Introduction

This dataset contains nine tissues (heart, hippocampus, hypothalamus, kidney, liver, prefrontal cortex, skeletal muscle, small intestine, and spleen) from C57BL/6J mice that were fed 2-deoxyglucose (6g/L) through their drinking water for 96hrs or 4wks. 96hr mice were given their 2DG treatment 2 weeks after the other cohort started the 4 week treatment. The organs from the mice were harvested and processed for metabolomics and transcriptomics. The data in this document pertains to the transcriptomics data only. The counts that were used were FPKM normalized before being log transformed. It was determined that sample A113 had low RNAseq quality and through further analyses with PCA, MA plots, and clustering was an outlier and will be removed for the rest of the analyses performed. This document will determine which pathways are significantly altered by each module.

needed.packages <- c("tidyverse", "here", "functional", "gplots", "dplyr", "GeneOverlap", "R.utils", "reshape2","magrittr","data.table", "RColorBrewer","preprocessCore", "ARTool","emmeans", "phia", "gprofiler2", "rlist","plotly","downloadthis")
for(i in 1:length(needed.packages)){library(needed.packages[i], character.only = TRUE)}

source(here("source_files","WGCNA_source.R"))

Module Bar plot

This bar plot shows the number of genes in each module.

modules<-read.csv(here("Data","Brain","log.tdata.FPKM.sample.info.subset.hip.hyp.cortex.WGCNA.module.membership.csv"), header=T)

module_barplot(modules)

Pathway Analysis

Pathway analysis was performed using the gprofiler package. Genes associated with each module were compared against KEGG and REACTOME databases. Modules that did not contain any significant pathways are blank.

Pathway plots

WGCNA<-read.table(here("Data","Brain","log.tdata.FPKM.sample.info.subset.hip.hyp.cortex.WGCNA.module.membership.csv"), header=T)

Data_setup(WGCNA, file = "Annotated_genes_in_hip_hyp_cortex_WGCNA_Chang_B6_96hr_4wk.RData", folder="Brain")

Matched<-readRDS(here("Data","Brain","Annotated_genes_in_hip_hyp_cortex_WGCNA_Chang_B6_96hr_4wk.RData"))
  
pathways(Matched, pathwayfile = "Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_hip_hyp_cortex_WGCNA.RData", genefile = "Chang_B6_96hr_4wk_gprofiler_gene_annotation_list_hip_hyp_cortex_WGCNA.RData", folder = "Brain")
  
WGCNA.pathway <-readRDS(here("Data","Brain","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_hip_hyp_cortex_WGCNA.RData"))

aliceblue

antiquewhite

blanchedalmond

brown1

brown3

burlywood

chocolate3

coral3

cyan

darkgrey

darkolivegreen4

darkseagreen

darkslateblue

deeppink2

dodgerblue4

firebrick4

green3

green4

grey

honeydew

indianred3

lavenderblush1

lavenderblush2

lavenderblush3

lightblue1

lightblue2

lightcoral

magenta1

magenta3

magenta4

mediumorchid3

mediumpurple

orange3

orangered1

orangered4

paleturquoise4

pink2

plum

royalblue

thistle3

wheat3

Table of Pathways

WGCNA.pathway <- readRDS(here("Data","Brain","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_hip_hyp_cortex_WGCNA.RData"))

Aliceblue

if(class(WGCNA.pathway[[1]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[1]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Antiquewhite

if(class(WGCNA.pathway[[2]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[2]][c(11,3:6)])}

Blanchedalmond

if(class(WGCNA.pathway[[3]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[3]][c(11,3:6)])}

Brown1

if(class(WGCNA.pathway[[4]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[4]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Brown3

if(class(WGCNA.pathway[[5]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[5]][c(11,3:6)])}

Burlywood

if(class(WGCNA.pathway[[6]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[6]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Chocolate3

if(class(WGCNA.pathway[[7]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[7]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Coral3

if(class(WGCNA.pathway[[8]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[8]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Cyan

if(class(WGCNA.pathway[[9]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[]][c(11,3:6)])}

Darkgrey

if(class(WGCNA.pathway[[10]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[10]][c(11,3:6)])}

Darkolivegreen4

if(class(WGCNA.pathway[[11]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[11]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Darkseagreen

if(class(WGCNA.pathway[[12]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[12]][c(11,3:6)])}

Darkslateblue

if(class(WGCNA.pathway[[13]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[13]][c(11,3:6)])}

Deeppink2

if(class(WGCNA.pathway[[14]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[14]][c(11,3:6)])}

Dodgerblue4

if(class(WGCNA.pathway[[15]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[15]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Firebrick4

if(class(WGCNA.pathway[[16]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[16]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Green3

if(class(WGCNA.pathway[[17]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[17]][c(11,3:6)])}

Green4

if(class(WGCNA.pathway[[18]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[18]][c(11,3:6)])}

Grey

if(class(WGCNA.pathway[[19]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[19]][c(11,3:6)])}

Honeydew

if(class(WGCNA.pathway[[20]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[20]][c(11,3:6)])}

Indianred3

if(class(WGCNA.pathway[[21]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[21]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Lavenderblush1

if(class(WGCNA.pathway[[22]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[22]][c(11,3:6)])}

Lavenderblush2

if(class(WGCNA.pathway[[23]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[23]][c(11,3:6)])}

Lavenderblush3

if(class(WGCNA.pathway[24]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[24]][c(11,3:6)])}

Lightblue1

if(class(WGCNA.pathway[[25]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[25]][c(11,3:6)])}

Lightblue2

if(class(WGCNA.pathway[[26]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[26]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Lightcoral

if(class(WGCNA.pathway[[27]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[27]][c(11,3:6)])}

Magenta1

if(class(WGCNA.pathway[[28]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[28]][c(11,3:6)])}

Magenta3

if(class(WGCNA.pathway[[29]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[29]][c(11,3:6)])}

Magenta4

if(class(WGCNA.pathway[[30]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[30]][c(11,3:6)])}

Mediumorchid3

if(class(WGCNA.pathway[[31]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[31]][c(11,3:6)])}

Mediumpurple

if(class(WGCNA.pathway[[32]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[32]][c(11,3:6)])}

Orange3

if(class(WGCNA.pathway[[33]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[33]][c(11,3:6)])}

Orangered1

if(class(WGCNA.pathway[[34]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[34]][c(11,3:6)])}

Orangered4

if(class(WGCNA.pathway[[35]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[35]][c(11,3:6)])}

Paleturquoise4

if(class(WGCNA.pathway[[36]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[36]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Pink2

if(class(WGCNA.pathway[[37]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[37]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Plum

if(class(WGCNA.pathway[[38]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[38]][c(11,3:6)])}

Royalblue

if(class(WGCNA.pathway[[39]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[39]][c(11,3:6)])}

Thistle3

if(class(WGCNA.pathway[[40]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[40]][c(11,3:6)])}

Wheat3

if(class(WGCNA.pathway[[41]]) == "numeric"){
   print("No pathways significantly overrepresented")
   } else {DT.table.path(WGCNA.pathway[[41]][c(11,3:6)])}

[1] “No pathways significantly overrepresented”

Pathway Frequency

WGCNA.pathway <- readRDS(here("Data","Brain","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_hip_hyp_cortex_WGCNA.RData"))

#pdf("Counts_of_each_pathway_identified_within_brain.pdf")
count <- count.pathways(WGCNA.pathway)

[1] 860 [1] 564

#dev.off()

Frequency Table

DT.table.freq(count)

Frequency Plot

  #pdf("Counts_of_each_pathway_identified_within_jaccard_index.pdf")
  p <- ggplot(data=count,aes(x=list.pathways,y=Freq))
  p <- p + geom_bar(color="black", fill=colorRampPalette(brewer.pal(n = 12, name = "Paired"))(length(count[,1])), stat="identity",position="identity") + theme_classic()
  p <- p + theme(axis.text.x = element_text(angle = 90, hjust =1, size = 3)) + scale_x_discrete(labels=count$list.pathways) + xlab("Modules")
  print(p)

  #dev.off()

Analysis performed by Ann Wells

The Carter Lab The Jackson Laboratory 2023

ann.wells@jax.org