid Metabolism, Muscle Fibers, Skeletal.
- metabolism : Muscle Fibers, Skeletal.
- methods : Drug Discovery, Drug Evaluation, Preclinical.
- Cell Culture Techniques, Cell Line, High-Throughput Screening Assays, Humans, Reproducibility of Results.
Abstract
Excess caloric consumption leads to triacylglyceride (TAG) accumulation in tissues that do not typically store fat, such as skeletal muscle. This ectopic accumulation alters cells, contributing to the pathogenesis of metabolic syndrome, a major health problem worldwide. We developed a 1536-well assay to measure intracellular TAG accumulation in differentiating H9c2 myoblasts. For this assay, cells were incubated with oleic acid to stimulate TAG accumulation prior to adding compounds. We used Nile red as a fluorescent dye to quantify TAG content with a microplate reader. The cell nuclei were counterstained with DAPI nuclear stain to assess cell count and filter cytotoxic compounds. In parallel, we developed an image-based assay in H9c2 cells to measure lipid accumulation levels via high-content analysis, exploiting the dual-emission spectra characteristic of Nile red staining of neutral and phospholipids. Using both approaches, we successfully screened ~227,000 compounds from the National Institutes of Health library. The screening data from the plate reader and IC50 values correlated with that from the Opera QEHS cell imager. The 1536-well plate reader assay is a powerful high-throughout screening platform to identify potent inhibitors of TAG accumulation to better understand the molecular pathways involved in lipid metabolism that lead to lipotoxicity.
DOI: 10.1177/1087057113501198
PubMed: 23989452
Affiliations:
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Le document en format XML
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Ruidiaz</name></author><author><name sortKey="Heynen Genel, Susanne" sort="Heynen Genel, Susanne" uniqKey="Heynen Genel S" first="Susanne" last="Heynen-Genel">Susanne Heynen-Genel</name></author><author><name sortKey="Nguyen, Kevin H" sort="Nguyen, Kevin H" uniqKey="Nguyen K" first="Kevin H" last="Nguyen">Kevin H. Nguyen</name></author><author><name sortKey="Vasile, Stefan" sort="Vasile, Stefan" uniqKey="Vasile S" first="Stefan" last="Vasile">Stefan Vasile</name></author><author><name sortKey="Soundarapandian, Mangala M" sort="Soundarapandian, Mangala M" uniqKey="Soundarapandian M" first="Mangala M" last="Soundarapandian">Mangala M. Soundarapandian</name></author><author><name sortKey="Vega, Rick B" sort="Vega, Rick B" uniqKey="Vega R" first="Rick B" last="Vega">Rick B. Vega</name></author><author><name sortKey="Kelly, Daniel P" sort="Kelly, Daniel P" uniqKey="Kelly D" first="Daniel P" last="Kelly">Daniel P. 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Ruidiaz</name></author><author><name sortKey="Heynen Genel, Susanne" sort="Heynen Genel, Susanne" uniqKey="Heynen Genel S" first="Susanne" last="Heynen-Genel">Susanne Heynen-Genel</name></author><author><name sortKey="Nguyen, Kevin H" sort="Nguyen, Kevin H" uniqKey="Nguyen K" first="Kevin H" last="Nguyen">Kevin H. Nguyen</name></author><author><name sortKey="Vasile, Stefan" sort="Vasile, Stefan" uniqKey="Vasile S" first="Stefan" last="Vasile">Stefan Vasile</name></author><author><name sortKey="Soundarapandian, Mangala M" sort="Soundarapandian, Mangala M" uniqKey="Soundarapandian M" first="Mangala M" last="Soundarapandian">Mangala M. Soundarapandian</name></author><author><name sortKey="Vega, Rick B" sort="Vega, Rick B" uniqKey="Vega R" first="Rick B" last="Vega">Rick B. Vega</name></author><author><name sortKey="Kelly, Daniel P" sort="Kelly, Daniel P" uniqKey="Kelly D" first="Daniel P" last="Kelly">Daniel P. Kelly</name></author><author><name sortKey="Smith, Layton H" sort="Smith, Layton H" uniqKey="Smith L" first="Layton H" last="Smith">Layton H. Smith</name></author><author><name sortKey="Malany, Siobhan" sort="Malany, Siobhan" uniqKey="Malany S" first="Siobhan" last="Malany">Siobhan Malany</name></author></analytic><series><title level="j">Journal of biomolecular screening</title><idno type="e-ISSN">1552-454X</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Cell Culture Techniques</term><term>Cell Line</term><term>Drug Discovery (methods)</term><term>Drug Evaluation, Preclinical (methods)</term><term>High-Throughput Screening Assays</term><term>Humans</term><term>Lipid Metabolism (drug effects)</term><term>Muscle Fibers, Skeletal (drug effects)</term><term>Muscle Fibers, Skeletal (metabolism)</term><term>Reproducibility of Results</term><term>Triglycerides (metabolism)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Triglycerides</term></keywords><keywords scheme="MESH" qualifier="drug effects" xml:lang="en"><term>Lipid Metabolism</term><term>Muscle Fibers, Skeletal</term></keywords><keywords scheme="MESH" qualifier="metabolism" xml:lang="en"><term>Muscle Fibers, Skeletal</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Drug Discovery</term><term>Drug Evaluation, Preclinical</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Cell Culture Techniques</term><term>Cell Line</term><term>High-Throughput Screening Assays</term><term>Humans</term><term>Reproducibility of Results</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Excess caloric consumption leads to triacylglyceride (TAG) accumulation in tissues that do not typically store fat, such as skeletal muscle. This ectopic accumulation alters cells, contributing to the pathogenesis of metabolic syndrome, a major health problem worldwide. We developed a 1536-well assay to measure intracellular TAG accumulation in differentiating H9c2 myoblasts. For this assay, cells were incubated with oleic acid to stimulate TAG accumulation prior to adding compounds. We used Nile red as a fluorescent dye to quantify TAG content with a microplate reader. The cell nuclei were counterstained with DAPI nuclear stain to assess cell count and filter cytotoxic compounds. In parallel, we developed an image-based assay in H9c2 cells to measure lipid accumulation levels via high-content analysis, exploiting the dual-emission spectra characteristic of Nile red staining of neutral and phospholipids. Using both approaches, we successfully screened ~227,000 compounds from the National Institutes of Health library. The screening data from the plate reader and IC50 values correlated with that from the Opera QEHS cell imager. The 1536-well plate reader assay is a powerful high-throughout screening platform to identify potent inhibitors of TAG accumulation to better understand the molecular pathways involved in lipid metabolism that lead to lipotoxicity.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Floride</li></region></list><tree><noCountry><name sortKey="Heynen Genel, Susanne" sort="Heynen Genel, Susanne" uniqKey="Heynen Genel S" first="Susanne" last="Heynen-Genel">Susanne Heynen-Genel</name><name sortKey="Kelly, Daniel P" sort="Kelly, Daniel P" uniqKey="Kelly D" first="Daniel P" last="Kelly">Daniel P. Kelly</name><name sortKey="Koo, Ada" sort="Koo, Ada" uniqKey="Koo A" first="Ada" last="Koo">Ada Koo</name><name sortKey="Malany, Siobhan" sort="Malany, Siobhan" uniqKey="Malany S" first="Siobhan" last="Malany">Siobhan Malany</name><name sortKey="Nguyen, Kevin H" sort="Nguyen, Kevin H" uniqKey="Nguyen K" first="Kevin H" last="Nguyen">Kevin H. Nguyen</name><name sortKey="Ruidiaz, Manuel E" sort="Ruidiaz, Manuel E" uniqKey="Ruidiaz M" first="Manuel E" last="Ruidiaz">Manuel E. Ruidiaz</name><name sortKey="Smith, Layton H" sort="Smith, Layton H" uniqKey="Smith L" first="Layton H" last="Smith">Layton H. Smith</name><name sortKey="Soundarapandian, Mangala M" sort="Soundarapandian, Mangala M" uniqKey="Soundarapandian M" first="Mangala M" last="Soundarapandian">Mangala M. Soundarapandian</name><name sortKey="Suyama, Eigo" sort="Suyama, Eigo" uniqKey="Suyama E" first="Eigo" last="Suyama">Eigo Suyama</name><name sortKey="Vasile, Stefan" sort="Vasile, Stefan" uniqKey="Vasile S" first="Stefan" last="Vasile">Stefan Vasile</name><name sortKey="Vega, Rick B" sort="Vega, Rick B" uniqKey="Vega R" first="Rick B" last="Vega">Rick B. Vega</name></noCountry><country name="États-Unis"><region name="Floride"><name sortKey="Sugarman, Eliot" sort="Sugarman, Eliot" uniqKey="Sugarman E" first="Eliot" last="Sugarman">Eliot Sugarman</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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