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","Hypothalamus","log.tdata.FPKM.sample.info.subset.hypothalamus.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","Hypothalamus","log.tdata.FPKM.sample.info.subset.hypothalamus.WGCNA.module.membership.csv"), header=T)

Data_setup(WGCNA, file = "Annotated_genes_in_hypothalamus_WGCNA_Chang_B6_96hr_4wk.RData", folder="Hypothalamus")

Matched<-readRDS(here("Data","Hypothalamus","Annotated_genes_in_hypothalamus_WGCNA_Chang_B6_96hr_4wk.RData"))
  
pathways(Matched, pathwayfile = "Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_hypothalamus_WGCNA.RData", genefile = "Chang_B6_96hr_4wk_gprofiler_gene_annotation_list_hypothalamus_WGCNA.RData", folder = "Hypothalamus")
  
WGCNA.pathway <-readRDS(here("Data","Hypothalamus","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_hypothalamus_WGCNA.RData"))

antiquewhite2

antiquewhite4

bisque4

black

blue

brown2

darkgreen

darkslateblue

darkturquoise

grey

indianred4

lavenderblush2

lightblue4

lightgreen

lightpink4

lightslateblue

lightsteelblue1

mediumpurple1

midnightblue

orangered1

plum

salmon4

skyblue

thistle3

Table of Pathways

WGCNA.pathway <- readRDS(here("Data","Hypothalamus","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_hypothalamus_WGCNA.RData"))

Antiquewhite2

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

Antiquewhite4

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

bisque4

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

Black

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

Blue

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

Brown2

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

Darkgreen

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”

Darkslateblue

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

Darkturquoise

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

Grey

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

[1] “No pathways significantly overrepresented”

Indianred

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

Lavenderblush2

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

[1] “No pathways significantly overrepresented”

lightblue4

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

Lightgreen

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

lightpink4

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”

Lightslateblue

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”

Lightsteelblue1

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

Mediumpurple1

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

[1] “No pathways significantly overrepresented”

Midnightblue

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

Orangred1

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

Plum

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

[1] “No pathways significantly overrepresented”

Salmon4

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

[1] “No pathways significantly overrepresented”

Skyblue

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

Thistle3

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

Pathway Frequency

WGCNA.pathway <- readRDS(here("Data","Hypothalamus","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_hypothalamus_WGCNA.RData"))

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

[1] 579 [1] 478

#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