Identification of Inhibitors of triacylglyceride accumulation in muscle cells: comparing HTS results from 1536-well plate-based and high-content platforms.
Identifieur interne : 000211 ( PubMed/Corpus ); précédent : 000210; suivant : 000212Identification of Inhibitors of triacylglyceride accumulation in muscle cells: comparing HTS results from 1536-well plate-based and high-content platforms.
Auteurs : Eliot Sugarman ; 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
Links to Exploration step
pubmed:23989452Le 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. Kelly</name></author><author><name sortKey="Smith, Layton H" sort="Smith, Layton H" uniqKey="Smith L" first="Layton H" last="Smith">Layton H. 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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><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">23989452</PMID><DateCreated><Year>2013</Year><Month>12</Month><Day>17</Day></DateCreated><DateCompleted><Year>2014</Year><Month>09</Month><Day>11</Day></DateCompleted><DateRevised><Year>2015</Year><Month>04</Month><Day>23</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1552-454X</ISSN><JournalIssue CitedMedium="Internet"><Volume>19</Volume><Issue>1</Issue><PubDate><Year>2014</Year><Month>Jan</Month></PubDate></JournalIssue><Title>Journal of biomolecular screening</Title><ISOAbbreviation>J Biomol Screen</ISOAbbreviation></Journal><ArticleTitle>Identification of Inhibitors of triacylglyceride accumulation in muscle cells: comparing HTS results from 1536-well plate-based and high-content platforms.</ArticleTitle><Pagination><MedlinePgn>77-87</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1177/1087057113501198</ELocationID><Abstract><AbstractText>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.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Sugarman</LastName><ForeName>Eliot</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>1Conrad Prebys Center for Chemical Genomics, Sanford Burham Medical Research Institute, Orlando, FL, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Koo</LastName><ForeName>Ada</ForeName><Initials>A</Initials></Author><Author ValidYN="Y"><LastName>Suyama</LastName><ForeName>Eigo</ForeName><Initials>E</Initials></Author><Author ValidYN="Y"><LastName>Ruidiaz</LastName><ForeName>Manuel E</ForeName><Initials>ME</Initials></Author><Author ValidYN="Y"><LastName>Heynen-Genel</LastName><ForeName>Susanne</ForeName><Initials>S</Initials></Author><Author ValidYN="Y"><LastName>Nguyen</LastName><ForeName>Kevin H</ForeName><Initials>KH</Initials></Author><Author ValidYN="Y"><LastName>Vasile</LastName><ForeName>Stefan</ForeName><Initials>S</Initials></Author><Author ValidYN="Y"><LastName>Soundarapandian</LastName><ForeName>Mangala M</ForeName><Initials>MM</Initials></Author><Author ValidYN="Y"><LastName>Vega</LastName><ForeName>Rick B</ForeName><Initials>RB</Initials></Author><Author ValidYN="Y"><LastName>Kelly</LastName><ForeName>Daniel P</ForeName><Initials>DP</Initials></Author><Author ValidYN="Y"><LastName>Smith</LastName><ForeName>Layton H</ForeName><Initials>LH</Initials></Author><Author ValidYN="Y"><LastName>Malany</LastName><ForeName>Siobhan</ForeName><Initials>S</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>R03 DA026213</GrantID><Acronym>DA</Acronym><Agency>NIDA NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>R03DA026213-01</GrantID><Acronym>DA</Acronym><Agency>NIDA NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>R24 DK092781</GrantID><Acronym>DK</Acronym><Agency>NIDDK NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>U54 HG005033</GrantID><Acronym>HG</Acronym><Agency>NHGRI NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>U54 HG005033</GrantID><Acronym>HG</Acronym><Agency>NHGRI NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2013</Year><Month>08</Month><Day>29</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>J Biomol Screen</MedlineTA><NlmUniqueID>9612112</NlmUniqueID><ISSNLinking>1087-0571</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D014280">Triglycerides</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Prog Lipid Res. 2012 Jan;51(1):36-49</RefSource><PMID Version="1">22120643</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Clin Invest. 2009 Mar;119(3):573-81</RefSource><PMID Version="1">19188683</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Med Chem. 2012 Feb 23;55(4):1751-7</RefSource><PMID Version="1">22263872</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Eur J Pharmacol. 2013 May 5;707(1-3):140-6</RefSource><PMID Version="1">23524088</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>JAMA. 2004 Jun 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Version="1">12055349</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>N Engl J Med. 2005 Mar 17;352(11):1138-45</RefSource><PMID Version="1">15784668</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Cardiol Rev. 2006 Sep-Oct;14(5):238-58</RefSource><PMID Version="1">16924165</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Diabetes. 2006 Dec;55 Suppl 2:S9-S15</RefSource><PMID Version="1">17130651</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Med Chem. 2010 Apr 8;53(7):2719-40</RefSource><PMID Version="1">20131845</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Biomol Screen. 2012 Mar;17(3):394-400</RefSource><PMID Version="1">22116976</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D018929">Cell Culture Techniques</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D002460">Cell Line</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D055808">Drug Discovery</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D004353">Drug Evaluation, Preclinical</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D057166">High-Throughput Screening Assays</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D006801">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D050356">Lipid Metabolism</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000187">drug effects</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D018485">Muscle Fibers, Skeletal</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000187">drug 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screening</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="aheadofprint"><Year>2013</Year><Month>8</Month><Day>29</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2013</Year><Month>8</Month><Day>31</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2013</Year><Month>8</Month><Day>31</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2014</Year><Month>9</Month><Day>12</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">23989452</ArticleId><ArticleId IdType="pii">1087057113501198</ArticleId><ArticleId IdType="doi">10.1177/1087057113501198</ArticleId><ArticleId IdType="pmc">PMC3901053</ArticleId><ArticleId IdType="mid">NIHMS541599</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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