Novel Pancreatic Endocrine Maturation Pathways Identified by Genomic Profiling and Causal Reasoning
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{"title"=>"Novel Pancreatic Endocrine Maturation Pathways Identified by Genomic Profiling and Causal Reasoning", "type"=>"journal", "authors"=>[{"first_name"=>"Alex", "last_name"=>"Gutteridge", "scopus_author_id"=>"6508239724"}, {"first_name"=>"J. Michael", "last_name"=>"Rukstalis", "scopus_author_id"=>"12770833400"}, {"first_name"=>"Daniel", "last_name"=>"Ziemek", "scopus_author_id"=>"24077744800"}, {"first_name"=>"Mark", "last_name"=>"Tié", "scopus_author_id"=>"55597107100"}, {"first_name"=>"Lin", "last_name"=>"Ji", "scopus_author_id"=>"55597541200"}, {"first_name"=>"Rebeca", "last_name"=>"Ramos-Zayas", "scopus_author_id"=>"55597719700"}, {"first_name"=>"Nancy A.", "last_name"=>"Nardone", "scopus_author_id"=>"57112184800"}, {"first_name"=>"Lisa D.", "last_name"=>"Norquay", "scopus_author_id"=>"6506022348"}, {"first_name"=>"Martin B.", "last_name"=>"Brenner", "scopus_author_id"=>"7402358530"}, {"first_name"=>"Kim", "last_name"=>"Tang", "scopus_author_id"=>"55597273400"}, {"first_name"=>"John D.", "last_name"=>"McNeish", "scopus_author_id"=>"7003321648"}, {"first_name"=>"Rebecca K.", "last_name"=>"Rowntree", "scopus_author_id"=>"57189992612"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84873888412", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "doi"=>"10.1371/journal.pone.0056024", "pui"=>"368342589", "sgr"=>"84873888412", "issn"=>"19326203", "pmid"=>"23418498"}, "id"=>"d1ade1b9-2fee-3507-8762-f3c840eaefcb", "abstract"=>"We have used a previously unavailable model of pancreatic development, derived in vitro from human embryonic stem cells, to capture a time-course of gene, miRNA and histone modification levels in pancreatic endocrine cells. We investigated whether it is possible to better understand, and hence control, the biological pathways leading to pancreatic endocrine formation by analysing this information and combining it with the available scientific literature to generate models using a casual reasoning approach. We show that the embryonic stem cell differentiation protocol is highly reproducible in producing endocrine precursor cells and generates cells that recapitulate many aspects of human embryonic pancreas development, including maturation into functional endocrine cells when transplanted into recipient animals. The availability of whole genome gene and miRNA expression data from the early stages of human pancreatic development will be of great benefit to those in the fields of developmental biology and diabetes research. Our causal reasoning algorithm suggested the involvement of novel gene networks, such as NEUROG3/E2F1/KDM5B and SOCS3/STAT3/IL-6, in endocrine cell development We experimentally investigated the role of the top-ranked prediction by showing that addition of exogenous IL-6 could affect the expression of the endocrine progenitor genes NEUROG3 and NKX2.2.", "link"=>"http://www.mendeley.com/research/novel-pancreatic-endocrine-maturation-pathways-identified-genomic-profiling-causal-reasoning", "reader_count"=>28, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Researcher"=>7, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>6, "Student > Postgraduate"=>3, "Student > Master"=>3, "Other"=>3, "Student > Bachelor"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Researcher"=>7, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>6, "Student > Postgraduate"=>3, "Student > Master"=>3, "Other"=>3, "Student > Bachelor"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>7, "Medicine and Dentistry"=>4, "Agricultural and Biological Sciences"=>11, "Design"=>1, "Psychology"=>1, "Immunology and Microbiology"=>2}, "reader_count_by_subdiscipline"=>{"Design"=>{"Design"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Psychology"=>{"Psychology"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>11}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>7}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>1}}, "reader_count_by_country"=>{"Italy"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/489184"], "description"=>"<p>(A) Genome wide gene expression correlation heatmap between samples. Samples are clustered by the Euclidean distance between rows/columns and single linkage clustering. The colored bar along the top of the heatmap indicates the timepoint at which the sample was taken (pink: day 0, maroon: day 11). (B) Heatmap of expression of selected markers. The developmental stage is indicated by the labels to the left of the heatmap.</p>", "links"=>[], "tags"=>["shows", "reproducibility", "differentiation"], "article_id"=>159691, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics", "Developmental Biology"], "users"=>["Alex Gutteridge", "J. Michael Rukstalis", "Daniel Ziemek", "Mark Tié", "Lin Ji", "Rebeca Ramos-Zayas", "Nancy A. Nardone", "Lisa D. Norquay", "Martin B. Brenner", "Kim Tang", "John D. McNeish", "Rebecca K. Rowntree"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0056024.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Transcriptome_analysis_shows_the_reproducibility_of_the_differentiation_protocol_/159691", "title"=>"Transcriptome analysis shows the reproducibility of the differentiation protocol.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 02:41:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/489302"], "description"=>"<p>(A) Heatmap of the correlation between H3K4me<sup>3</sup> levels (rows) and gene expression (columns) at every pair of timepoints. (B) Heatmap of the correlation between changes in H3K4me<sup>3</sup> levels (rows) and changes in gene expression (columns) at every pair of time intervals. (C) Histogram of the correlation coefficients (R) for the correlation between H3K4me<sup>3</sup> levels and gene expression across time for every gene in the dataset. Green bars show the distribution of R for all genes and red bars show the distribution for ribosomal genes only. (D) Gene expression (green) and H3K4me<sup>3</sup> levels across time for ribosomal gene MRPS17.</p>", "links"=>[], "tags"=>["changes", "correlate", "transcriptional"], "article_id"=>159814, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics", "Developmental Biology"], "users"=>["Alex Gutteridge", "J. Michael Rukstalis", "Daniel Ziemek", "Mark Tié", "Lin Ji", "Rebeca Ramos-Zayas", "Nancy A. Nardone", "Lisa D. Norquay", "Martin B. Brenner", "Kim Tang", "John D. McNeish", "Rebecca K. Rowntree"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0056024.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Epigenetic_changes_generally_correlate_with_transcriptional_changes_/159814", "title"=>"Epigenetic changes generally correlate with transcriptional changes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 02:43:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/489435"], "description"=>"<p>(A) Gene expression (blue) and H3K4me<sup>3</sup> level (red) profiles for SOX17. The horizontal dashed line indicates the background H3K4me<sup>3</sup> level. (B) H3K4me<sup>3</sup> reads piled up over the SOX17 gene body at days 0, 2 and 11. The start and end points of SOX17 are indicated by dashed lines. (C&D) As for (A&B) but for Insulin.</p>", "links"=>[], "tags"=>["insulin", "accompanied", "epigenetic"], "article_id"=>159949, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics", "Developmental Biology"], "users"=>["Alex Gutteridge", "J. Michael Rukstalis", "Daniel Ziemek", "Mark Tié", "Lin Ji", "Rebeca Ramos-Zayas", "Nancy A. Nardone", "Lisa D. Norquay", "Martin B. Brenner", "Kim Tang", "John D. McNeish", "Rebecca K. Rowntree"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0056024.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Induction_of_insulin_expression_is_not_accompanied_with_epigenetic_changes_/159949", "title"=>"Induction of insulin expression is not accompanied with epigenetic changes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 02:45:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/489578"], "description"=>"<p>(A–C) Gene expression (blue), miRNA expression (green) and H3K4me<sup>3</sup> levels for CD47, ITGB1 and ANP32B and the miRNAs associated with them. In all 3 cases the miRNA is predicted to regulate the relevant gene and is also anti-correlated in level. In (A) H3K4me<sup>3</sup> levels correlate closely with the gene expression, whilst in (B & C) there is no correlation, suggesting a stronger role for the miRNA regulation.</p>", "links"=>[], "tags"=>["changes"], "article_id"=>160095, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics", "Developmental Biology"], "users"=>["Alex Gutteridge", "J. Michael Rukstalis", "Daniel Ziemek", "Mark Tié", "Lin Ji", "Rebeca Ramos-Zayas", "Nancy A. Nardone", "Lisa D. Norquay", "Martin B. Brenner", "Kim Tang", "John D. McNeish", "Rebecca K. Rowntree"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0056024.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Epigenetic_changes_can_be_used_to_identify_novel_regulatory_miRNAs_/160095", "title"=>"Epigenetic changes can be used to identify novel regulatory miRNAs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 00:01:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/489769"], "description"=>"<p>(A) Heatmap of enrichment of GO terms amongst up and down-regulated genes at each time interval. Red indicates an enrichment amongst up-regulated genes and green an enrichment amongst down-regulated genes. (B) SPIA analysis of signaling pathways at each time point. The numbers in each cell indicate the -log10 of the P value for perturbation of the given pathway at each time interval. Darker colors indicate strong perturbations and white indicates no perturbation.</p>", "links"=>[], "tags"=>["changes"], "article_id"=>160281, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics", "Developmental Biology"], "users"=>["Alex Gutteridge", "J. Michael Rukstalis", "Daniel Ziemek", "Mark Tié", "Lin Ji", "Rebeca Ramos-Zayas", "Nancy A. Nardone", "Lisa D. Norquay", "Martin B. Brenner", "Kim Tang", "John D. McNeish", "Rebecca K. Rowntree"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0056024.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Functional_analysis_of_gene_expression_changes_at_each_time_interval_/160281", "title"=>"Functional analysis of gene expression changes at each time interval.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 00:04:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/489863"], "description"=>"<p>(A) Heatmap of genes involved in cell cycle processes. (B–D) Gene expression (green) and H3K4me<sup>3</sup> levels (red) for CCNE1, CDKN1C and CCND1.</p>", "links"=>[], "tags"=>["genes", "occurs"], "article_id"=>160371, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics", "Developmental Biology"], "users"=>["Alex Gutteridge", "J. Michael Rukstalis", "Daniel Ziemek", "Mark Tié", "Lin Ji", "Rebeca Ramos-Zayas", "Nancy A. Nardone", "Lisa D. Norquay", "Martin B. Brenner", "Kim Tang", "John D. McNeish", "Rebecca K. Rowntree"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0056024.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Expression_of_cell_cycle_genes_occurs_in_bursts_/160371", "title"=>"Expression of cell cycle genes occurs in bursts.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 00:06:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/490016"], "description"=>"<p>(A) The cholesterol biosynthesis pathway from squalene-PP (top left) to cholesterol (top right) showing all enzymes and some intermediates. Enzymes whose gene expression patterns fall into the largest cluster seen in the heatmap of the expression levels for these enzymes (B) are colored purple, other enzymes are light blue. LIPA and SOAT1/2 interconvert cholesterol and cholesterol ester. The cholesterol sensitive miRNA-33a shows a similar expression pattern (C) to the main cluster.</p>", "links"=>[], "tags"=>["metabolizing", "genes", "shows"], "article_id"=>160529, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics", "Developmental Biology"], "users"=>["Alex Gutteridge", "J. Michael Rukstalis", "Daniel Ziemek", "Mark Tié", "Lin Ji", "Rebeca Ramos-Zayas", "Nancy A. Nardone", "Lisa D. Norquay", "Martin B. Brenner", "Kim Tang", "John D. McNeish", "Rebecca K. Rowntree"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0056024.g007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Expression_of_cholesterol_metabolizing_genes_shows_a_strong_time_dependence_/160529", "title"=>"Expression of cholesterol metabolizing genes shows a strong time dependence.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 00:08:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/490168"], "description"=>"<p>(A) The KDM5B, E2F1 and NEUROG3 pathway described in the text. Arrows within the blue boxes indicate predicted increases (green) or decreases (red) in activity of the given protein between day 8 and day 11. The pie charts indicate the number of gene expression changes between day 8 and day 11 that are correctly (blue) and incorrectly (red) predicted. Genes regulated by each protein are indicated on the right. Blue arrows indicate activation of gene expression by the protein and red arrows indicate inhibition. The red stop symbol indicates that KDM5B is a known inhibitor of E2F1 expression and the green cross that E2F1 is a known activator of NEUROG3 expression. (B) The IL6, SOCS3 and STAT3 pathway. All details as for (A). (C) A network of causal drivers for gene expression changes between day 8 and day 11. Species predicted to have increased activity at day 11 are given in yellow boxes, species predicted to have decreased activity are given in purple. The two pathways discussed in the text are highlighted by dashed lines and red arrows. Grey arrows indicate known regulatory interactions between species (both activating and inhibiting).</p>", "links"=>[], "tags"=>["identifies", "pathways", "stages", "endocrine", "pancreas"], "article_id"=>160673, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics", "Developmental Biology"], "users"=>["Alex Gutteridge", "J. Michael Rukstalis", "Daniel Ziemek", "Mark Tié", "Lin Ji", "Rebeca Ramos-Zayas", "Nancy A. Nardone", "Lisa D. Norquay", "Martin B. Brenner", "Kim Tang", "John D. McNeish", "Rebecca K. Rowntree"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0056024.g008"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_CRE_identifies_a_number_of_novel_pathways_potentially_involved_in_the_final_stages_of_endocrine_pancreas_development_/160673", "title"=>"CRE identifies a number of novel pathways potentially involved in the final stages of endocrine pancreas development.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 00:11:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/490286"], "description"=>"<p>(A) Treatment of pancreatic aggregates with IL-6 induces de novo gene expression of the pro-endocrine transcription factors NEUROG3 and NKX2.2, indicating commitment of pancreatic progenitor cells into the endocrine lineage. Noggin induction of these genes resulted in 8-fold increases (data not shown) (B) Gene expression in response to IL-6 was compared between whole aggregates (mixture of pancreatic progenitors and endocrine cells) and cultures of enriched endocrine cells (depleted of pancreatic progenitors). Induction of NKX2.2 expression was only seen in whole aggregates, consistent with the role of IL-6 in converting pancreatic progenitors into new endocrine cells. Enhanced expression of NEUROD1, IAPP, and SOMATOSTATIN seen in response to IL-6 in purified endocrine cells, suggesting IL-6 has additional roles in committed endocrine cells. No significant differences seen in INSULIN or GCG gene expression. Statistical testing using a standard t-test was performed.</p>", "links"=>[], "tags"=>["cre", "endocrine"], "article_id"=>160800, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics", "Developmental Biology"], "users"=>["Alex Gutteridge", "J. Michael Rukstalis", "Daniel Ziemek", "Mark Tié", "Lin Ji", "Rebeca Ramos-Zayas", "Nancy A. Nardone", "Lisa D. Norquay", "Martin B. Brenner", "Kim Tang", "John D. McNeish", "Rebecca K. Rowntree"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0056024.g009"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_IL6_the_top_CRE_prediction_has_effects_on_expression_of_endocrine_markers_/160800", "title"=>"IL6, the top CRE prediction, has effects on expression of endocrine markers.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 00:13:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/490403"], "description"=>"<p>The number of correctly, incorrectly and ambiguously explained gene expression observations are given for each gene as well as the predicted direction of regulation (up meaning activation/down meaning inhibition). The notes for each gene indicate that in cases where the gene is already associated with beta cell function whether it is generally considered a positive or negative regulator of beta cell differentiation, proliferation (growth) or apoptosis. All hypotheses pass correctness and enrichment p-value thresholds of 10<sup>−5</sup>.</p>", "links"=>[], "tags"=>["20", "causal", "drivers", "pancreatic", "endoderm"], "article_id"=>160910, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics", "Developmental Biology"], "users"=>["Alex Gutteridge", "J. Michael Rukstalis", "Daniel Ziemek", "Mark Tié", "Lin Ji", "Rebeca Ramos-Zayas", "Nancy A. Nardone", "Lisa D. Norquay", "Martin B. Brenner", "Kim Tang", "John D. McNeish", "Rebecca K. Rowntree"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0056024.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Top_20_protein_causal_drivers_of_early_pancreatic_endoderm_formation_between_day_8_and_day_11_/160910", "title"=>"Top 20 protein causal drivers of early pancreatic endoderm formation between day 8 and day 11.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-13 00:15:10"}

PMC Usage Stats | Further Information

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Relative Metric

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