Additional file 1: Table S1. of Association between off-hour presentation and endotracheal-intubation-related adverse events in trauma patients with a predicted difficult airway: A historical cohort study at a community emergency department in Japan

The ED census from 2007 to 2016. (DOCX 14 kb

An Integrated Cell Purification and Genomics Strategy Reveals Multiple Regulators of Pancreas Development

<div><p>The regulatory logic underlying global transcriptional programs controlling development of visceral organs like the pancreas remains undiscovered. Here, we profiled gene expression in 12 purified populations of fetal and adult pancreatic epithelial cells representing crucial progenitor cell subsets, and their endocrine or exocrine progeny. Using probabilistic models to decode the general programs organizing gene expression, we identified co-expressed gene sets in cell subsets that revealed patterns and processes governing progenitor cell development, lineage specification, and endocrine cell maturation. Purification of <i>Neurog3</i> mutant cells and module network analysis linked established regulators such as <i>Neurog3</i> to unrecognized gene targets and roles in pancreas development. Iterative module network analysis nominated and prioritized transcriptional regulators, including diabetes risk genes. Functional validation of a subset of candidate regulators with corresponding mutant mice revealed that the transcription factors <i>Etv1</i>, <i>Prdm16</i>, <i>Runx1t1</i> and <i>Bcl11a</i> are essential for pancreas development. Our integrated approach provides a unique framework for identifying regulatory genes and functional gene sets underlying pancreas development and associated diseases such as diabetes mellitus.</p></div

Gene-module network reveals candidate pancreas regulators.

<p>(A) Normalized expression values of Prdm16 in sorted cells. (B) Normalized expression values of Bcl11a from purified cell populations. (C) Relative mRNA expression in Bcl11a mutant mice (n = 4) and control mice (n = 4) in sorted cells enriched for endocrine cells at E15. (D) Normalized expression values for Etv1 from purified cell populations. (E) Relative mRNA expression of pancreatic markers in Etv1 mutant (n = 4) and control (n = 4) pancreata at E18. (F) Cell mass changes in PP cells in Etv1 mutant mice at birth (n = 3). (G) Normalized expression values for Runx1t1 from purified cell populations. (H) Relative gene expression in Runx1t1 mutant mice (n = 4) and controls (n = 4) at E18 from whole pancreata. In (B–G), data are represented as mean +/− SEM. In (C), (E), (H) expression levels were normalized to <i>beta-actin</i> and results are shown relative to littermate controls, (A), (B), (D), (G) represent raw values obtained from microarray analysis.</p

Expression-based identification of pancreatic regulators.

<p>(A) Schematic of approach used to identify regulators of pancreas development, their targets, and their predicted biological functions using the module network algorithm of Genomica. To identify regulators two lists are loaded into the program: 1) a list of potential regulators and 2) normalized expression values of samples. Genes with similar expression patterns are grouped (termed a module). Regulators that are most predictive of a specific module expression pattern are learned. Output information includes a list of regulators and their potential targets. Functional enrichment analysis is used to predict the biological function of each regulator (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004645#s4" target="_blank">Methods</a> for details). An example of module-network analysis nominating Neurog3 as a candidate regulator of endocrine development is shown along with its potential targets. (B) Optimal number of modules and iterations were determined by calculating the percentage of known regulators of pancreas development for each module and iteration combination. (C) Gene set enrichment analysis (GSEA) for 100 iterations of 75 modules yielded an enrichment score greater than >0.5 when known regulators were used. Distribution of known regulators based on their rank is displayed on the top panel. (D) Ranking of candidate regulators based on their frequency. Among the most reproducible candidates included known pancreas regulators such as <i>Pdx1</i> and <i>Neurog3</i> (red font) and candidate regulators validated in subsequent analysis (red font). (E) GSEA plot for the distribution of diabetes risk factors among list of predicted regulators. (F) Ranking of diabetes risk factors based on their frequency score. Validated GWAS genes include Bcl11a (red). (D and F) A frequency of 1.0 means that the candidate regulator appeared in 100% of the iterations performed.</p

Module map analysis of differentially expressed gene sets.

<p>The module map algorithm of Genomica software was executed to identify gene-sets (representing gene ontology biological functions) that are differentially expressed between 12 cell populations representing various stages and cell types of the pancreas. Each individual block represents the average expression of statistically enriched (yellow) or depleted (teal) genes based on a log2 scale (<i>P</i><0.05 and FDR<0.05, Cut-off values >1 or <−1, based on a log2 scale). Black blocks indicate that there was no significant enrichment or depletion of a gene-set. Because of resolution and space constraints not all gene set terms are displayed (signified with dots). Endocrine progenitor (EP).</p

Acquisition and analysis of global gene-expression.

<p>(A) Schematic of experiments in this study. (B) Lineage-diagram of pancreas development. The following cell types were collected: E11 and E15 pancreatic progenitors, E15 acinar cells, E15 endocrine progenitors (EP), E15, E17, P1, P15, 8–12 week beta cells, P1 and 8–12 week alpha cells, and adult duct cells. The sort strategy is displayed in blue. Each sample was collected in at least triplicate. MIP: Mouse Insulin Promoter, GcgVenus: Glucagon-Venus. (C) Pearson correlation plot and hierarchical clustering (right) of 12 cell populations. The Pearson correlation coefficient was calculated on mean-centered normalized expression values of a subset of significant expressed genes (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004645#s4" target="_blank">Methods</a> for details). A positive correlation is portrayed in yellow and a negative correlation in purple.</p

Identifying biological functions and targets of Neurog3.

<p>(A) Venn diagram displaying the number of predicted activated targets of Neurog3 using the module network algorithm of Genomica based on a cut-off value of two-fold (orange), and the number of genes that are downregulated upon the loss of Neurog3 by a two-fold difference based on expression profiling of E15 <i>Neurog3</i>-null cells (yellow). Overlap of a subset of activated Neurog3 target genes is shown to the right. Validated targets are in red. Fisher's exact test was used to calculate the <i>P</i>-value. (B) mRNA expression of a subset of nominated regulators (<i>Etv1</i>, <i>Prdm16</i>, <i>Runxt1t1</i>, and <i>Bcl11a</i>) in <i>Neurog3</i> mutant pancreata (n = 3) and control mice (n = 3) at E15. (C) Adeno-based overexpression of Neurog3 in ductal cell line (mPAC) and its effect on <i>Runx1t1</i>, <i>Bcl11a</i>, <i>Etv1</i>, and <i>Prdm16</i> expression. (n = 3,each). (D) Immunohistochemistry showing the expression of Runx1t1 (red) in a subset of Neurog3-eGFP⁺ cells in heterozygous Neurog3^eGFP/+ (left panel). Loss of Runx1t1 (red) in the epithelium of <i>Neurog3</i>-null pancreas (right panel). No change in expression of Runx1t1 (red) in mesenchymal cells in <i>Neurog3</i>-null pancreas. Epithelial cells are labeled with E-cadherin (white). (E) Genomica-based predicted biological functions of Neurog3 based on the target genes that were positively correlated with the expression of Neurog3. (F) Biological functions of targets based on expression profiling of Neurog3⁺ endocrine progenitor cells and E15 <i>Neurog3</i>-null cells based on a 2-fold difference. (B–C) data are represented as mean +/− SEM. (D–E) functional enrichment analysis for each set of targets genes was performed through DAVID. FDR<0.05.</p