Integrative Genomics-Based Discovery of Novel Regulators of the Innate Antiviral Response
Publication Date
October 20, 2015
Journal
PLOS Computational Biology
Authors
Robin Van Der Lee, Qian Feng, Martijn A. Langereis, Rob Ter Horst, et al
Volume
11
Issue
10
Pages
e1004553
DOI
http://doi.org/10.1371/journal.pcbi.1004553
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004553
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/26485378
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4618338
Europe PMC
http://europepmc.org/abstract/MED/26485378
Web of Science
000364399700077
Scopus
84946093899
Mendeley
http://www.mendeley.com/research/integrative-genomicsbased-discovery-novel-regulators-innate-antiviral-response-4
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Mendeley | Further Information

{"title"=>"Integrative Genomics-Based Discovery of Novel Regulators of the Innate Antiviral Response", "type"=>"journal", "authors"=>[{"first_name"=>"Robin", "last_name"=>"van der Lee", "scopus_author_id"=>"6603094372"}, {"first_name"=>"Qian", "last_name"=>"Feng", "scopus_author_id"=>"57188738901"}, {"first_name"=>"Martijn A.", "last_name"=>"Langereis", "scopus_author_id"=>"10639551100"}, {"first_name"=>"Rob", "last_name"=>"ter Horst", "scopus_author_id"=>"56940539400"}, {"first_name"=>"Radek", "last_name"=>"Szklarczyk", "scopus_author_id"=>"8868473100"}, {"first_name"=>"Mihai G.", "last_name"=>"Netea", "scopus_author_id"=>"35378641700"}, {"first_name"=>"Arno C.", "last_name"=>"Andeweg", "scopus_author_id"=>"6602702173"}, {"first_name"=>"Frank J M", "last_name"=>"van Kuppeveld", "scopus_author_id"=>"7004255109"}, {"first_name"=>"Martijn A.", "last_name"=>"Huynen", "scopus_author_id"=>"7005639383"}], "year"=>2015, "source"=>"PLoS Computational Biology", "identifiers"=>{"scopus"=>"2-s2.0-84946093899", "pui"=>"606741219", "doi"=>"10.1371/journal.pcbi.1004553", "issn"=>"15537358", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "sgr"=>"84946093899", "pmid"=>"26485378"}, "id"=>"e95ff2cc-78ec-3c5b-be66-f789d9a56852", "abstract"=>"The RIG-I-like receptor (RLR) pathway is essential for detecting cytosolic viral RNA to trigger the production of type I interferons (IFNα/β) that initiate an innate antiviral response. Through systematic assessment of a wide variety of genomics data, we discovered 10 molecular signatures of known RLR pathway components that collectively predict novel members. We demonstrate that RLR pathway genes, among others, tend to evolve rapidly, interact with viral proteins, contain a limited set of protein domains, are regulated by specific transcription factors, and form a tightly connected interaction network. Using a Bayesian approach to integrate these signatures, we propose likely novel RLR regulators. RNAi knockdown experiments revealed a high prediction accuracy, identifying 94 genes among 187 candidates tested (~50%) that affected viral RNA-induced production of IFNβ. The discovered antiviral regulators may participate in a wide range of processes that highlight the complexity of antiviral defense (e.g. MAP3K11, CDK11B, PSMA3, TRIM14, HSPA9B, CDC37, NUP98, G3BP1), and include uncharacterized factors (DDX17, C6orf58, C16orf57, PKN2, SNW1). Our validated RLR pathway list (http://rlr.cmbi.umcn.nl/), obtained using a combination of integrative genomics and experiments, is a new resource for innate antiviral immunity research.", "link"=>"http://www.mendeley.com/research/integrative-genomicsbased-discovery-novel-regulators-innate-antiviral-response-4", "reader_count"=>25, "reader_count_by_academic_status"=>{"Researcher"=>9, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>8, "Student > Postgraduate"=>1, "Student > Master"=>3, "Student > Bachelor"=>2, "Unspecified"=>1}, "reader_count_by_user_role"=>{"Researcher"=>9, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>8, "Student > Postgraduate"=>1, "Student > Master"=>3, "Student > Bachelor"=>2, "Unspecified"=>1}, "reader_count_by_subject_area"=>{"Biochemistry, Genetics and Molecular Biology"=>6, "Agricultural and Biological Sciences"=>11, "Medicine and Dentistry"=>2, "Neuroscience"=>2, "Chemistry"=>1, "Computer Science"=>1, "Immunology and Microbiology"=>1, "Unspecified"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>2}, "Chemistry"=>{"Chemistry"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>11}, "Computer Science"=>{"Computer Science"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>6}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"Netherlands"=>1}, "group_count"=>2}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2367119", "https://ndownloader.figshare.com/files/2367120", "https://ndownloader.figshare.com/files/2367121", "https://ndownloader.figshare.com/files/2367122", "https://ndownloader.figshare.com/files/2367123", "https://ndownloader.figshare.com/files/2367124", "https://ndownloader.figshare.com/files/2367125", "https://ndownloader.figshare.com/files/2367126", "https://ndownloader.figshare.com/files/2367128", "https://ndownloader.figshare.com/files/2367129", "https://ndownloader.figshare.com/files/2367130", "https://ndownloader.figshare.com/files/2367131", "https://ndownloader.figshare.com/files/2367132", "https://ndownloader.figshare.com/files/2367133", "https://ndownloader.figshare.com/files/2367134", "https://ndownloader.figshare.com/files/2367135", "https://ndownloader.figshare.com/files/2367136", "https://ndownloader.figshare.com/files/2367137", "https://ndownloader.figshare.com/files/2367138", "https://ndownloader.figshare.com/files/2367139", "https://ndownloader.figshare.com/files/2367140", "https://ndownloader.figshare.com/files/2367141", "https://ndownloader.figshare.com/files/2367142"], "description"=>"<div><p>The RIG-I-like receptor (RLR) pathway is essential for detecting cytosolic viral RNA to trigger the production of type I interferons (IFNα/β) that initiate an innate antiviral response. Through systematic assessment of a wide variety of genomics data, we discovered 10 molecular signatures of known RLR pathway components that collectively predict novel members. We demonstrate that RLR pathway genes, among others, tend to evolve rapidly, interact with viral proteins, contain a limited set of protein domains, are regulated by specific transcription factors, and form a tightly connected interaction network. Using a Bayesian approach to integrate these signatures, we propose likely novel RLR regulators. RNAi knockdown experiments revealed a high prediction accuracy, identifying 94 genes among 187 candidates tested (~50%) that affected viral RNA-induced production of IFNβ. The discovered antiviral regulators may participate in a wide range of processes that highlight the complexity of antiviral defense (e.g. <i>MAP3K11</i>, <i>CDK11B</i>, <i>PSMA3</i>, <i>TRIM14</i>, <i>HSPA9B</i>, <i>CDC37</i>, <i>NUP98</i>, <i>G3BP1</i>), and include uncharacterized factors (<i>DDX17</i>, <i>C6orf58</i>, <i>C16orf57</i>, <i>PKN2</i>, <i>SNW1</i>). Our validated RLR pathway list (<a href=\"http://rlr.cmbi.umcn.nl/\" target=\"_blank\">http://rlr.cmbi.umcn.nl/</a>), obtained using a combination of integrative genomics and experiments, is a new resource for innate antiviral immunity research.</p></div>", "links"=>[], "tags"=>["RLR pathway components", "genomics data", "pkn", "SNW", "187 candidates", "3k", "Bayesian approach", "RLR pathway genes", "Protein domains", "novel regulators", "prediction accuracy", "3BP", "DDX", "novel members", "uncharacterized factors", "RNAi knockdown experiments", "RLR pathway list", "transcription factors", "11b", "Innate Antiviral Response", "immunity research", "9b", "hspa", "94 genes", "trim", "interaction network", "novel RLR regulators", "cdc", "cdk", "nup", "IFN β.", "psma"], "article_id"=>1581175, "categories"=>["Uncategorised"], "users"=>["Robin van der Lee", "Qian Feng", "Martijn A. Langereis", "Rob ter Horst", "Radek Szklarczyk", "Mihai G. Netea", "Arno C. Andeweg", "Frank J. M. van Kuppeveld", "Martijn A. Huynen"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1004553.s001", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s002", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s003", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s004", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s005", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s006", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s007", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s008", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s009", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s010", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s011", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s012", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s013", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s014", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s015", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s016", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s017", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s018", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s019", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s020", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s021", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s022", "https://dx.doi.org/10.1371/journal.pcbi.1004553.s023"], "stats"=>{"downloads"=>83, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Integrative_Genomics_Based_Discovery_of_Novel_Regulators_of_the_Innate_Antiviral_Response_/1581175", "title"=>"Integrative Genomics-Based Discovery of Novel Regulators of the Innate Antiviral Response", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-10-20 04:25:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/2367079"], "description"=>"<p>Human proteins are represented by circles, viral proteins by rounded rectangles (purple nodes). Green nodes represent known components of the RLR pathway. Orange nodes (DDX17 and SNW1) are novel RIG-I pathway components discovered in our study, which are connected to the RLR network through interactions with the green nodes. Edges between human proteins represent physical interactions (both low- and high-throughput) obtained from BioGRID Release 3.3 [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004553#pcbi.1004553.ref054\" target=\"_blank\">54</a>]. Interactions between human and viral proteins were obtained from the PHISTO database (29 Sep. 2014) [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004553#pcbi.1004553.ref028\" target=\"_blank\">28</a>]. See <b><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004553#pcbi.1004553.s001\" target=\"_blank\">S1 Fig</a></b> for a more complete representation of the RLR pathway containing the curated set of 49 known RLR genes. LaCV, La Crosse virus; EBV, Epstein-Barr virus; SFSV, Sandfly fever Sicilian virus; PRRSV, Porcine reproductive and respiratory syndrome virus; HPV, Human papillomavirus.</p>", "links"=>[], "tags"=>["RLR pathway components", "genomics data", "pkn", "SNW", "187 candidates", "3k", "Bayesian approach", "RLR pathway genes", "Protein domains", "novel regulators", "prediction accuracy", "3BP", "DDX", "novel members", "uncharacterized factors", "RNAi knockdown experiments", "RLR pathway list", "transcription factors", "11b", "Innate Antiviral Response", "immunity research", "9b", "hspa", "94 genes", "trim", "interaction network", "novel RLR regulators", "cdc", "cdk", "nup", "IFN β.", "psma"], "article_id"=>1581140, "categories"=>["Uncategorised"], "users"=>["Robin van der Lee", "Qian Feng", "Martijn A. Langereis", "Rob ter Horst", "Radek Szklarczyk", "Mihai G. Netea", "Arno C. Andeweg", "Frank J. M. van Kuppeveld", "Martijn A. Huynen"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004553.g003", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Human_and_viral_protein_interaction_networks_connecting_the_known_RLR_pathway_with_the_newly_identified_RIG_I_factors_DDX17_and_SNW1_/1581140", "title"=>"Human and viral protein interaction networks connecting the known RLR pathway with the newly identified RIG-I factors DDX17 and SNW1.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-20 04:25:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/2367075"], "description"=>"<p>(<b>A</b>) Distributions of the 49 known RLR pathway components (RLR genes, green) and 5,818 genes unlikely to be part of the pathway (non-RLR genes, red) across the 10 molecular signature data sets we identified as predictive of the RLR system (see also <b><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004553#pcbi.1004553.t001\" target=\"_blank\">Table 1</a></b>). Data sets were binned into discrete intervals and fractions of (non-)RLR genes add up to one. Arrows indicate the behavior of RIG-I across the data. The top five signatures describe the relationship of RLR signaling with viruses; the bottom five describe properties of the pathway itself. (<b>B</b>) Boxplots of the genome-wide integrated RLR score (Bayesian posterior probability score). Genes were grouped into one of five classes: known RLR genes (green, see [A]), components of other PRR signaling pathways (‘TLR, CLR, NLR, cytDNA’; purple), genes functioning in other aspects of the innate immune response (‘innate immunity’; blue), and non-RLR genes (red, see [A]). The remaining genes are classified as ‘other’ (gray). (<b>C</b>) The 50 genes with the highest RLR scores. Representative RLR and other innate antiviral response genes are indicated. The pie chart shows the occurrences of the different gene classes in the top 354 RLR ranks. (<b>D</b>) Receiver operating characteristic (ROC) curve illustrating the performance of the integrated RLR score (solid black line) and the individual molecular signatures (black dots) for predicting known RLR versus non-RLR genes. Sensitivity and specificity were calculated at various score thresholds (for the RLR score), or at specific thresholds that include all bins with positive likelihood ratio scores (for the individual data sets; see (A)). The asterisk denotes the sensitivity and specificity corresponding to a false discovery rate (FDR) of 57% (top 354 genes). Note that, to avoid circularity, the predictive ability of the co-expression, protein domain and RLR pathway PPI data sets in (A) and (D) was assessed using the set of TLR, CLR, NLR, cytDNA genes instead of the RLR genes (see <b><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004553#sec026\" target=\"_blank\">Methods</a></b>).</p>", "links"=>[], "tags"=>["RLR pathway components", "genomics data", "pkn", "SNW", "187 candidates", "3k", "Bayesian approach", "RLR pathway genes", "Protein domains", "novel regulators", "prediction accuracy", "3BP", "DDX", "novel members", "uncharacterized factors", "RNAi knockdown experiments", "RLR pathway list", "transcription factors", "11b", "Innate Antiviral Response", "immunity research", "9b", "hspa", "94 genes", "trim", "interaction network", "novel RLR regulators", "cdc", "cdk", "nup", "IFN β.", "psma"], "article_id"=>1581136, "categories"=>["Uncategorised"], "users"=>["Robin van der Lee", "Qian Feng", "Martijn A. Langereis", "Rob ter Horst", "Radek Szklarczyk", "Mihai G. Netea", "Arno C. Andeweg", "Frank J. M. van Kuppeveld", "Martijn A. Huynen"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004553.g001", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Bayesian_integration_of_ten_molecular_signatures_of_RLR_pathway_components_from_genomics_data_/1581136", "title"=>"Bayesian integration of ten molecular signatures of RLR pathway components from genomics data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-20 04:25:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/2367084"], "description"=>"<p><sup>a</sup> '+': positive regulator (expected decrease in IFNβ induction upon knockdown). '-': negative regulator (expected increase in IFNβ induction upon knockdown).</p><p><sup>b</sup> Annotated cells (‘+’, ‘-’, ‘0’) indicate 11 candidate RLR genes that were tested in RNAi screen 1. ‘+’: down-hits from RNAi screen 1 (decreased RIG-I-mediated IFNβ induction upon knockdown, Z-score <-1.25). ‘-’: up-hits from RNAi screen 1 (increased RIG-I-mediated IFNβ induction upon knockdown, Z-score >1.25). ‘0’: no hit in RNAi screen 1, or inconsistent effect across RNAi screens 1 and 2 (<i>CSNK2A1</i> and <i>CSNK2A2</i>, <sup>c</sup>).</p><p>Validations of our predicted RLR candidates by independent studies.</p>", "links"=>[], "tags"=>["RLR pathway components", "genomics data", "pkn", "SNW", "187 candidates", "3k", "Bayesian approach", "RLR pathway genes", "Protein domains", "novel regulators", "prediction accuracy", "3BP", "DDX", "novel members", "uncharacterized factors", "RNAi knockdown experiments", "RLR pathway list", "transcription factors", "11b", "Innate Antiviral Response", "immunity research", "9b", "hspa", "94 genes", "trim", "interaction network", "novel RLR regulators", "cdc", "cdk", "nup", "IFN β.", "psma"], "article_id"=>1581145, "categories"=>["Uncategorised"], "users"=>["Robin van der Lee", "Qian Feng", "Martijn A. Langereis", "Rob ter Horst", "Radek Szklarczyk", "Mihai G. Netea", "Arno C. Andeweg", "Frank J. M. van Kuppeveld", "Martijn A. Huynen"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004553.t003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Validations_of_our_predicted_RLR_candidates_by_independent_studies_/1581145", "title"=>"Validations of our predicted RLR candidates by independent studies.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-10-20 04:25:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/2367082"], "description"=>"<p><sup>a</sup> These PPIs were not part of the RLR interaction network used for the RLR predictions (i.e. for the ‘RLR pathway PPI’ signature)</p><p><sup>b</sup> These interactions were not used to determine the virus-interacting human proteins used for the RLR predictions (i.e. for the ‘PPI with viruses’ signature)</p><p>Overlap between innate (antiviral) response data sets and the top 354 RLR predictions excluding known RLR genes.</p>", "links"=>[], "tags"=>["RLR pathway components", "genomics data", "pkn", "SNW", "187 candidates", "3k", "Bayesian approach", "RLR pathway genes", "Protein domains", "novel regulators", "prediction accuracy", "3BP", "DDX", "novel members", "uncharacterized factors", "RNAi knockdown experiments", "RLR pathway list", "transcription factors", "11b", "Innate Antiviral Response", "immunity research", "9b", "hspa", "94 genes", "trim", "interaction network", "novel RLR regulators", "cdc", "cdk", "nup", "IFN β.", "psma"], "article_id"=>1581143, "categories"=>["Uncategorised"], "users"=>["Robin van der Lee", "Qian Feng", "Martijn A. Langereis", "Rob ter Horst", "Radek Szklarczyk", "Mihai G. Netea", "Arno C. Andeweg", "Frank J. M. van Kuppeveld", "Martijn A. Huynen"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004553.t002", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overlap_between_innate_antiviral_response_data_sets_and_the_top_354_RLR_predictions_excluding_known_RLR_genes_/1581143", "title"=>"Overlap between innate (antiviral) response data sets and the top 354 RLR predictions excluding known RLR genes.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-10-20 04:25:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/2367081"], "description"=>"<p><sup>a</sup> Data used directly (d) or as basis for further calculation (c)</p><p><sup>b</sup> Combination of all bins with positive likelihood ratio scores per feature, derived from <b><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004553#pcbi.1004553.g001\" target=\"_blank\">Fig 1A</a></b></p><p><sup>c</sup> RLR genes versus non-RLR genes: <i>P</i>(D<sub>i</sub> | RLR genes) / <i>P</i>(D<sub>i</sub> | non-RLR genes), see <b><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004553#sec026\" target=\"_blank\">Methods</a></b>.</p><p>Note that, to avoid circularity, the predictive ability of the co-expression, protein domain and RLR pathway PPI data sets was assessed using the set of TLR, CLR, NLR, cytDNA genes instead of the RLR genes (see <b><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004553#sec026\" target=\"_blank\">Methods</a></b>).</p><p>See also <b><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004553#pcbi.1004553.g001\" target=\"_blank\">Fig 1A</a></b> and <b><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004553#pcbi.1004553.s015\" target=\"_blank\">S1 Table</a></b>.</p><p>Ten molecular signatures from genomics data used for predicting novel RLR pathway components.</p>", "links"=>[], "tags"=>["RLR pathway components", "genomics data", "pkn", "SNW", "187 candidates", "3k", "Bayesian approach", "RLR pathway genes", "Protein domains", "novel regulators", "prediction accuracy", "3BP", "DDX", "novel members", "uncharacterized factors", "RNAi knockdown experiments", "RLR pathway list", "transcription factors", "11b", "Innate Antiviral Response", "immunity research", "9b", "hspa", "94 genes", "trim", "interaction network", "novel RLR regulators", "cdc", "cdk", "nup", "IFN β.", "psma"], "article_id"=>1581142, "categories"=>["Uncategorised"], "users"=>["Robin van der Lee", "Qian Feng", "Martijn A. Langereis", "Rob ter Horst", "Radek Szklarczyk", "Mihai G. Netea", "Arno C. Andeweg", "Frank J. M. van Kuppeveld", "Martijn A. Huynen"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004553.t001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ten_molecular_signatures_from_genomics_data_used_for_predicting_novel_RLR_pathway_components_/1581142", "title"=>"Ten molecular signatures from genomics data used for predicting novel RLR pathway components.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-10-20 04:25:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/2367077"], "description"=>"<p>(<b>A</b>) Flow chart of the RNAi validation screens. 187 candidate RLR genes were screened for RIG-I pathway activity in three different RNAi screens. In screens 1 and 2, HeLa cells stably expressing an IFNβ promoter-controlled firefly luciferase (Fluc) reporter were stimulated with a 5’-ppp-containing RIG-I RNA ligand. The 57 hits (15 up, 42 down) with the largest effect on IFNβ induction upon siRNA knockdown in screen 1 (stringent Z-score <-2 or >2) were tested again in screen 2 with a different set of siRNAs. The 19 top hits from screen 2 were then picked for screen 3, which is similar to the first two screens except that it measures <i>IFNβ</i> mRNA levels using quantitative real-time qRT-PCR. (<b>B</b>) Correlation between the negative control-based robust Z-scores of RNAi screens 1 and 2. The 57 top hits with Z-scores <-2 or >2 in screen 1 were tested again in screen 2 (purple data points). N.T., non-transfected; SCR, scrambled. (<b>C</b>) Overview of the 19 novel RIG-I pathway genes with the largest effects on IFNβ induction in screens 1 and 2 (Z-score <-2 in both screens). Black data points correspond to genes whose knockdown also causes a reduction in <i>IFNβ</i> mRNA levels in screen 3. (<b>D</b>) RNAi screen 3. 13 of the 19 top hits from screens 1 and 2 also reduce RIG-I-mediated <i>IFNβ</i> mRNA production (black bars). Experiments were performed in triplicate (n = 3). Bars (mean±SEM) display the fold induction of <i>IFNβ</i> mRNA (corrected for actin mRNA levels) compared to the mock-treated control. Statistical significance was assessed by one-way analysis of variance (ANOVA) followed by Dunnett’s multiple comparison test, comparing the values for each of the 19 test genes to the combined negative control conditions (scrambled and LGP2, red bars). ** <i>P</i> < 0.01; *** <i>P</i> < 0.001. (<b>E</b>) Correlation between the <i>in silico</i> integrated RLR score and the probability of experimental confirmation in RNAi screen 1. The dark purple line represents all 94 hits with Z-score <-1.25 or >1.25; the light purple line represents the top 57 hits with Z-score <-2 or >2. The 187 experimentally tested genes were rank-ordered based on the RLR score and precision was calculated sequentially as the fraction of validated hits among all tested genes having a certain RLR score or higher.</p>", "links"=>[], "tags"=>["RLR pathway components", "genomics data", "pkn", "SNW", "187 candidates", "3k", "Bayesian approach", "RLR pathway genes", "Protein domains", "novel regulators", "prediction accuracy", "3BP", "DDX", "novel members", "uncharacterized factors", "RNAi knockdown experiments", "RLR pathway list", "transcription factors", "11b", "Innate Antiviral Response", "immunity research", "9b", "hspa", "94 genes", "trim", "interaction network", "novel RLR regulators", "cdc", "cdk", "nup", "IFN β.", "psma"], "article_id"=>1581138, "categories"=>["Uncategorised"], "users"=>["Robin van der Lee", "Qian Feng", "Martijn A. Langereis", "Rob ter Horst", "Radek Szklarczyk", "Mihai G. Netea", "Arno C. Andeweg", "Frank J. M. van Kuppeveld", "Martijn A. Huynen"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004553.g002", "stats"=>{"downloads"=>2, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_RNAi_screens_validate_a_role_for_the_novel_RLR_candidates_in_RIG_I_mediated_IFN_946_induction_/1581138", "title"=>"RNAi screens validate a role for the novel RLR candidates in RIG-I-mediated IFNβ induction.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-20 04:25:18"}

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{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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