Coordinated Concentration Changes of Transcripts and Metabolites in Saccharomyces cerevisiae
Publication Date
January 30, 2009
Journal
PLOS Computational Biology
Authors
Patrick H. Bradley, Matthew J. Brauer, Joshua D. Rabinowitz & Olga G. Troyanskaya
Volume
5
Issue
1
Pages
e1000270
DOI
https://dx.plos.org/10.1371/journal.pcbi.1000270
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1000270
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/19180179
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2614473
Europe PMC
http://europepmc.org/abstract/MED/19180179
Web of Science
000263924300004
Scopus
59149103774
Mendeley
http://www.mendeley.com/research/coordinated-concentration-changes-transcripts-metabolites-saccharomyces-cerevisiae
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Mendeley | Further Information

{"title"=>"Coordinated concentration changes of transcripts and metabolites in Saccharomyces cerevisiae", "type"=>"journal", "authors"=>[{"first_name"=>"Patrick H.", "last_name"=>"Bradley", "scopus_author_id"=>"26024970800"}, {"first_name"=>"Matthew J.", "last_name"=>"Brauer", "scopus_author_id"=>"7102421885"}, {"first_name"=>"Joshua D.", "last_name"=>"Rabinowitz", "scopus_author_id"=>"7202369805"}, {"first_name"=>"Olga G.", "last_name"=>"Troyanskaya", "scopus_author_id"=>"6602361421"}], "year"=>2009, "source"=>"PLoS Computational Biology", "identifiers"=>{"issn"=>"1553734X", "scopus"=>"2-s2.0-59149103774", "sgr"=>"59149103774", "pui"=>"354131988", "isbn"=>"1553-7358 (Electronic)", "pmid"=>"19180179", "doi"=>"10.1371/journal.pcbi.1000270"}, "id"=>"20820ca7-06c4-3396-965b-1e59a87d0607", "abstract"=>"Metabolite concentrations can regulate gene expression, which can in turn regulate metabolic activity. The extent to which functionally related transcripts and metabolites show similar patterns of concentration changes, however, remains unestablished. We measure and analyze the metabolomic and transcriptional responses of Saccharomyces cerevisiae to carbon and nitrogen starvation. Our analysis demonstrates that transcripts and metabolites show coordinated response dynamics. Furthermore, metabolites and gene products whose concentration profiles are alike tend to participate in related biological processes. To identify specific, functionally related genes and metabolites, we develop an approach based on Bayesian integration of the joint metabolomic and transcriptomic data. This algorithm finds interactions by evaluating transcript-metabolite correlations in light of the experimental context in which they occur and the class of metabolite involved. It effectively predicts known enzymatic and regulatory relationships, including a gene-metabolite interaction central to the glycolytic-gluconeogenetic switch. This work provides quantitative evidence that functionally related metabolites and transcripts show coherent patterns of behavior on the genome scale and lays the groundwork for building gene-metabolite interaction networks directly from systems-level data.", "link"=>"http://www.mendeley.com/research/coordinated-concentration-changes-transcripts-metabolites-saccharomyces-cerevisiae", "reader_count"=>184, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>12, "Researcher"=>66, "Student > Doctoral Student"=>10, "Student > Ph. D. Student"=>54, "Student > Postgraduate"=>4, "Student > Master"=>8, "Other"=>6, "Student > Bachelor"=>7, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>2, "Professor"=>11}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>12, "Researcher"=>66, "Student > Doctoral Student"=>10, "Student > Ph. D. Student"=>54, "Student > Postgraduate"=>4, "Student > Master"=>8, "Other"=>6, "Student > Bachelor"=>7, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>2, "Professor"=>11}, "reader_count_by_subject_area"=>{"Engineering"=>9, "Unspecified"=>5, "Biochemistry, Genetics and Molecular Biology"=>28, "Agricultural and Biological Sciences"=>123, "Medicine and Dentistry"=>2, "Physics and Astronomy"=>1, "Chemical Engineering"=>1, "Chemistry"=>5, "Computer Science"=>9, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>9}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Chemistry"=>{"Chemistry"=>5}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>123}, "Computer Science"=>{"Computer Science"=>9}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>28}, "Unspecified"=>{"Unspecified"=>5}, "Chemical Engineering"=>{"Chemical Engineering"=>1}}, "reader_count_by_country"=>{"United States"=>19, "United Kingdom"=>1, "Switzerland"=>2, "Portugal"=>2, "Spain"=>1, "Canada"=>1, "Netherlands"=>1, "Sweden"=>1, "Finland"=>1, "Denmark"=>1, "Israel"=>1, "Chile"=>1, "Peru"=>1, "Germany"=>2}, "group_count"=>5}

CrossRef

Scopus | Further Information

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  • {"files"=>["https://ndownloader.figshare.com/files/449562", "https://ndownloader.figshare.com/files/449582", "https://ndownloader.figshare.com/files/449602", "https://ndownloader.figshare.com/files/449612", "https://ndownloader.figshare.com/files/449618", "https://ndownloader.figshare.com/files/449622"], "description"=>"<div><p>Metabolite concentrations can regulate gene expression, which can in turn regulate metabolic activity. The extent to which functionally related transcripts and metabolites show similar patterns of concentration changes, however, remains unestablished. We measure and analyze the metabolomic and transcriptional responses of <em>Saccharomyces cerevisiae</em> to carbon and nitrogen starvation. Our analysis demonstrates that transcripts and metabolites show coordinated response dynamics. Furthermore, metabolites and gene products whose concentration profiles are alike tend to participate in related biological processes. To identify specific, functionally related genes and metabolites, we develop an approach based on Bayesian integration of the joint metabolomic and transcriptomic data. This algorithm finds interactions by evaluating transcript–metabolite correlations in light of the experimental context in which they occur and the class of metabolite involved. It effectively predicts known enzymatic and regulatory relationships, including a gene–metabolite interaction central to the glycolytic–gluconeogenetic switch. This work provides quantitative evidence that functionally related metabolites and transcripts show coherent patterns of behavior on the genome scale and lays the groundwork for building gene–metabolite interaction networks directly from systems-level data.</p></div>", "links"=>[], "tags"=>["coordinated", "changes", "transcripts", "metabolites"], "article_id"=>148609, "categories"=>["Biological Sciences", "Medicine", "Cell Biology"], "users"=>["Patrick H. Bradley", "Matthew J. Brauer", "Joshua D. Rabinowitz", "Olga G. Troyanskaya"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000270.s001", "https://dx.doi.org/10.1371/journal.pcbi.1000270.s002", "https://dx.doi.org/10.1371/journal.pcbi.1000270.s003", "https://dx.doi.org/10.1371/journal.pcbi.1000270.s004", "https://dx.doi.org/10.1371/journal.pcbi.1000270.s005", "https://dx.doi.org/10.1371/journal.pcbi.1000270.s006"], "stats"=>{"downloads"=>38, "page_views"=>28, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Coordinated_Concentration_Changes_of_Transcripts_and_Metabolites_in_Saccharomyces_cerevisiae_/148609", "title"=>"Coordinated Concentration Changes of Transcripts and Metabolites in <em>Saccharomyces cerevisiae</em>", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2009-01-30 02:23:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/909975"], "description"=>"<p>Metabolite and gene transcript concentration changes are represented as ratios of measurements from starved cells to measurements from unstarved cells. The responses observed under carbon starvation (light gray circles, “Carbon” in legend) are labeled distinctly from the responses under nitrogen starvation (dark gray triangles, “Nitrogen” in legend), but are plotted on the same axes. Solid light gray and dark gray lines are linear best-fits for the responses observed under carbon and nitrogen starvation, respectively; the dashed line is a linear best-fit curve for all data. (A–E) Scatterplots of metabolites from the glycolysis and pentose-phosphate pathway metabolic class versus related genes show an inverse relationship under carbon starvation, but a positive correlation under nitrogen starvation. The dashed line shows that this relationship would be obscured by computing correlation across all data points. <i>ILV2</i> catalyzes the first step of isoleucine and valine biosynthesis from pyruvate; <i>ARO3</i> catalyzes the first step in aromatic amino acid biosynthesis from PEP and erythrose-4-phosphate; <i>ALD6</i>, which also plays a key role in redox metabolism, is involved in the creation of cytosolic acetyl-CoA from pyruvate; <i>GLK1</i> phosphorylates glucose to glucose-6-phosphate; and <i>PGM2</i> catalyzes the interconversion of glucose-1-phosphate and glucose-6-phosphate. (F–H) Scatterplots of metabolites from the amino acid metabolic class versus related genes, in contrast, show positive correlation in both carbon and nitrogen starvation. Even in this case, however, computing correlation across both conditions can lead to an underestimation of the extent of the relationship (e.g., (H) threonine vs. <i>THR4</i>, where although and , ). <i>HTS1</i> charges (i.e. aminoacylates) the histidinyl-tRNA; <i>MET6</i> catalyzes the formation of methionine from homocysteine; and <i>THR4</i> converts phosphohomoserine to threonine.</p>", "links"=>[], "tags"=>["scatterplots", "condition-dependent", "metabolites", "motivating", "bayesian"], "article_id"=>580426, "categories"=>["Computational Biology", "Medicine", "Cell Biology"], "users"=>["Patrick H. Bradley", "Matthew J. Brauer", "Joshua D. Rabinowitz", "Olga G. Troyanskaya"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000270.g002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_selection_of_example_scatterplots_that_demonstrate_experimental_condition_dependent_correlation_between_metabolites_and_related_genes_motivating_the_use_of_a_Bayesian_algorithm_/580426", "title"=>"A selection of example scatterplots that demonstrate experimental condition-dependent correlation between metabolites and related genes, motivating the use of a Bayesian algorithm.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-01-30 00:07:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/909789"], "description"=>"<p>Eigenvectors of the transcription (left) and metabolite (right) concentration data sets were calculated. Each eigenvector is composed of a characteristic response under carbon starvation (“carbon”, in light gray circles) and under nitrogen starvation (“nitrogen”, in dark gray triangles). For each gene eigenvector on the left, the corresponding metabolite eigenvector is plotted on the right. The corresponding eigenvectors correlated significantly, with p-values of (A) 6.9×10<sup>−3</sup>, (B) 2.2×10<sup>−4</sup>, and (C) 2.1×10<sup>−2</sup>. For the transcript data, the percent information explained by each eigenvector was (A) 46%, (B) 13%, and (C) 9%. For the metabolite data, the percent information explained was (A) 33%, (B) 20%, and (C) 10%.</p>", "links"=>[], "tags"=>["decomposition", "reveals", "coordination", "transcriptional", "metabolic", "responses", "carbon", "nitrogen"], "article_id"=>580237, "categories"=>["Computational Biology", "Medicine", "Cell Biology"], "users"=>["Patrick H. Bradley", "Matthew J. Brauer", "Joshua D. Rabinowitz", "Olga G. Troyanskaya"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000270.g001", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Singular_value_decomposition_reveals_coordination_between_transcriptional_and_metabolic_responses_to_carbon_and_nitrogen_starvation_/580237", "title"=>"Singular value decomposition reveals coordination between transcriptional and metabolic responses to carbon and nitrogen starvation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-01-30 00:03:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/910327"], "description"=>"<p>(A) Schematic of reactions involving FBP and <i>VID24</i>. The conversion of FBP to hexose phosphate is catalyzed by fructose-1,6-bisphosphatase (<i>FBP1</i>). Vid24p destroys this enzyme by targeting it to the vacuole for destruction. (B) Scatterplot showing the relationship between <i>VID24</i> and FBP abundances over carbon starvation (“carbon” in the legend) and nitrogen starvation (“nitrogen” in the legend). As in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000270#pcbi-1000270-g002\" target=\"_blank\">Figure 2</a>, lines represent linear best-fit curves, calculated separately for each condition (solid lines) or over both conditions (dashed line). <i>VID24</i> and FBP are inversely correlated under carbon starvation (light gray), but positively correlated under nitrogen starvation (dark gray), as anticipated for a gene interacting with a glycolytic metabolite. <i>VID24</i> was in the top 3% of predictions made for FBP.</p>", "links"=>[], "tags"=>["regulating", "interacting", "glycolytic", "metabolite"], "article_id"=>580784, "categories"=>["Computational Biology", "Medicine", "Cell Biology"], "users"=>["Patrick H. Bradley", "Matthew J. Brauer", "Joshua D. Rabinowitz", "Olga G. Troyanskaya"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000270.g005", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_of_gene_regulating_the_glycolytic_gluconeogenic_switch_VID24_that_we_identified_as_interacting_with_the_key_glycolytic_metabolite_fructose_1_6_bisphosphate_FBP_/580784", "title"=>"Example of gene regulating the glycolytic–gluconeogenic switch (<i>VID24</i>) that we identified as interacting with the key glycolytic metabolite fructose-1,6-bisphosphate (FBP).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-01-30 00:13:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/910240"], "description"=>"<p>Recall, or fraction of known positives predicted by the system, is plotted on the X-axis (log scale); precision, or fraction of predictions that are in the training set, is plotted on the Y axis. The Bayesian integration PRC shows greater area under the curve than the correlation PRC, especially in the left-most, highest-confidence regime.</p>", "links"=>[], "tags"=>["context-sensitive", "bayesian", "compared", "interactions", "transcriptomic", "metabolomic"], "article_id"=>580690, "categories"=>["Computational Biology", "Medicine", "Cell Biology"], "users"=>["Patrick H. Bradley", "Matthew J. Brauer", "Joshua D. Rabinowitz", "Olga G. Troyanskaya"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000270.g004", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Precision_recall_curve_PRC_showing_the_superior_performance_of_context_sensitive_Bayesian_integration_dark_gray_line_as_compared_to_overall_strength_of_gene_8211_metabolite_concentration_correlation_light_gray_line_for_identifying_gene_8211_metabolite_fu/580690", "title"=>"Precision-recall curve (PRC) showing the superior performance of context-sensitive Bayesian integration (dark gray line), as compared to overall strength of gene–metabolite concentration correlation (light gray line), for identifying gene–metabolite functional interactions from transcriptomic and metabolomic data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-01-30 00:11:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/910431"], "description"=>"<p>Contingency table representing enrichment of predicted pairs for evidence in literature validation study (by Fisher's exact test, ). <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000270#pcbi.1000270.s003\" target=\"_blank\">Dataset S3</a> contains the full results from the literature study.</p>", "links"=>[], "tags"=>["cell biology/microbial physiology and metabolism", "computational biology/metabolic networks", "computational biology/systems biology"], "article_id"=>580884, "categories"=>["Computational Biology", "Medicine", "Cell Biology"], "users"=>["Patrick H. Bradley", "Matthew J. Brauer", "Joshua D. Rabinowitz", "Olga G. Troyanskaya"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000270.t002", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Literature_support_for_predicted_gene_8211_metabolite_interactions_/580884", "title"=>"Literature support for predicted gene–metabolite interactions.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-01-30 00:14:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/910109"], "description"=>"<p>(A) Overview of Bayesian integration procedure. Transcript and metabolite data were used to compute correlations between genes and metabolites over time under different experimental conditions. These correlations, along with a set of positive and negative examples obtained from KEGG, were used to train a Bayesian network. (B) Structure of the Bayesian network. This four-node network states that the variables corresponding to gene–metabolite correlations observed under either nitrogen or carbon starvation depend on the class of the metabolite involved, and whether or not a functional relationship between the gene and metabolite exists. The rounded boxes by each node represent the possible values that the nodes can take. (C) Conditional probability distributions learned from the experimental data. The parameters of the Bayesian network were computed from the experimental data and the set of positive and negative examples of gene–metabolite functional interactions. The light gray line gives the probability (y-axis) that, given no functional relationship, one would observe a given correlation (x-axis); the dark gray line gives the corresponding probability if given a true functional relationship instead. (D) Conditional probability distributions represented as log-odds scores. The sign of the bar corresponds to whether observing a certain strength and direction of correlation is more likely for a true functional relationship (positive) or for no functional relationship (negative), while the magnitude of the bar corresponds to how much more likely this is.</p>", "links"=>[], "tags"=>["relating", "interactions", "metabolomic", "transcriptomic", "metabolite"], "article_id"=>580566, "categories"=>["Computational Biology", "Medicine", "Cell Biology"], "users"=>["Patrick H. Bradley", "Matthew J. Brauer", "Joshua D. Rabinowitz", "Olga G. Troyanskaya"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000270.g003", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Bayesian_network_relating_gene_8211_metabolite_interactions_to_metabolomic_and_transcriptomic_data_and_to_metabolite_class_/580566", "title"=>"Bayesian network relating gene–metabolite interactions to metabolomic and transcriptomic data and to metabolite class.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-01-30 00:09:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/910473"], "description"=>"<p>Complete results of Gene Ontology (GO) term enrichment for each metabolite class: glycolysis and pentose-phosphate pathway compounds (G/PPP), amino acids (AA), TCA cycle intermediates (TCA), and biosynthetic intermediates (BSI).</p>", "links"=>[], "tags"=>["ontology", "enrichment", "metabolite", "glycolysis", "pentose-phosphate", "pathway", "compounds", "amino", "acids", "tca", "intermediates", "biosynthetic"], "article_id"=>580916, "categories"=>["Computational Biology", "Medicine", "Cell Biology"], "users"=>["Patrick H. Bradley", "Matthew J. Brauer", "Joshua D. Rabinowitz", "Olga G. Troyanskaya"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000270.t001", "stats"=>{"downloads"=>6, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Complete_results_of_Gene_Ontology_GO_term_enrichment_for_each_metabolite_class_glycolysis_and_pentose_phosphate_pathway_compounds_G_PPP_amino_acids_AA_TCA_cycle_intermediates_TCA_and_biosynthetic_intermediates_BSI_/580916", "title"=>"Complete results of Gene Ontology (GO) term enrichment for each metabolite class: glycolysis and pentose-phosphate pathway compounds (G/PPP), amino acids (AA), TCA cycle intermediates (TCA), and biosynthetic intermediates (BSI).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-01-30 00:15:16"}

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  • {"unique-ip"=>"5", "full-text"=>"7", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"4", "full-text"=>"3", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"3", "cited-by"=>"1", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"8", "full-text"=>"6", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"14", "full-text"=>"9", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"6", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}

Relative Metric

{"start_date"=>"2009-01-01T00:00:00Z", "end_date"=>"2009-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences/Cell biology", "average_usage"=>[336, 588, 721, 826, 931, 1029, 1138, 1224, 1311, 1395, 1477, 1555, 1632, 1704, 1774, 1847, 1919, 1987, 2039, 2089, 2149, 2204, 2264, 2319, 2370, 2424, 2481, 2535, 2593, 2645, 2702, 2765, 2816, 2865, 2932, 2992, 3045, 3105, 3159, 3208, 3259, 3316, 3374, 3430, 3490, 3542, 3602, 3654, 3720, 3783, 3851, 3919, 3981, 4038, 4096, 4151, 4197, 4235, 4297, 4372, 4437]}, {"subject_area"=>"/Physical sciences/Mathematics", "average_usage"=>[390, 657, 764, 869, 966, 1052, 1148, 1251, 1329, 1410, 1469, 1529, 1603, 1663, 1723, 1784, 1858, 1929, 1995, 2054, 2103, 2155, 2202, 2268, 2305, 2346, 2398, 2449, 2484, 2546, 2590, 2652, 2726, 2763, 2825, 2879, 2926, 2985, 3054, 3114, 3160, 3243, 3304, 3389, 3466, 3519, 3561, 3631, 3679, 3731, 3805, 3859, 3907, 3944, 3994, 4076, 4150, 4202, 4229, 4275, 4344, 4373]}]}
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