Summary

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

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

There were 16 samples (4 per time and treatment). Mice were fed 2DG (6g/L) or nothing in their water for 96hrs or 2 weeks. WGCNA analysis was performed to determine gene clustering. A soft-thresholding power was chosen so that the scale-free topology correlation attempts to hit 0.9. The power chosen for kidney was 3, which allowed the correlations to hover around 0.90. This power clustered the genes into 24 modules originally, however, modules that were highly correlated (0.75 or higher) were further merged together to create 17 modules.

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

module_barplot(modules)

Pathways

Pathways were assessed for each module. Out of 17 modules, only 12 modules had pathway hits of any kind. The pathways that had the highest frequency across modules were metabolic pathways and collagen. The blue, cyan, pink, and red modules all contained these pathways.

Blue

47 pathways were identified for the blue module. The pathways identified were involved in cell cycle, BCAA metabolism, DNA pathways, general metabolism, post-translational modification, and Class 1 MHC processing and presentation.

Cyan

107 pathways identified for the cyan module. The pathways identified were involved in several cancers, insulin signaling, toll like receptors, and cancer pathways.

Darkgreen module

12 pathways were identified for the darkgreen module. The pathways identified were involved in DNA repair, PPAR signaling, and protein digestion and absorption.

Darkgrey

2 pathways were identified for the darkgrey module, ABC transporters and neurotrophin signaling pathway.

Darkred

2 pathways were identified for the darkred module, SLC-mediated transmembrane transport and transport of small molecules.

Darkturquoise

11 pathways were identified for the darkturquoise module. The pathways identified were involved in collagen, extracellular matrix, calcium and potassium channels, and protein digestion and absorption.

Grey60

4 pathways were identified for the grey60 module. The pathways involved were glutamatergic synapse, neurexins and neuroligins, neuronal system, and protein-protein interactions at synapses.

Lightgreen

2 pathways were identified for the lightgreen module, potassium channels and voltage gated potassium channels.

Lightyellow

3 pathways were identified for the lightyellow module, mitotic spindle checkpoint, phosphorylation of the APC/C, regulation fo APC/C activators between G1/S and early anaphase.

Magenta

3 pathways were identified for the magenta module, arrhythmogenic right ventricular cardiomyopathy, muscle contraction, and striated muscle contraction.

Pink

73 pathways were identified for the pink module. The pathways identified were involved in bile acid metabolism, chylomicron pathways, complement pathways, HDL pathways, innate immunity, general metabolism, plasma pathways, and VLDL pathways.

Red

101 pathways were identified for the red module. The pathways identified were involved in Alzheimer’s disease, external cellular response (hypoxia, stress), general metabolism, mRNA and RNA pathways, NAFLD, and triglyceride biosynthesis.

tdata.FPKM.sample.info <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_phenotypes.RData"))

tdata.FPKM <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_numeric.RData"))

log.tdata.FPKM <- log(tdata.FPKM + 1)
log.tdata.FPKM <- as.data.frame(log.tdata.FPKM)

log.tdata.FPKM.sample.info <- cbind(log.tdata.FPKM, tdata.FPKM.sample.info[,27238:27240])

log.tdata.FPKM.sample.info <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(rowname != "A113") %>% column_to_rownames()

log.tdata.FPKM.subset <- log.tdata.FPKM[,colMeans(log.tdata.FPKM != 0) > 0.5] 

log.tdata.FPKM.subset <- log.tdata.FPKM.subset %>% rownames_to_column() %>% filter(rowname != "A113") %>% column_to_rownames()

log.tdata.FPKM.sample.info.subset.kidney <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue == "Kidney") %>% column_to_rownames()

log.tdata.FPKM.subset <- subset(log.tdata.FPKM.sample.info.subset.kidney, select = -c(Time,Treatment,Tissue))
  
WGCNA.pathway <-readRDS(here("Data","Kidney","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_kidney_WGCNA.RData"))

Matched<-readRDS(here("Data","Kidney","Annotated_genes_in_kidney_WGCNA_Chang_B6_96hr_4wk.RData"))


module.names <- Matched$X..Module.
name <- str_split(module.names,"_")
samples <-c()
for(i in 1:length(name)){
samples[[i]] <- name[[i]][2]
}  
name <- str_split(samples,"\"")
name <- unlist(name)

Treatment <- unclass(as.factor(log.tdata.FPKM.sample.info.subset.kidney[,27238]))
Time <- unclass(as.factor(log.tdata.FPKM.sample.info.subset.kidney[,27237]))
Treat.Time <- paste0(Treatment, Time)
phenotype <- data.frame(cbind(Treatment, Time, Treat.Time))

nSamples <- nrow(log.tdata.FPKM.sample.info.subset.kidney)

MEs0 <- read.csv(here("Data","Kidney","log.tdata.FPKM.sample.info.subset.kidney.WGCNA.module.eigens.csv"),header = T, row.names = 1)
name <- str_split(names(MEs0),"_")
samples <-c()
for(i in 1:length(name)){
samples[[i]] <- name[[i]][2]
}  
name <- str_split(samples,"\"")
name <- unlist(name)
colnames(MEs0) <-name
MEs <- orderMEs(MEs0)
moduleTraitCor <- cor(MEs, phenotype, use = "p");
moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nSamples)

Relationship between Modules and Traits

#sizeGrWindow(10,6)
# Will display correlations and their p-values
textMatrix = paste(signif(moduleTraitCor, 2), "\n(",
                        signif(moduleTraitPvalue, 1), ")", sep = "");
dim(textMatrix) = dim(moduleTraitCor)

# Display the correlation values within a heatmap plot
heat <- pheatmap(moduleTraitCor, main = paste("Module-trait relationships"), display_numbers = textMatrix,color=colorRampPalette(brewer.pal(n = 11, name = "Paired"))(10), cluster_rows = F, cluster_cols = F, fontsize_number = 4, angle_col = 45, number_color = "black", border_color = "white")
heat

Top 10 Hub genes within each significant module

Treatment

The top 10 genes that were significant for treatment within the lightyellow module and had the highest correlation with lightyellow were:

Grin3b
ENSMUSG00000081674
ENSMUSG00000094749
ENSMUSG00000084896
Gm15797
Wfikkn2
Iscu
ENSMUSG00000075589
C1qtfn6
Spink4

When assessing the overall sample contribution through their summary eigengenes it appears that for lightyellow all mice had a negative effect with the exception of one 4 wk control and one 96 hr control. All samples with a negative effect had mostly under-expressed genes while the two positive samples had mostly over-expressed genes.

The top 10 genes that were significant for treatment within the purple module and had the highest correlation with purple were:

Fam57a
ENSMUSG00000021944
ENSMUSG00000095547
ENSMUSG00000084132
Rfc4
Pcdhb3
Scnm1
Grik4
ENSMUSG00000074650
Nrn1l

When assessing the overall sample contribution through their summary eigengenes it appears that for purple all mice had a negative effect with the exception of two 4 wk 2DG and two 96 hr 2DG. All samples with a negative effect had mostly under-expressed genes while the 4 positive samples had mostly over-expressed genes.

The top 10 genes that were significant for treatment within the lightcyan module and had the highest correlation with lightcyan were:

Wnt6
ENSMUSG00000089667
Rps29
Mrpl11
Ndufa1
Rft1
Bccip
Atox1
Gm10241
Gm5834

When assessing the overall sample contribution through their summary eigengenes it appears that for lightcyan controls mostly had a negative effect while 2DG had a positive effect. Controls had mostly under-expressed genes while 2DG had mostly over-expressed genes.

Time

The top 10 genes that were significant for time within the blue module and had the highest correlation with blue were:

Tnfrsf21
Rec8
Iws1
Ing1
Syt15
Slc25a35
Zfp652
Eya2
Abcc10
Pcgf6

When assessing the overall sample contribution through their summary eigengenes it appears that for blue controls mostly had a negative effect while 2DG had a half positive and half negative effect. Controls had mostly under-expressed genes while 2DG had both over and under-expressed genes.

The top 10 genes that were significant for time within the pink module and had the highest correlation with pink were:

ENSMUSG00000091393
Tmc8
Mmd
Rab27a
Adamts2
Gm26590
ENSMUSG00000059864
ENSMUSG00000070426
Hmox2
ENSMUSG00000086944

When assessing the overall sample contribution through their summary eigengenes it appears that for pink controls and 2DG mostly had a negative effect. 96 hr 2DG had mostly a positive effect. Controls and 2DG had mostly under-expressed genes while 96 hr 2DG samples had mostly over-expressed genes.

The top 10 genes that were significant for time within the lightcyan module and had the highest correlation with lightcyan were:

Chka
Mrpl30
ENSMUSG00000074354
Klf5
Ddx17
ENSMUSG00000092459
9830147E19Rik
Car5b
Atox1
ENSMUSG00000054988

When assessing the overall sample contribution through their summary eigengenes it appears that for lightcyan controls mostly had a negative effect while 2DG had a positive effect, somewhat independent of time. Controls had mostly under-expressed genes while 2DG had mostly over-expressed genes.

The top 10 genes that were significant for time within the midnightblue module and had the highest correlation with midnightblue were:

Noc3l
Gm26588
Rtel1
Zfp189
Ppp1cb
Noxo1
Cd37
Zfp191
Anp32e
Ttc30b

When assessing the overall sample contribution through their summary eigengenes it appears that for midnightblue controls mostly had a negative effect while 4 wk 2DG had a negative effect and 96 hr 2DG had a positive effect. Controls had mostly under-expressed genes while 4wk 2DG had mostly under-expressed genes and 96 hr 2DG had mostly over-expressed genes.

Treatment by time

The top 10 genes that were significant for treatment by time within the lightyellow module and had the highest correlation with lightyellow were:

Grin3b
ENSMUSG00000081674
ENSMUSG00000084896
Gm15797
Wfikkn2
ENSMUSG00000086918
Iscu
Pbk
ENSMUSG00000027077
C1qtnf6

The top 10 genes that were significant for treatment by time within the purple module and had the highest correlation with purple were:

Fam57a
Trnp1
AV099323
4930447C04Rik
ENSMUSG00000021944
ENSMUSG00000095547
ENSMUSG00000084132
Lcat
Mup10
Abca1

The top 10 genes that were significant for treatment by time within the lightcyan module and had the highest correlation with lightcyan were:

Gm561
Wnt6
ENSMUSG00000089667
Rps29
Mrpl11
Ndufa1
Zfc3h1
Rft1
Atox1
Fam136a