Introduction and Data files

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.

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

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

tdata.FPKM.sample.info <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_phenotypes.RData"))
tdata.FPKM.sample.info.pheno <- tdata.FPKM.sample.info[,c(27238:27240)] %>% rownames_to_column()

small.intestine.gsva <- readRDS(here("Data","Small Intestine","GSVA.modules.small.intestine.RData")) 
small.intestine.pathways <- readRDS(here("Data","Small Intestine","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_small_intestine_WGCNA.RData"))
term.names <- small.intestine.pathways[[18]]$term_name
small.intestine.green4 <- small.intestine.gsva$log.tdata.FPKM.sample.info.subset.small.intestine.WGCNA_green4[,match(term.names, colnames(small.intestine.gsva$log.tdata.FPKM.sample.info.subset.small.intestine.WGCNA_green4))]
SI.gsva <- as.data.frame(small.intestine.green4)[,-27] %>% rownames_to_column()
SI.gsva.phenotypes <- left_join(SI.gsva, tdata.FPKM.sample.info.pheno, by = c("rowname" = "rowname"))
SI.gsva.phenotypes <- SI.gsva.phenotypes %>% mutate("Module" = rep("Green4",16)) 

heart.gsva <- readRDS(here("Data","Heart","GSVA.modules.heart.RData")) 
heart.pathways <- readRDS(here("Data","Heart","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_heart_WGCNA.RData"))
term.names <- heart.pathways[[4]]$term_name
heart.darkgreen <- heart.gsva$log.tdata.FPKM.sample.info.subset.heart.WGCNA_darkgreen[,match(term.names,colnames(heart.gsva$log.tdata.FPKM.sample.info.subset.heart.WGCNA_darkgreen))]
heart.gsva <- as.data.frame(heart.darkgreen) %>% rownames_to_column()
heart.gsva.phenotypes <- left_join(heart.gsva, tdata.FPKM.sample.info.pheno, by = c("rowname" = "rowname"))
heart.gsva.phenotypes <- heart.gsva.phenotypes %>% mutate("Module" = rep("darkgreen",16))

SI.heart.gsva.phenotypes <- full_join(heart.gsva.phenotypes, SI.gsva.phenotypes, by = c("rowname")) %>% column_to_rownames()

#write.csv(SI.heart.gsva.phenotypes,here("Data","SI.heart.gsva.for.mediation.analysis.csv"), row.names = T)

prefrontal.cortex.gsva <- readRDS(here("Data","Prefrontal Cortex","GSVA.modules.prefrontal.cortex.RData")) 
prefrontal.cortex.pathways <- readRDS(here("Data","Prefrontal Cortex","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_prefrontal_cortex_WGCNA.RData"))
term.names <- prefrontal.cortex.pathways[[3]]$term_name
prefrontal.cortex.bisque2 <- prefrontal.cortex.gsva$log.tdata.FPKM.sample.info.subset.prefrontal.cortex.WGCNA_bisque2[,match(term.names, colnames(prefrontal.cortex.gsva$log.tdata.FPKM.sample.info.subset.prefrontal.cortex.WGCNA_bisque2))][,-1]
PC.gsva <- as.data.frame(prefrontal.cortex.bisque2) %>% rownames_to_column()
PC.gsva.phenotypes <- left_join(PC.gsva, tdata.FPKM.sample.info.pheno, by = c("rowname" = "rowname"))
PC.gsva.phenotypes <- PC.gsva.phenotypes %>% mutate("Module" = rep("Bisque2",16))

hypothalamus.gsva <- readRDS(here("Data","Hypothalamus","GSVA.modules.hypothalamus.RData")) 
hypothalamus.pathways <- readRDS(here("Data","Hypothalamus","Chang_B6_96hr_4wk_gprofiler_pathway_annotation_list_hypothalamus_WGCNA.RData"))
term.names <- hypothalamus.pathways[[3]]$term_name
hypothalamus.bisque4 <- hypothalamus.gsva$log.tdata.FPKM.sample.info.subset.hypothalamus.WGCNA_bisque4[,match(term.names,colnames(hypothalamus.gsva$log.tdata.FPKM.sample.info.subset.hypothalamus.WGCNA_bisque4))]
hypothalamus.gsva <- as.data.frame(hypothalamus.bisque4) %>% rownames_to_column()
hypothalamus.gsva.phenotypes <- left_join(hypothalamus.gsva, tdata.FPKM.sample.info.pheno, by = c("rowname" = "rowname")) %>% column_to_rownames()

hypothalamus.gsva.phenotypes <- hypothalamus.gsva.phenotypes %>% mutate("Module" = rep("Bisque4",16))

PC.hyp.gsva.phenotypes <- full_join(hypothalamus.gsva.phenotypes, PC.gsva.phenotypes, by = c("Treatment")) %>% column_to_rownames()

#write.csv(PC.hyp.gsva.phenotypes,here("Data","PC.hyp.gsva.for.mediation.analysis.csv"), row.names = T)

SI.heart.gsva.phenotypes <- read.csv(here("Data","SI.heart.gsva.for.mediation.analysis.csv"), row.names = 1)
SI.heart.gsva.phenotypes$Treatment <- replace(SI.heart.gsva.phenotypes$Treatment, SI.heart.gsva.phenotypes$Treatment == "None", "Control")
SI.heart.gsva.phenotypes$Treatment <- as.factor(SI.heart.gsva.phenotypes$Treatment)
SI.heart.gsva.phenotypes$Time <- as.factor(SI.heart.gsva.phenotypes$Time)

Mediation analysis

To determine if any of the correlations between heart and small intestine could be explained more definitively causal mediation analysis was performed. The total effect is the summation of the causal mediated effect and the directed effect. The causal mediated effect is the indirect effect of the independent variable on the dependent variable that goes through the mediator. The directed effect is the direct effect of the independent variable on the dependent variable.

Heart and Small Intestine

We assessed whether BCAA degradation in the heart mediated the pathways identified in the small intestine.

for(i in 1: 78){
    cat("\n####",colnames(SI.heart.gsva.phenotypes)[i],"\n")
med.fit <- lm(data = SI.heart.gsva.phenotypes, Valine..leucine.and.isoleucine.degradation ~ Treatment + Time + Treatment:Time)
out.fit <- glm(data = SI.heart.gsva.phenotypes, SI.heart.gsva.phenotypes[,i] ~ Valine..leucine.and.isoleucine.degradation + Treatment + Time + Treatment:Time + Valine..leucine.and.isoleucine.degradation*Treatment)

med.out <- mediate(med.fit, out.fit, treat = "Treatment", mediator = "Valine..leucine.and.isoleucine.degradation", sims = 1000)
model <- summary(med.out)
#panderOptions('knitr.auto.asis', FALSE)
#      print(pander(model))
bt_effect <- c("Indirect Effect", "Direct Effect", "Total Effect", 
               "Percent Direct Effect")
bt_est <- c(med.out$d1, med.out$z1, med.out$tau.coef, med.out$n1)
#bt_p <- format.pval(c(med.out$d1.p, med.out$z1.p, med.out$tau.p, med.out$n1.p))
bt_p <- c(med.out$d1.p, med.out$z1.p, med.out$tau.p, med.out$n1.p)
bt_stars <- c(stars.pval(med.out$d1.p), stars.pval(med.out$z1.p),
              stars.pval(med.out$tau.p), stars.pval(med.out$n1.p))
bt_DF <- data.frame(row.names = bt_effect, format(bt_est, digits = 2), 
                    format(bt_p, nsmall = 3), bt_stars)
colnames(bt_DF) <- c("Coefficients", "p-value", "")

print(knitr::kable(bt_DF, booktabs = T, align = "c",
      caption = "Bootstrapping Analysis for Mediation") %>%
      footnote(general = c("Simulations: 1000", "Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001"),
               general_title = "Legend:") %>% kable_styling())

cat("\n \n")
}

Propanoate.metabolism

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.059	0.524
Direct Effect	-0.097	0.290
Total Effect	0.371	0.000	***
Percent Direct Effect	0.148	0.524
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Valine..leucine.and.isoleucine.degradation

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-4.0e-01	0.000	***
Direct Effect	1.3e-16	0.166
Total Effect	-4.0e-01	0.000	***
Percent Direct Effect	1.0e+00	0.000	***
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Regulation.of.pyruvate.dehydrogenase..PDH..complex

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.243	0.050
Direct Effect	-0.023	0.738
Total Effect	0.370	0.000	***
Percent Direct Effect	0.660	0.050
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Mitochondrial.translation

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.089	0.692
Direct Effect	-0.183	0.144
Total Effect	0.308	0.004	**
Percent Direct Effect	0.299	0.692
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Mitochondrial.translation.elongation

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.09	0.686
Direct Effect	-0.20	0.122
Total Effect	0.30	0.002	**
Percent Direct Effect	0.31	0.688
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Mitochondrial.translation.termination

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.079	0.760
Direct Effect	-0.191	0.128
Total Effect	0.300	0.022
Percent Direct Effect	0.283	0.762
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Pyruvate.metabolism.and.Citric.Acid..TCA..cycle

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.143	0.264
Direct Effect	-0.076	0.340
Total Effect	0.368	0.000	***
Percent Direct Effect	0.396	0.264
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Pyruvate.metabolism

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.16	0.376
Direct Effect	-0.17	0.096	.
Total Effect	0.32	0.006	**
Percent Direct Effect	0.49	0.382
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

The.citric.acid..TCA..cycle.and.respiratory.electron.transport

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.114	0.340
Direct Effect	-0.054	0.490
Total Effect	0.376	0.000	***
Percent Direct Effect	0.300	0.340
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Metabolism

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.292	0.010	**
Direct Effect	0.078	0.140
Total Effect	0.415	0.000	***
Percent Direct Effect	0.705	0.010	**
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Antigen.processing.and.presentation

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.31	0.328
Direct Effect	0.49	0.010	**
Total Effect	0.38	0.006	**
Percent Direct Effect	-0.75	0.334
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Th17.cell.differentiation

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.094	0.794
Direct Effect	0.441	0.020
Total Effect	0.333	0.010	**
Percent Direct Effect	-0.251	0.792
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Graft.versus.host.disease

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.31	0.366
Direct Effect	-0.49	0.002	**
Total Effect	-0.37	0.006	**
Percent Direct Effect	-0.75	0.372
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Th1.and.Th2.cell.differentiation

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.13	0.720
Direct Effect	0.48	0.010	**
Total Effect	0.36	0.006	**
Percent Direct Effect	-0.33	0.722
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Hematopoietic.cell.lineage

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.057	0.858
Direct Effect	0.439	0.036
Total Effect	0.356	0.008	**
Percent Direct Effect	-0.174	0.862
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Type.I.diabetes.mellitus

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.25	0.494
Direct Effect	-0.48	0.002	**
Total Effect	-0.36	0.004	**
Percent Direct Effect	-0.64	0.498
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Cytokine.cytokine.receptor.interaction

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.027	0.962
Direct Effect	0.420	0.028
Total Effect	0.328	0.018
Percent Direct Effect	-0.075	0.956
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Phagosome

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.014	0.960
Direct Effect	0.535	0.000	***
Total Effect	0.371	0.004	**
Percent Direct Effect	-0.047	0.956
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Cell.adhesion.molecules

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.26	0.444
Direct Effect	0.46	0.022
Total Effect	0.35	0.008	**
Percent Direct Effect	-0.68	0.448
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Allograft.rejection

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.30	0.402
Direct Effect	-0.50	0.006	**
Total Effect	-0.36	0.008	**
Percent Direct Effect	-0.73	0.406
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Viral.myocarditis

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.30	0.414
Direct Effect	-0.46	0.022
Total Effect	-0.35	0.006	**
Percent Direct Effect	-0.73	0.420
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Leishmaniasis

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.0088	0.984
Direct Effect	-0.6032	0.000	***
Total Effect	-0.4028	0.002	**
Percent Direct Effect	-0.0097	0.986
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Rheumatoid.arthritis

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.034	0.928
Direct Effect	-0.507	0.002	**
Total Effect	-0.379	0.002	**
Percent Direct Effect	-0.068	0.926
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Viral.protein.interaction.with.cytokine.and.cytokine.receptor

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.016	0.944
Direct Effect	-0.500	0.004	**
Total Effect	-0.359	0.002	**
Percent Direct Effect	-0.055	0.946
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Autoimmune.thyroid.disease

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.31	0.346
Direct Effect	-0.51	0.010	**
Total Effect	-0.37	0.010	**
Percent Direct Effect	-0.74	0.356
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Inflammatory.bowel.disease

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.11	0.660
Direct Effect	-0.56	0.002	**
Total Effect	-0.39	0.002	**
Percent Direct Effect	0.27	0.662
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Tuberculosis

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.071	0.870
Direct Effect	0.556	0.002	**
Total Effect	0.383	0.002	**
Percent Direct Effect	-0.155	0.872
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Toxoplasmosis

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.077	0.798
Direct Effect	-0.571	0.002	**
Total Effect	-0.387	0.006	**
Percent Direct Effect	0.179	0.804
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Human.T.cell.leukemia.virus.1.infection

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.30	0.432
Direct Effect	0.43	0.032
Total Effect	0.33	0.018
Percent Direct Effect	-0.84	0.450
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Epstein.Barr.virus.infection

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.14	0.680
Direct Effect	0.50	0.010	**
Total Effect	0.37	0.008	**
Percent Direct Effect	-0.33	0.688
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Staphylococcus.aureus.infection

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.045	0.884
Direct Effect	-0.522	0.002	**
Total Effect	-0.388	0.002	**
Percent Direct Effect	-0.107	0.886
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Asthma

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.27	0.312
Direct Effect	-0.58	0.000	***
Total Effect	-0.41	0.000	***
Percent Direct Effect	-0.61	0.312
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Chemokine.signaling.pathway

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.18	0.646
Direct Effect	-0.47	0.006	**
Total Effect	-0.33	0.014
Percent Direct Effect	-0.47	0.644
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Primary.immunodeficiency

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.22	0.570
Direct Effect	-0.40	0.048
Total Effect	-0.31	0.028
Percent Direct Effect	-0.62	0.582
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Natural.killer.cell.mediated.cytotoxicity

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.36	0.344
Direct Effect	0.44	0.020
Total Effect	0.35	0.020
Percent Direct Effect	-0.89	0.360
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Herpes.simplex.virus.1.infection

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.069	0.836
Direct Effect	0.506	0.002	**
Total Effect	0.384	0.002	**
Percent Direct Effect	-0.183	0.838
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

TNF.signaling.pathway

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.14	0.668
Direct Effect	-0.50	0.002	**
Total Effect	-0.38	0.002	**
Percent Direct Effect	0.38	0.670
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Pertussis

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.14	0.672
Direct Effect	-0.48	0.002	**
Total Effect	-0.39	0.004	**
Percent Direct Effect	0.33	0.676
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Chagas.disease

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.14	0.634
Direct Effect	0.49	0.010	**
Total Effect	0.37	0.002	**
Percent Direct Effect	0.37	0.636
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Human.immunodeficiency.virus.1.infection

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.12	0.764
Direct Effect	-0.49	0.010	**
Total Effect	-0.34	0.014
Percent Direct Effect	-0.32	0.774
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Osteoclast.differentiation

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.0019	0.986
Direct Effect	-0.4619	0.016
Total Effect	-0.3106	0.016
Percent Direct Effect	0.0183	0.990
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Influenza.A

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.11	0.754
Direct Effect	-0.52	0.004	**
Total Effect	-0.39	0.004	**
Percent Direct Effect	-0.27	0.758
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Human.cytomegalovirus.infection

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.21	0.610
Direct Effect	-0.43	0.026
Total Effect	-0.32	0.024
Percent Direct Effect	-0.59	0.626
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Toll.like.receptor.signaling.pathway

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.16	0.630
Direct Effect	-0.52	0.000	***
Total Effect	-0.37	0.002	**
Percent Direct Effect	0.39	0.632
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Systemic.lupus.erythematosus

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.016	0.982
Direct Effect	-0.574	0.000	***
Total Effect	-0.419	0.000	***
Percent Direct Effect	-0.021	0.982
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

JAK.STAT.signaling.pathway

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.027	0.942
Direct Effect	0.334	0.108
Total Effect	0.275	0.054	.
Percent Direct Effect	-0.115	0.944
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

PD.L1.expression.and.PD.1.checkpoint.pathway.in.cancer

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.0044	0.992
Direct Effect	-0.5050	0.004	**
Total Effect	-0.3416	0.002	**
Percent Direct Effect	0.0189	0.994
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Complement.and.coagulation.cascades

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.21	0.518
Direct Effect	-0.47	0.004	**
Total Effect	-0.39	0.002	**
Percent Direct Effect	0.52	0.520
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Measles

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.031	0.952
Direct Effect	0.443	0.030
Total Effect	0.313	0.028
Percent Direct Effect	-0.054	0.948
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Cytosolic.DNA.sensing.pathway

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.066	0.846
Direct Effect	-0.490	0.006	**
Total Effect	-0.355	0.006	**
Percent Direct Effect	-0.186	0.844
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

NF.kappa.B.signaling.pathway

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.18	0.668
Direct Effect	-0.32	0.128
Total Effect	-0.28	0.050
Percent Direct Effect	-0.48	0.702
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

NOD.like.receptor.signaling.pathway

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.068	0.844
Direct Effect	0.449	0.026
Total Effect	0.303	0.032
Percent Direct Effect	0.252	0.848
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Ether.lipid.metabolism

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.24	0.548
Direct Effect	-0.42	0.046
Total Effect	-0.30	0.026
Percent Direct Effect	-0.66	0.550
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

C.type.lectin.receptor.signaling.pathway

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.019	0.958
Direct Effect	-0.514	0.004	**
Total Effect	-0.346	0.008	**
Percent Direct Effect	-0.044	0.954
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Immune.System

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.11	0.778
Direct Effect	-0.40	0.044
Total Effect	-0.32	0.024
Percent Direct Effect	-0.30	0.786
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Chemokine.receptors.bind.chemokines

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.21	0.512
Direct Effect	-0.52	0.006	**
Total Effect	-0.35	0.004	**
Percent Direct Effect	-0.54	0.516
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Dectin.2.family

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.19	0.598
Direct Effect	-0.39	0.058	.
Total Effect	-0.33	0.026
Percent Direct Effect	-0.52	0.612
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Cytokine.Signaling.in.Immune.system

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.065	0.864
Direct Effect	-0.356	0.098	.
Total Effect	-0.318	0.020
Percent Direct Effect	-0.214	0.864
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

ER.Phagosome.pathway

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.27	0.484
Direct Effect	-0.40	0.062	.
Total Effect	-0.32	0.032
Percent Direct Effect	-0.65	0.508
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Signaling.by.Interleukins

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.084	0.852
Direct Effect	-0.355	0.070	.
Total Effect	-0.326	0.024
Percent Direct Effect	-0.239	0.852
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Interleukin.3..Interleukin.5.and.GM.CSF.signaling

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.23	0.608
Direct Effect	-0.26	0.222
Total Effect	-0.26	0.074	.
Percent Direct Effect	-0.63	0.666
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

DAP12.signaling

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.69	0.046
Direct Effect	-0.40	0.060	.
Total Effect	-0.31	0.056	.
Percent Direct Effect	-1.90	0.102
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Immunoregulatory.interactions.between.a.Lymphoid.and.a.non.Lymphoid.cell

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.20	0.620
Direct Effect	0.40	0.074	.
Total Effect	0.29	0.040
Percent Direct Effect	-0.59	0.648
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Antigen.Presentation..Folding..assembly.and.peptide.loading.of.class.I.MHC

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.25	0.542
Direct Effect	-0.43	0.052	.
Total Effect	-0.33	0.028
Percent Direct Effect	-0.63	0.566
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

DAP12.interactions

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.70	0.050
Direct Effect	-0.39	0.072	.
Total Effect	-0.29	0.094	.
Percent Direct Effect	-1.92	0.144
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Antigen.processing.Cross.presentation

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.25	0.468
Direct Effect	-0.47	0.010	**
Total Effect	-0.37	0.004	**
Percent Direct Effect	-0.61	0.472
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Adaptive.Immune.System

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.15	0.728
Direct Effect	0.40	0.034
Total Effect	0.33	0.012
Percent Direct Effect	-0.43	0.728
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Endosomal.Vacuolar.pathway

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.46	0.302
Direct Effect	-0.36	0.128
Total Effect	-0.26	0.104
Percent Direct Effect	-1.29	0.390
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Innate.Immune.System

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.14	0.740
Direct Effect	-0.43	0.024
Total Effect	-0.32	0.018
Percent Direct Effect	-0.37	0.746
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Interleukin.2.signaling

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.22	0.616
Direct Effect	-0.32	0.180
Total Effect	-0.24	0.130
Percent Direct Effect	-0.67	0.686
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Post.translational.protein.phosphorylation

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.06	0.836
Direct Effect	-0.48	0.016
Total Effect	-0.36	0.002	**
Percent Direct Effect	0.20	0.838
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Platelet.Adhesion.to.exposed.collagen

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.39	0.272
Direct Effect	-0.30	0.150
Total Effect	-0.33	0.026
Percent Direct Effect	-1.01	0.294
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

C.type.lectin.receptors..CLRs.

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.24	0.480
Direct Effect	-0.48	0.002	**
Total Effect	-0.38	0.004	**
Percent Direct Effect	-0.56	0.484
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Regulation.of.Insulin.like.Growth.Factor..IGF..transport.and.uptake.by.Insulin.like.Growth.Factor.Binding.Proteins..IGFBPs.

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.054	0.892
Direct Effect	-0.478	0.014
Total Effect	-0.358	0.012
Percent Direct Effect	0.148	0.896
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Interleukin.15.signaling

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.22	0.608
Direct Effect	-0.30	0.176
Total Effect	-0.24	0.106
Percent Direct Effect	-0.64	0.670
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Signaling.by.SCF.KIT

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.24	0.584
Direct Effect	-0.26	0.260
Total Effect	-0.24	0.104
Percent Direct Effect	-0.72	0.636
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Proton.oligopeptide.cotransporters

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.043	0.950
Direct Effect	-0.357	0.086	.
Total Effect	-0.251	0.074	.
Percent Direct Effect	-0.086	0.960
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Growth.hormone.receptor.signaling

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.27	0.572
Direct Effect	-0.27	0.240
Total Effect	-0.22	0.134
Percent Direct Effect	-0.83	0.654
Legend:
Simulations: 1000
Significance: ∗ p<0.05, ∗∗ p<0.01; ∗∗∗ p<0.001

Prefrontal Cortex and Hypothalamus

We assessed whether G2/M DNA damage checkpoint in the hypothalamus mediated any of the pathways identified in the prefrontal cortex.

PC.hyp <- read.csv(here("Data","PC.hyp.gsva.for.mediation.analysis.csv"), row.names = 1)
PC.hyp$Treatment <- replace(PC.hyp$Treatment, PC.hyp$Treatment == "None", "Control")
PC.hyp$Treatment <- as.factor(PC.hyp$Treatment)
PC.hyp$Time <- as.factor(PC.hyp$Time)

for(i in 1: 23){
    cat("\n####",colnames(PC.hyp)[i],"\n")
med.fit <- lm(data = PC.hyp, G2.M.DNA.damage.checkpoint ~ Treatment + Time + Treatment:Time)
out.fit <- glm(data = PC.hyp, PC.hyp[,i] ~ G2.M.DNA.damage.checkpoint + Treatment + Time + Treatment:Time + G2.M.DNA.damage.checkpoint*Treatment)

med.out <- mediate(med.fit, out.fit, treat = "Treatment", mediator = "G2.M.DNA.damage.checkpoint", sims = 1000)
model <- summary(med.out)
#panderOptions('knitr.auto.asis', FALSE)
#      print(pander(model))
bt_effect <- c("Indirect Effect", "Direct Effect", "Total Effect", 
               "Percent Direct Effect")
bt_est <- c(med.out$d1, med.out$z1, med.out$tau.coef, med.out$n1)
#bt_p <- format.pval(c(med.out$d1.p, med.out$z1.p, med.out$tau.p, med.out$n1.p))
bt_p <- c(med.out$d1.p, med.out$z1.p, med.out$tau.p, med.out$n1.p)
bt_stars <- c(stars.pval(med.out$d1.p), stars.pval(med.out$z1.p),
              stars.pval(med.out$tau.p), stars.pval(med.out$n1.p))
bt_DF <- data.frame(row.names = bt_effect, format(bt_est, digits = 2), 
                    format(bt_p, nsmall = 3), bt_stars)
colnames(bt_DF) <- c("Coefficients", "p-value", "")

print(knitr::kable(bt_DF, booktabs = T, align = "c",
      caption = "Bootstrapping Analysis for Mediation") %>%
      footnote(general = c("Simulations: 1000", "Significance: ∗ p<0.05; 
                            ∗∗ p<0.01; ∗∗∗ p<0.001"),
               general_title = "Legend:") %>% kable_styling())
cat("\n \n")
}

Mitochondrial.Fatty.Acid.Beta.Oxidation

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.268	0.002	**
Direct Effect	-0.031	0.686
Total Effect	-0.323	0.002	**
Percent Direct Effect	0.827	0.004	**
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

mitochondrial.fatty.acid.beta.oxidation.of.saturated.fatty.acids

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.161	0.068	.
Direct Effect	0.029	0.826
Total Effect	-0.293	0.014
Percent Direct Effect	0.541	0.082	.
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

Telomere.Maintenance

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.30	0.002	**
Direct Effect	-0.05	0.184
Total Effect	-0.27	0.002	**
Percent Direct Effect	1.09	0.000	***
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

Base.Excision.Repair

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.298	0.004	**
Direct Effect	0.041	0.372
Total Effect	-0.245	0.014
Percent Direct Effect	1.211	0.014
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

Beta.oxidation.of.palmitoyl.CoA.to.myristoyl.CoA

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.20	0.056	.
Direct Effect	0.15	0.274
Total Effect	-0.20	0.142
Percent Direct Effect	0.93	0.190
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

G2.M.DNA.damage.checkpoint

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	2.6e-01	0.000	***
Direct Effect	2.6e-17	0.876
Total Effect	2.6e-01	0.000	***
Percent Direct Effect	1.0e+00	0.000	***
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

HDR.through.Homologous.Recombination..HRR..or.Single.Strand.Annealing..SSA.

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.2789	0.002	**
Direct Effect	-0.0034	0.932
Total Effect	-0.2662	0.006	**
Percent Direct Effect	1.0531	0.004	**
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

Homology.Directed.Repair

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.2763	0.006	**
Direct Effect	-0.0069	0.862
Total Effect	-0.2646	0.006	**
Percent Direct Effect	1.0427	0.004	**
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

Chromosome.Maintenance

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.296	0.006	**
Direct Effect	-0.049	0.158
Total Effect	-0.268	0.002	**
Percent Direct Effect	1.102	0.008	**
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

Herpes.simplex.virus.1.infection

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.054	0.686
Direct Effect	0.475	0.006	**
Total Effect	0.340	0.006	**
Percent Direct Effect	0.150	0.680
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

Protein.processing.in.endoplasmic.reticulum

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.040	0.776
Direct Effect	0.469	0.002	**
Total Effect	0.357	0.004	**
Percent Direct Effect	0.088	0.780
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

Gene.expression..Transcription.

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.019	0.888
Direct Effect	-0.453	0.008	**
Total Effect	-0.327	0.012
Percent Direct Effect	0.052	0.888
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

RNA.Polymerase.II.Transcription

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.037	0.778
Direct Effect	-0.452	0.012
Total Effect	-0.324	0.014
Percent Direct Effect	0.094	0.788
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

PERK.regulates.gene.expression

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.061	0.624
Direct Effect	0.456	0.008	**
Total Effect	0.349	0.004	**
Percent Direct Effect	0.153	0.628
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

RNA.polymerase.II.transcribes.snRNA.genes

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.052	0.720
Direct Effect	-0.508	0.000	***
Total Effect	-0.290	0.038
Percent Direct Effect	-0.149	0.722
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

mRNA.Capping

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.034	0.822
Direct Effect	0.472	0.010	**
Total Effect	0.328	0.020
Percent Direct Effect	-0.081	0.822
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

Global.Genome.Nucleotide.Excision.Repair..GG.NER.

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.040	0.772
Direct Effect	-0.437	0.018
Total Effect	-0.304	0.024
Percent Direct Effect	0.097	0.788
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

Nucleotide.Excision.Repair

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.046	0.770
Direct Effect	-0.438	0.024
Total Effect	-0.305	0.032
Percent Direct Effect	0.118	0.790
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

Recycling.of.eIF2.GDP

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.11	0.434
Direct Effect	0.48	0.004	**
Total Effect	0.33	0.020
Percent Direct Effect	0.28	0.442
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

Generic.Transcription.Pathway

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.038	0.802
Direct Effect	-0.447	0.014
Total Effect	-0.319	0.032
Percent Direct Effect	0.086	0.822
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

RNA.Pol.II.CTD.phosphorylation.and.interaction.with.CE

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.016	0.926
Direct Effect	0.453	0.006	**
Total Effect	0.320	0.016
Percent Direct Effect	-0.038	0.930
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

Class.I.peroxisomal.membrane.protein.import

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	-0.081	0.618
Direct Effect	-0.377	0.036
Total Effect	-0.288	0.042
Percent Direct Effect	0.224	0.636
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

RNA.Polymerase.II.Pre.transcription.Events

Bootstrapping Analysis for Mediation
	Coefficients	p-value
Indirect Effect	0.033	0.820
Direct Effect	-0.480	0.004	**
Total Effect	-0.298	0.024
Percent Direct Effect	-0.093	0.832
Legend:
Simulations: 1000
Significance: ∗ p<0.05; ∗∗ p<0.01; ∗∗∗ p<0.001

Analysis performed by Ann Wells

The Carter Lab The Jackson Laboratory 2023

ann.wells@jax.org

Mediation Analysis Across Tissues

Ann Wells

April 23, 2023

Introduction and Data files

Mediation analysis

Heart and Small Intestine

Propanoate.metabolism

Valine..leucine.and.isoleucine.degradation

Regulation.of.pyruvate.dehydrogenase..PDH..complex

Mitochondrial.translation

Mitochondrial.translation.elongation

Mitochondrial.translation.termination

Pyruvate.metabolism.and.Citric.Acid..TCA..cycle

Pyruvate.metabolism

The.citric.acid..TCA..cycle.and.respiratory.electron.transport

Metabolism

Antigen.processing.and.presentation

Th17.cell.differentiation

Graft.versus.host.disease

Th1.and.Th2.cell.differentiation

Hematopoietic.cell.lineage

Type.I.diabetes.mellitus

Cytokine.cytokine.receptor.interaction

Phagosome

Cell.adhesion.molecules

Allograft.rejection

Viral.myocarditis

Leishmaniasis

Rheumatoid.arthritis

Viral.protein.interaction.with.cytokine.and.cytokine.receptor

Autoimmune.thyroid.disease

Inflammatory.bowel.disease

Tuberculosis

Toxoplasmosis

Human.T.cell.leukemia.virus.1.infection

Epstein.Barr.virus.infection

Staphylococcus.aureus.infection

Asthma

Chemokine.signaling.pathway

Primary.immunodeficiency

Natural.killer.cell.mediated.cytotoxicity

Herpes.simplex.virus.1.infection

TNF.signaling.pathway

Pertussis

Chagas.disease

Human.immunodeficiency.virus.1.infection

Osteoclast.differentiation

Influenza.A

Human.cytomegalovirus.infection

Toll.like.receptor.signaling.pathway

Systemic.lupus.erythematosus

JAK.STAT.signaling.pathway

PD.L1.expression.and.PD.1.checkpoint.pathway.in.cancer

Complement.and.coagulation.cascades

Measles

Cytosolic.DNA.sensing.pathway

NF.kappa.B.signaling.pathway

NOD.like.receptor.signaling.pathway

Ether.lipid.metabolism

C.type.lectin.receptor.signaling.pathway

Immune.System

Chemokine.receptors.bind.chemokines

Dectin.2.family

Cytokine.Signaling.in.Immune.system

ER.Phagosome.pathway

Signaling.by.Interleukins

Interleukin.3..Interleukin.5.and.GM.CSF.signaling

DAP12.signaling

Immunoregulatory.interactions.between.a.Lymphoid.and.a.non.Lymphoid.cell

Antigen.Presentation..Folding..assembly.and.peptide.loading.of.class.I.MHC

DAP12.interactions

Antigen.processing.Cross.presentation

Adaptive.Immune.System

Endosomal.Vacuolar.pathway

Innate.Immune.System

Interleukin.2.signaling

Post.translational.protein.phosphorylation

Platelet.Adhesion.to.exposed.collagen

C.type.lectin.receptors..CLRs.

Regulation.of.Insulin.like.Growth.Factor..IGF..transport.and.uptake.by.Insulin.like.Growth.Factor.Binding.Proteins..IGFBPs.