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Pancreatic β-cells undergo profound hyperplasia during pregnancy to maintain maternal euglycemia. Failure to reprogram β-cells into a more replicative state has been found to underlie susceptibility to gestational diabetes mellitus (GDM). We recently identified a requirement for prolactin receptor (PRLR) signaling in the metabolic adaptations to…

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Computational Methods Defining the Impact of Pregnancy-Specific Prolactin Receptor Signaling on Pancreatic Islet Gene Expression in Mice

Authors: Mark E. Pepin, PhD; Hayden H. Bickerton, BS; Maigen Bethea, BS; Chad S. Hunter, PhD; Adam R. Wende, PhD; Ronadip R. Banerjee, MD, PhD

Introduction

This document summarizes the computational methods that were used to examine the contribution of prolactin receptor-mediated control of beta cell transcriptional reprogramming on metabolic adaptation to pregnancy. The Supplemental Tables (as .xlsx file) and Supplemental Figures (as .pptx file) are located in files above this document (available for download).

Microarray Pre-processing

Before differential expression could be generated, the Affymetrix array was first pre-processed for numerous quality assessments using the oligo package in r. The raw data were first adjusted for background signal noise, as well as normalized across samples using the quantile normalization and summarization as previously described (see oligo methods).

## try http:// if https:// URLs are not supported
# source("https://bioconductor.org/biocLite.R")
# biocLite("pd.mogene.2.0.st")
# biocLite("affy")
# biocLite("limma")
# biocLite("oligo")
#biocLite("mogene20sttranscriptcluster.db")
#biocLite("mogene20stprobeset.db")
##Load all required packages
library(affy)
library(limma)
library(pd.mogene.2.0.st)
library(oligo)
library(mogene20stprobeset.db)
library(mogene20sttranscriptcluster.db)
library(openxlsx)
## import "phenotype" data, describing the experimental design
celFiles <- list.celfiles('../1_Input/Microarray', full.names=TRUE)
rawData <- read.celfiles(celFiles)
## Reading in : ../1_Input/Microarray/Ronadip Banerjee_RB-C2_(MoGene-2_0-st).CEL
## Reading in : ../1_Input/Microarray/Ronadip Banerjee_RB-C3_(MoGene-2_0-st).CEL
## Reading in : ../1_Input/Microarray/Ronadip Banerjee_RB-C4_(MoGene-2_0-st).CEL
## Reading in : ../1_Input/Microarray/Ronadip Banerjee_RB-KO1_(MoGene-2_0-st).CEL
## Reading in : ../1_Input/Microarray/Ronadip Banerjee_RB-KO3_(MoGene-2_0-st).CEL
## Reading in : ../1_Input/Microarray/Ronadip Banerjee_RB-KO4_(MoGene-2_0-st).CEL
##MA Plot
xl <- c(2.8, 4) 
yl <- c(-1, 1) 
MAplot(rawData[, 1:6], pairs=TRUE, ylim=yl, xlim=xl, plotFun=plot)

pdf(file = "../2_Output/MA.Plot_bPRLRKO.pdf", height = 4, width = 6)
MAplot(rawData[, 1:6], pairs=TRUE, ylim=yl, xlim=xl, plotFun=plot)
dev.off()
## quartz_off_screen 
##                 2
#Background and noise subtraction, with log transformation normalization
eset<-rma(rawData)
## Background correcting
## Normalizing
## Calculating Expression
#add sample information
Index<-read.xlsx("../1_Input/Index.xlsx", rowNames = T)
Index.fix<-AnnotatedDataFrame(Index)
phenoData(eset)<-Index.fix
#
crma=exprs(eset)
rma=format(crma, digits=5)
rma_annotated<-merge(rma, mogene20sttranscriptclusterSYMBOL, by.x = 0, by.y = 1)
rma_annotated<-merge(rma_annotated, mogene20sttranscriptclusterENTREZID, by.x = "Row.names", by.y = 1)
rma_annotated<-dplyr::rename(rma_annotated, Affy_id = Row.names, Gene.Symbol = symbol, Entrez_id=gene_id)
rownames(rma_annotated)<-rma_annotated$Affy_id
write.table(rma_annotated, file = "rma.txt", quote = FALSE, sep = "\t", row.names = FALSE, col.names = TRUE)
#generate the design matrix
design<-model.matrix(~factor(c(0, 0, 0, 1, 1, 1)))
rownames(design)<-celFiles
colnames(design)<-c("CON", "KO")
fit <- lmFit(eset, design) 
ebayes <- eBayes(fit) 
lod <- -log10(ebayes[["p.value"]][,2]) 
mtstat<- ebayes[["t"]][,2]
tab <- topTable(ebayes, coef=2, adjust="fdr", number=nrow(ebayes))
tab$Affy_id<-rownames(tab)
table<-dplyr::inner_join(tab, rma_annotated, by="Affy_id")
results<-table
results_p05<-dplyr::filter(results, P.Value<0.05)
results_q05<-dplyr::filter(results, adj.P.Val<0.05)
#Export data as tabbed excel workbook
library(openxlsx)
wb_DESeq<-createWorkbook()
#Unfiltered
  addWorksheet(wb_DESeq, "Unfiltered")
  writeData(wb_DESeq, "Unfiltered", results, startCol = 1)
#P-value Significant (0.05)
  addWorksheet(wb_DESeq, "P < 0.05")
  writeData(wb_DESeq, "P < 0.05", results_p05, startCol = 1)
#Q-value Significant (0.05)
  addWorksheet(wb_DESeq, "Q < 0.05")
  writeData(wb_DESeq, "Q < 0.05", results_q05, startCol = 1)
saveWorkbook(wb_DESeq, file = "../2_Output/bPRLR.KO_Diff.EX_Results.xlsx", overwrite = TRUE)

results_ebayes<-as.data.frame(ebayes)
results_ebayes<-merge(results_ebayes, crma, by=0)

Data Visualizations

QQ Plot

#Create Q-Q plot
library(Haplin)
test<-table
test<-test[complete.cases(test),]
pQQ(test$P.Value, lim=c(0,10))

pdf(file = "../2_Output/QQ.Plot_bPRLRKO.pdf", height = 4, width = 6)
pQQ(test$P.Value, lim=c(0,10))
dev.off()
## quartz_off_screen 
##                 2

Volcano Plot

# Load packages
library(dplyr)
library(ggplot2)
library(ggrepel)
library(openxlsx)
# Read data from the web
results<-read.xlsx("../2_Output/bPRLR.KO_Diff.EX_Results.xlsx", sheet = "Unfiltered")
results = mutate(results, sig=ifelse(results$P.Value<0.05 & abs(results$logFC)>0.585, "Q < 0.05 and |FC| > 1.5", "Not Sig"))
results = mutate(results, minuslogp=-log(P.Value))
#plot the ggplot
p = ggplot(results, aes(logFC, minuslogp)) + theme(panel.background = element_rect("white", colour = "black", size=2), panel.grid.major = element_line(colour = "gray50", size=.75), panel.grid.minor = element_line(colour = "gray50", size=0.4)) + 
geom_point(aes(fill=sig), colour="black", shape=21) + labs(x=expression(Log[2](Fold-Change)), y=expression(-Log[10](P-value))) + xlim(-5.5,2)+  geom_hline(yintercept = 0, size = 1) + geom_vline(xintercept=0, size=1)+ 
scale_fill_manual(values=c("black", "tomato"))
#add a repelling effect to the text labels.
p+geom_text_repel(data=filter(results, adj.P.Val<0.01), aes(label=Gene.Symbol))

pdf(file = "../2_Output/Volcano.Plot_bPRLRKO.pdf", height = 4, width = 6)
p+geom_text_repel(data=filter(results, adj.P.Val<0.01), aes(label=Gene.Symbol))
dev.off()
## quartz_off_screen 
##                 2

Principal Components Analysis

#Plot Features of the PCA
library(dplyr)
library(plotly)
##Import the data to be used for PCA
results_DEG<-dplyr::select(results, contains("Ronadip"))
results_DEG$`Ronadip.Banerjee_RB-C2_(MoGene-2_0-st).CEL`<-as.numeric(as.character(results_DEG$`Ronadip.Banerjee_RB-C2_(MoGene-2_0-st).CEL`))
results_DEG$`Ronadip.Banerjee_RB-C3_(MoGene-2_0-st).CEL`<-as.numeric(as.character(results_DEG$`Ronadip.Banerjee_RB-C3_(MoGene-2_0-st).CEL`))
results_DEG$`Ronadip.Banerjee_RB-C4_(MoGene-2_0-st).CEL`<-as.numeric(as.character(results_DEG$`Ronadip.Banerjee_RB-C4_(MoGene-2_0-st).CEL`))
results_DEG$`Ronadip.Banerjee_RB-KO1_(MoGene-2_0-st).CEL`<-as.numeric(as.character(results_DEG$`Ronadip.Banerjee_RB-KO1_(MoGene-2_0-st).CEL`))
results_DEG$`Ronadip.Banerjee_RB-KO3_(MoGene-2_0-st).CEL`<-as.numeric(as.character(results_DEG$`Ronadip.Banerjee_RB-KO3_(MoGene-2_0-st).CEL`))
results_DEG$`Ronadip.Banerjee_RB-KO4_(MoGene-2_0-st).CEL`<-as.numeric(as.character(results_DEG$`Ronadip.Banerjee_RB-KO4_(MoGene-2_0-st).CEL`))
#transpose the dataset (required for PCA)
data.pca<-t(results_DEG)
data.pca<-as.data.frame(data.pca)
##merge the file
Index<-design
rownames(Index)<-row.names(data.pca)
data.pca_Final<-merge(Index, data.pca, by=0)
rownames(data.pca_Final)<-data.pca_Final$Row.names
data.pca_Final<-dplyr::select(data.pca_Final, -Row.names)
pca.comp<-prcomp(data.pca_Final[,(ncol(Index)+1):ncol(data.pca_Final)])

pcaCharts=function(x) {
    x.var <- x$sdev ^ 2
    x.pvar <- x.var/sum(x.var)
    par(mfrow=c(2,2))
    plot(x.pvar,xlab="Principal component", 
         ylab="Proportion of variance", ylim=c(0,1), type='b')
    plot(cumsum(x.pvar),xlab="Principal component", 
         ylab="Cumulative Proportion of variance", 
         ylim=c(0,1), 
         type='b')
    screeplot(x)
    screeplot(x,type="l")
    par(mfrow=c(1,1))
}
pcaCharts(pca.comp)

write.csv(data.pca, "../2_Output/data.pca.csv")
library(ggfortify)
library(cluster)
autoplot(pca.comp, data = data.pca_Final, colour = "KO")

Heatmap and Hierarchical Clustering

library(pheatmap)
results_DEG<-dplyr::select(results_p05, `Ronadip Banerjee_RB-C2_(MoGene-2_0-st).CEL`:`Ronadip Banerjee_RB-KO4_(MoGene-2_0-st).CEL`)
results_DEG$`Ronadip Banerjee_RB-C2_(MoGene-2_0-st).CEL`<-as.numeric(as.character(results_DEG$`Ronadip Banerjee_RB-C2_(MoGene-2_0-st).CEL`))
results_DEG$`Ronadip Banerjee_RB-C3_(MoGene-2_0-st).CEL`<-as.numeric(as.character(results_DEG$`Ronadip Banerjee_RB-C3_(MoGene-2_0-st).CEL`))
results_DEG$`Ronadip Banerjee_RB-C4_(MoGene-2_0-st).CEL`<-as.numeric(as.character(results_DEG$`Ronadip Banerjee_RB-C4_(MoGene-2_0-st).CEL`))
results_DEG$`Ronadip Banerjee_RB-KO1_(MoGene-2_0-st).CEL`<-as.numeric(as.character(results_DEG$`Ronadip Banerjee_RB-KO1_(MoGene-2_0-st).CEL`))
results_DEG$`Ronadip Banerjee_RB-KO3_(MoGene-2_0-st).CEL`<-as.numeric(as.character(results_DEG$`Ronadip Banerjee_RB-KO3_(MoGene-2_0-st).CEL`))
results_DEG$`Ronadip Banerjee_RB-KO4_(MoGene-2_0-st).CEL`<-as.numeric(as.character(results_DEG$`Ronadip Banerjee_RB-KO4_(MoGene-2_0-st).CEL`))
##Index file for annotating samples
Annotation<-Index
rownames(Index)<-colnames(results_DEG)
paletteLength <- 100
myColor <- colorRampPalette(c("dodgerblue4", "white", "gold2"))(paletteLength)
pheatmap(results_DEG, 
         cluster_cols=T, 
         border_color=NA, 
         cluster_rows=T, 
         scale = 'row', 
         show_colnames = F, 
         show_rownames = F, 
         color = myColor)

Circular Genome Plot of Differentially-Expressed EZH2 Targets

##Create a circular genome plot of EZH2 and its targets that are Diff. Exp'd
library(dplyr)
library(openxlsx)
#Import "Links" (the PRLR_DEGGs)
Gene_Labels<-read.xlsx("../1_Input/Circos/Links_EZH2.xlsx")
#Re-order based on chromosome name
Gene_Labels<-arrange(Gene_Labels, chromStart)
Gene_Labels$chrom<-factor(Gene_Labels$chrom, levels=c("chr1", "chr2", "chr3", "chr4", 
                                                      "chr5", "chr6", "chr7", "chr8", 
                                                      "chr9", "chr10", "chr11", "chr12", 
                                                      "chr13", "chr14", "chr15", "chr16", 
                                                      "chr17", "chr18", "chr19", "chr20", 
                                                      "chr21", "chr22", "chr23", "chrX", 
                                                      "chrY"))
Gene_Labels<-Gene_Labels[order(Gene_Labels$chrom),]
Gene_Labels<-Gene_Labels[!duplicated(Gene_Labels[,4]),]

#Read in differential expression for lines and coloring
DiffEX_raw<-read.csv("../2_Output/Diff.EQ_ebayes.limma_180315mep.csv")
rownames(DiffEX_raw)<-DiffEX_raw$X
#Filter by EZH2 Targets
EZH2_PRLR.DEGGs<-merge(Gene_Labels, DiffEX_raw, by.x="GeneSymbol", by.y = "Symbols")
EZH2_PRLR.DEGGs<-dplyr::select(EZH2_PRLR.DEGGs, chrom, chromStart, chromEnd, logFC)
# Split for coloring (up = yellow, down = blue)
EZH2_PRLR.DEGGs_UP<-filter(EZH2_PRLR.DEGGs, logFC>0)
EZH2_PRLR.DEGGs_DOWN<-filter(EZH2_PRLR.DEGGs, logFC<0)

EZH2_PRLR.DEGGs_UP<-arrange(EZH2_PRLR.DEGGs_UP, chromStart)
EZH2_PRLR.DEGGs_UP$chrom<-factor(EZH2_PRLR.DEGGs_UP$chrom, levels=c("chr1", "chr2", "chr3", "chr4", 
                                                      "chr5", "chr6", "chr7", "chr8", 
                                                      "chr9", "chr10", "chr11", "chr12", 
                                                      "chr13", "chr14", "chr15", "chr16", 
                                                      "chr17", "chr18", "chr19", "chr20", 
                                                      "chr21", "chr22", "chr23", "chrX", 
                                                      "chrY"))
EZH2_PRLR.DEGGs_UP<-EZH2_PRLR.DEGGs_UP[order(EZH2_PRLR.DEGGs_UP$chrom),]
#Fold Change DOWN (RNA)
EZH2_PRLR.DEGGs_DOWN<-arrange(EZH2_PRLR.DEGGs_DOWN, chromStart)
EZH2_PRLR.DEGGs_DOWN$chrom<-factor(EZH2_PRLR.DEGGs_DOWN$chrom, levels=c("chr1", "chr2", "chr3", "chr4", 
                                                      "chr5", "chr6", "chr7", "chr8", 
                                                      "chr9", "chr10", "chr11", "chr12", 
                                                      "chr13", "chr14", "chr15", "chr16", 
                                                      "chr17", "chr18", "chr19", "chr20", 
                                                      "chr21", "chr22", "chr23", "chrX", 
                                                      "chrY"))
EZH2_PRLR.DEGGs_DOWN<-EZH2_PRLR.DEGGs_DOWN[order(EZH2_PRLR.DEGGs_DOWN$chrom),]

##Fold Change List
Gene_FoldChange_List<-list(EZH2_PRLR.DEGGs_UP, EZH2_PRLR.DEGGs_DOWN)

library(circlize)
library(gtools)
library(dplyr)
om = circos.par("track.margin")
oc = circos.par("cell.padding")
circos.par(track.margin = c(0, 0), cell.padding = c(0, 0, 0, 0))
circos.par(start.degree = 0)
circos.initializeWithIdeogram(track.height = 0.05)
### Labels for PRLR_DEGGs
circos.genomicLabels(Gene_Labels, labels.column=4, side='outside', cex=.8)
##DEGGs
circos.genomicTrackPlotRegion(Gene_FoldChange_List, 
                              ylim = c(-4, 4), bg.border=NA,
                              panel.fun = function(region, value, ...) {
 col = ifelse(value[[1]] > 0, "darkgoldenrod1", "blue")
 circos.genomicPoints(region, value, col = col, cex = 0.8, pch = 16)
 cell.xlim = get.cell.meta.data("cell.xlim")
 for(h in c(-4, -2, 0, 2, 4)) {
   circos.lines(cell.xlim, c(h, h), col ="#00000040")
 }
}, track.height = 0.1)

circos.par(track.margin=om, cell.padding=oc)
## Add link for all DEGs with DMRs in promoter CGIs
Link_Anchor <- read.xlsx("../1_Input/Circos/Links_EZH2.xlsx", sheet = "Anchor")
Link<-Gene_Labels
Link<-dplyr::select(Gene_Labels, -GeneSymbol)
Link$chrom<-factor(Link$chrom, levels=c("chr1", "chr2", "chr3", "chr4", 
                                                      "chr5", "chr6", "chr7", "chr8", 
                                                      "chr9", "chr10", "chr11", "chr12", 
                                                      "chr13", "chr14", "chr15", "chr16", 
                                                      "chr17", "chr18", "chr19", "chr20", 
                                                      "chr21", "chr22", "chr23", "chrX", 
                                                      "chrY"))
Link<-Link[order(Link$chrom),]
Link_Anchor<-Link_Anchor[1:nrow(Link),]
circos.genomicLink(Link, Link_Anchor, col="black", lwd=0.5)

\label{Circos_PRLR_DEGGs}Circular Genome Plot of all differentially-expressed genes associated with gestation (P<0.05).

circos.clear()

Cross-Study Candidate Validation

Based on the dataset published by Schraenen et. al 2015 (PMID), we wanted to determine whether the transcriptional changes due to PRLR-knockout were impacted by pregnancy alone. To test this, we acquired the expression data from GEO, and analyzed based on the following differential-expression analysis workflow. Because C57Bl6/J mice exhibited dysglycemia at day-30 of high-fat diet treatment, we chose to examine the time-point immediately prior to onset of dysglycemia (day 10) to minimize the confounding effects of the diabetic milieu.

## class: DESeqDataSet 
## dim: 21382 12 
## metadata(1): version
## assays(1): counts
## rownames(21382): 0610005C13Rik 0610007C21Rik ... Zzz3 l7Rn6
## rowData names(0):
## colnames(12): 1HF141 1HF142 ... 1RC121 1RC122
## colData names(4): Sample_ID Strain Diet Duration

## quartz_off_screen 
##                 2
## class: DESeqDataSet 
## dim: 21382 12 
## metadata(1): version
## assays(2): counts mu
## rownames(21382): 0610005C13Rik 0610007C21Rik ... Zzz3 l7Rn6
## rowData names(9): baseMean baseVar ... dispOutlier dispMAP
## colnames(12): 1HF141 1HF142 ... 1RC121 1RC122
## colData names(5): Sample_ID Strain Diet Duration sizeFactor

Comparison of bPRLR-KO DEGs with HFD-induced DEGs

We then examined whether High-fat feeding and pregnancy employed overlapping transcriptional pathways for beta cell adaptation. Based on these results, we see a clear divergence in the transcriptional signature.

library(Vennerable)
library(openxlsx)
bPRLRKO<-read.xlsx("../2_Output/bPRLR.KO_Diff.EX_Results.xlsx", sheet = "Q < 0.05")
HFD.Effect_10d<-read.xlsx("../2_Output/C57Bl6J_HFD.v.NC_10days_DESeq2.xlsx", sheet = "Q < 0.05")
HFD.Effect_30d<-read.xlsx("../2_Output/C57Bl6J_HFD.v.NC_30days_DESeq2.xlsx", sheet = "Q < 0.05")
#Create a list containing gene names for each analysis
Venn.list<-list(bPRLRKO$Gene.Symbol, HFD.Effect_30d$ensembl_gene_id)
venny<-Venn(Sets=Venn.list[1:2], SetNames = c("bPRLRKO", "Bellini HFD-30d"))
plot(venny, doWeights = T)

Supplemental Table: R Session Information

All packages and setting are acquired using the following command:

library(kableExtra)
sinfo<-devtools::session_info()
sinfo$platform
##  setting  value                       
##  version  R version 3.4.3 (2017-11-30)
##  os       macOS  10.14                
##  system   x86_64, darwin15.6.0        
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       America/Chicago             
##  date     2018-10-29
sinfo$packages %>% kable( 
                         align="c", 
                         longtable=T, 
                         booktabs=T,
                         caption="Packages and Required Dependencies") %>% 
    kable_styling(latex_options=c("striped", "repeat_header", "condensed"))
Packages and Required Dependencies
package ondiskversion loadedversion path loadedpath attached is_base date source md5ok library
acepack acepack 1.4.1 1.4.1 /Library/Frameworks/R.framework/Versions/3.4/Resources/library/acepack /Library/Frameworks/R.framework/Versions/3.4/Resources/library/acepack FALSE FALSE 2016-10-29 CRAN (R 3.4.0) /Library/Frameworks/R.framework/Versions/3.4/Resources/library
affxparser affxparser 1.50.0 1.50.0 /Library/Frameworks/R.framework/Versions/3.4/Resources/library/affxparser /Library/Frameworks/R.framework/Versions/3.4/Resources/library/affxparser FALSE FALSE 2017-10-31 Bioconductor /Library/Frameworks/R.framework/Versions/3.4/Resources/library
affy affy 1.56.0 1.56.0 /Library/Frameworks/R.framework/Versions/3.4/Resources/library/affy /Library/Frameworks/R.framework/Versions/3.4/Resources/library/affy TRUE FALSE 2017-10-31 Bioconductor /Library/Frameworks/R.framework/Versions/3.4/Resources/library
affyio affyio 1.48.0 1.48.0 /Library/Frameworks/R.framework/Versions/3.4/Resources/library/affyio /Library/Frameworks/R.framework/Versions/3.4/Resources/library/affyio FALSE FALSE 2017-10-31 Bioconductor /Library/Frameworks/R.framework/Versions/3.4/Resources/library
annotate annotate 1.56.2 1.56.2 /Library/Frameworks/R.framework/Versions/3.4/Resources/library/annotate /Library/Frameworks/R.framework/Versions/3.4/Resources/library/annotate FALSE FALSE 2018-03-22 Bioconductor /Library/Frameworks/R.framework/Versions/3.4/Resources/library
AnnotationDbi AnnotationDbi 1.40.0 1.40.0 /Library/Frameworks/R.framework/Versions/3.4/Resources/library/AnnotationDbi /Library/Frameworks/R.framework/Versions/3.4/Resources/library/AnnotationDbi TRUE FALSE 2017-10-31 Bioconductor /Library/Frameworks/R.framework/Versions/3.4/Resources/library
assertthat assertthat 0.2.0 0.2.0 /Library/Frameworks/R.framework/Versions/3.4/Resources/library/assertthat /Library/Frameworks/R.framework/Versions/3.4/Resources/library/assertthat FALSE FALSE 2017-04-11 CRAN (R 3.4.0) /Library/Frameworks/R.framework/Versions/3.4/Resources/library
backports backports 1.1.2 1.1.2 /Library/Frameworks/R.framework/Versions/3.4/Resources/library/backports /Library/Frameworks/R.framework/Versions/3.4/Resources/library/backports FALSE FALSE 2017-12-13 CRAN (R 3.4.3) /Library/Frameworks/R.framework/Versions/3.4/Resources/library
base64enc base64enc 0.1.3 0.1-3 /Library/Frameworks/R.framework/Versions/3.4/Resources/library/base64enc /Library/Frameworks/R.framework/Versions/3.4/Resources/library/base64enc FALSE FALSE 2015-07-28 CRAN (R 3.4.0) /Library/Frameworks/R.framework/Versions/3.4/Resources/library
bindr bindr 0.1.1 0.1.1 /Library/Frameworks/R.framework/Versions/3.4/Resources/library/bindr /Library/Frameworks/R.framework/Versions/3.4/Resources/library/bindr FALSE FALSE 2018-03-13 CRAN (R 3.4.3) /Library/Frameworks/R.framework/Versions/3.4/Resources/library
bindrcpp bindrcpp 0.2.2 0.2.2 /Library/Frameworks/R.framework/Versions/3.4/Resources/library/bindrcpp /Library/Frameworks/R.framework/Versions/3.4/Resources/library/bindrcpp TRUE FALSE 2018-03-29 CRAN (R 3.4.3) /Library/Frameworks/R.framework/Versions/3.4/Resources/library
Biobase Biobase 2.38.0 2.38.0 /Library/Frameworks/R.framework/Versions/3.4/Resources/library/Biobase /Library/Frameworks/R.framework/Versions/3.4/Resources/library/Biobase TRUE FALSE 2017-10-31 Bioconductor /Library/Frameworks/R.framework/Versions/3.4/Resources/library
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BiocInstaller BiocInstaller 1.28.0 1.28.0 /Library/Frameworks/R.framework/Versions/3.4/Resources/library/BiocInstaller /Library/Frameworks/R.framework/Versions/3.4/Resources/library/BiocInstaller TRUE FALSE 2017-10-31 Bioconductor /Library/Frameworks/R.framework/Versions/3.4/Resources/library
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Pancreatic β-cells undergo profound hyperplasia during pregnancy to maintain maternal euglycemia. Failure to reprogram β-cells into a more replicative state has been found to underlie susceptibility to gestational diabetes mellitus (GDM). We recently identified a requirement for prolactin receptor (PRLR) signaling in the metabolic adaptations to…

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