A Quantitative Model of Transcriptional Regulation Reveals the Influence of Binding Location on Expression
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{"title"=>"A quantitative model of transcriptional regulation reveals the influence of binding location on expression", "type"=>"journal", "authors"=>[{"first_name"=>"Kenzie D.", "last_name"=>"Macisaac", "scopus_author_id"=>"6602906872"}, {"first_name"=>"Kinyui A.", "last_name"=>"Lo", "scopus_author_id"=>"8862998400"}, {"first_name"=>"William", "last_name"=>"Gordon", "scopus_author_id"=>"35961076500"}, {"first_name"=>"Shmulik", "last_name"=>"Motola", "scopus_author_id"=>"14421471600"}, {"first_name"=>"Tali", "last_name"=>"Mazor", "scopus_author_id"=>"35082403300"}, {"first_name"=>"Ernest", "last_name"=>"Fraenkel", "scopus_author_id"=>"6603560234"}], "year"=>2010, "source"=>"PLoS Computational Biology", "identifiers"=>{"scopus"=>"2-s2.0-77954586080", "sgr"=>"77954586080", "issn"=>"1553734X", "doi"=>"10.1371/journal.pcbi.1000773", "pmid"=>"20442865", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "pui"=>"359319059"}, "id"=>"97d8d7b7-eee7-3852-a5bd-5a7380bdb0c2", "abstract"=>"Understanding the mechanistic basis of transcriptional regulation has been a central focus of molecular biology since its inception. New high-throughput chromatin immunoprecipitation experiments have revealed that most regulatory proteins bind thousands of sites in mammalian genomes. However, the functional significance of these binding sites remains unclear. We present a quantitative model of transcriptional regulation that suggests the contribution of each binding site to tissue-specific gene expression depends strongly on its position relative to the transcription start site. For three cell types, we show that, by considering binding position, it is possible to predict relative expression levels between cell types with an accuracy approaching the level of agreement between different experimental platforms. Our model suggests that, for the transcription factors profiled in these cell types, a regulatory site's influence on expression falls off almost linearly with distance from the transcription start site in a 10 kilobase range. Binding to both evolutionarily conserved and non-conserved sequences contributes significantly to transcriptional regulation. Our approach also reveals the quantitative, tissue-specific role of individual proteins in activating or repressing transcription. These results suggest that regulator binding position plays a previously unappreciated role in influencing expression and blurs the classical distinction between proximal promoter and distal binding events.", "link"=>"http://www.mendeley.com/research/quantitative-model-transcriptional-regulation-reveals-influence-binding-location-expression", "reader_count"=>112, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>12, "Researcher"=>39, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>35, "Student > Postgraduate"=>3, "Student > Master"=>8, "Student > Bachelor"=>5, "Professor"=>5}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>12, "Researcher"=>39, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>35, "Student > Postgraduate"=>3, "Student > Master"=>8, "Student > Bachelor"=>5, "Professor"=>5}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Biochemistry, Genetics and Molecular Biology"=>10, "Mathematics"=>1, "Agricultural and Biological Sciences"=>93, "Chemistry"=>1, "Computer Science"=>5}, "reader_count_by_subdiscipline"=>{"Chemistry"=>{"Chemistry"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>93}, "Computer Science"=>{"Computer Science"=>5}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>10}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"Argentina"=>2, "Sweden"=>1, "United States"=>14, "Norway"=>1, "Brazil"=>1, "Italy"=>1, "United Kingdom"=>5, "Mexico"=>1, "Switzerland"=>2, "Germany"=>3, "Russia"=>1}, "group_count"=>3}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/851423"], "description"=>"<p>(A) Scatter plots of observed and predicted expression difference are presented for differentially expressed genes bound only at non-conserved regions at stringent and moderate conservation thresholds. Training data points are shown in blue and test data is shown in red. Each non-conserved binding event's effect on transcription was modulated by its distance to the TSS. In both tissue pairs, and at both conservation thresholds, the model's predictions are strongly correlated with observed expression differences. (B) The expression difference of genes bound at both conserved and non-conserved sites was predicted using only conserved sites, and the prediction error was compared to that obtained when both conserved and non-conserved binding sites were used. Including non-conserved regions significantly improved performance in both tissue pairs. Error bars indicate +/− s.e.m.</p>", "links"=>[], "tags"=>["binding", "events"], "article_id"=>521883, "categories"=>["Infectious Diseases", "Computational Biology"], "users"=>["Kenzie D. MacIsaac", "Kinyui A. Lo", "William Gordon", "Shmulik Motola", "Tali Mazor", "Ernest Fraenkel"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000773.g004", "stats"=>{"downloads"=>3, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Non_conserved_binding_events_predict_expression_/521883", "title"=>"Non-conserved binding events predict expression.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-29 00:31:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/851641"], "description"=>"<p>Anti-sera used in ChIP experiments.</p>", "links"=>[], "tags"=>["computational biology/genomics", "computational biology/transcriptional regulation"], "article_id"=>522100, "categories"=>["Infectious Diseases", "Computational Biology"], "users"=>["Kenzie D. MacIsaac", "Kinyui A. Lo", "William Gordon", "Shmulik Motola", "Tali Mazor", "Ernest Fraenkel"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000773.t001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Anti_sera_used_in_ChIP_experiments_/522100", "title"=>"Anti-sera used in ChIP experiments.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-04-29 00:35:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/851356"], "description"=>"<p>We compared three models relating regulator binding to relative transcription levels in liver and 3T3-L1 cells. The first model (A) learns a binding site's expression influence based on its location relative to the TSS. The second model (B) treats all binding events equally regardless of position. The third model (C) learns a binding site's expression influence based on its level of sequence conservation. We tested each model using several different distance cutoffs to identify bound genes. The bar graph shows how the number of genes included in the analysis increases as this cutoff is increased. Each model's performance, as measured by mean-squared prediction error on held out test data, is shown as a function of distance cutoff. Error bars indicate +/− s.e.m. The model that learns influence from position significantly outperforms the other approaches.</p>", "links"=>[], "tags"=>["tissue-specific"], "article_id"=>521813, "categories"=>["Infectious Diseases", "Computational Biology"], "users"=>["Kenzie D. MacIsaac", "Kinyui A. Lo", "William Gordon", "Shmulik Motola", "Tali Mazor", "Ernest Fraenkel"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000773.g003", "stats"=>{"downloads"=>2, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Regulatory_region_position_predicts_tissue_specific_expression_/521813", "title"=>"Regulatory region position predicts tissue-specific expression.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-29 00:30:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/851155"], "description"=>"<p>Putative regulatory regions in liver and 3T3-L1 cells were identified from ChIP-seq experiments and are defined as sites bound by p300 and/or at least two other transcription factors. We performed a similar analysis in liver and cerebellum using ChIP-chip with promoter microarrays, where a regulatory region is defined as any site bound by CBP. (A) Genes with a regulatory region within 5kb of their transcription start site have a higher mean expression level than genes with no binding event. Error bars indicate +/− s.e.m. (B) Bound genes display large variation in levels of absolute gene expression. (C) Putative regulatory regions show great variation in their sequence conservation levels. Conservation level was calculated as the maximum 100bp moving average of Phastcons scores from alignments of placental mammal genomes.</p>", "links"=>[], "tags"=>["bound", "genes"], "article_id"=>521609, "categories"=>["Infectious Diseases", "Computational Biology"], "users"=>["Kenzie D. MacIsaac", "Kinyui A. Lo", "William Gordon", "Shmulik Motola", "Tali Mazor", "Ernest Fraenkel"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000773.g001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Characteristics_of_bound_genes_and_bound_regions_/521609", "title"=>"Characteristics of bound genes and bound regions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-29 00:26:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/851245"], "description"=>"<p>(A) The mean log expression of bound genes is shown in each tissue as a function of both the distance between the transcription start site and the nearest regulatory region identified by ChIP, and the maximum conservation score of any regulatory region within 5kb of that gene's TSS. Error bars indicate +/− s.e.m. Also shown is the Spearman correlation, and associated p-value from a right-tailed t-test, between log expression and the distance and conservation measures. (B) In the upper plot the mean log expression of genes in liver and 3T3-L1 cells is shown as a function of the location of the nearest binding site over a 200kb window. Error bars indicate +/− s.e.m. In the lower plot we show the influence function, which measures a binding event's predicted effect on expression as a function of position, obtained by fitting our predictive model to 1,000 bootstrapped samples of ChIP and expression data in each tissue. Shaded regions show the empirical 99% confidence intervals obtained from the bootstrap iterations.</p>", "links"=>[], "tags"=>["computational biology/genomics", "computational biology/transcriptional regulation"], "article_id"=>521705, "categories"=>["Infectious Diseases", "Computational Biology"], "users"=>["Kenzie D. MacIsaac", "Kinyui A. Lo", "William Gordon", "Shmulik Motola", "Tali Mazor", "Ernest Fraenkel"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000773.g002", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Binding_site_position_but_not_sequence_conservation_is_strongly_associated_with_gene_expression_level_/521705", "title"=>"Binding site position, but not sequence conservation, is strongly associated with gene expression level.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-29 00:28:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/851502"], "description"=>"<p>(A) Shown are representative scatter plots of predicted vs. observed expression differences for held out test genes in liver/cerebellum and liver/3T3-L1 cells. Predictions were made using a transcriptional model that takes into account the influence of both the genomic position and the particular proteins bound by a site. The median correlation from 11 separate trials was 0.65 and 0.74 for liver/cerebellum and liver/3T3-L1 respectively. (B) The prediction error of the full model that includes individual transcription factor influence weights is compared to a model that uses only position to predict influence. Modeling the influence of bound regulators improves predictive performance. Error bars indicate +/− s.e.m. (C) The expression influence for each protein is learned in our transcriptional model. Sites bound by proteins with known repressive activity (E2F4 and Sirt1) are predicted to have the smallest influence.</p>", "links"=>[], "tags"=>["regulators", "influences"], "article_id"=>521961, "categories"=>["Infectious Diseases", "Computational Biology"], "users"=>["Kenzie D. MacIsaac", "Kinyui A. Lo", "William Gordon", "Shmulik Motola", "Tali Mazor", "Ernest Fraenkel"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000773.g005", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Transcriptional_regulators_have_distinct_influences_on_expression_/521961", "title"=>"Transcriptional regulators have distinct influences on expression.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-29 00:32:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/423670", "https://ndownloader.figshare.com/files/423822"], "description"=>"<div><p>Understanding the mechanistic basis of transcriptional regulation has been a central focus of molecular biology since its inception. New high-throughput chromatin immunoprecipitation experiments have revealed that most regulatory proteins bind thousands of sites in mammalian genomes. However, the functional significance of these binding sites remains unclear. We present a quantitative model of transcriptional regulation that suggests the contribution of each binding site to tissue-specific gene expression depends strongly on its position relative to the transcription start site. For three cell types, we show that, by considering binding position, it is possible to predict relative expression levels between cell types with an accuracy approaching the level of agreement between different <em>experimental</em> platforms. Our model suggests that, for the transcription factors profiled in these cell types, a regulatory site's influence on expression falls off almost linearly with distance from the transcription start site in a 10 kilobase range. Binding to both evolutionarily conserved and non-conserved sequences contributes significantly to transcriptional regulation. Our approach also reveals the quantitative, tissue-specific role of individual proteins in activating or repressing transcription. These results suggest that regulator binding position plays a previously unappreciated role in influencing expression and blurs the classical distinction between proximal promoter and distal binding events.</p></div>", "links"=>[], "tags"=>["quantitative", "transcriptional", "reveals", "binding"], "article_id"=>143641, "categories"=>["Cancer", "Biological Sciences"], "users"=>["Kenzie D. MacIsaac", "Kinyui A. Lo", "William Gordon", "Shmulik Motola", "Tali Mazor", "Ernest Fraenkel"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000773.s001", "https://dx.doi.org/10.1371/journal.pcbi.1000773.s002"], "stats"=>{"downloads"=>8, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/A_Quantitative_Model_of_Transcriptional_Regulation_Reveals_the_Influence_of_Binding_Location_on_Expression/143641", "title"=>"A Quantitative Model of Transcriptional Regulation Reveals the Influence of Binding Location on Expression", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-04-29 01:00:41"}

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