Spatial_Metabolism
Shane Denecke
4/20/2021
R Markdown
This analysis accompanies the publication Luong et. al (in prep). It focuses on the use of the GAL4-UAS system to consider which tissues are the most important in pesticide metabolism.
shhh <- suppressPackageStartupMessages
shhh(library(tidyverse))
shhh(library(data.table))
shhh(library(gridExtra))
shhh(library(ggsignif))
shhh(library(ggsci))
dir.create('plots',showWarnings = F)\[\\[1in]\]
Luciferase Assays to Measure GAL4 Strength
raw.luc=fread('./raw_data/GAL4_Luciferase_Table.csv') %>%
filter(Repeat==1)
raw.luc[['Normalized million RLU']]=raw.luc[['Normalized RLU']]/1000000
raw.luc$Set=factor(raw.luc$Set,levels=c('Midgut','Tubules','Fat Body'))
gp=ggplot(raw.luc ,aes(x=Cyp6g1,y=`Normalized million RLU`,fill=Cyp6g1))
gp=gp+facet_grid(rows=vars(Set),scales = 'free_y')
gp=gp+lims(y=c(0,25))
gp=gp+stat_summary(fun=mean,position=position_dodge(width=0.95),geom="bar")
gp=gp+geom_dotplot(binaxis = "y",stackdir = "center", colour = "black",fill='black',position = position_dodge(width = 0.5), dotsize = 1.5,binwidth = 1)
gp=gp+geom_signif(comparisons=list(c('Hikone-R','Tissue Specific')),test='t.test',y_position=20,tip_length=.2)
gp=gp+labs(x='Genotype',y='Millions RLUs / microgram Protein')
gp=gp+stat_summary(geom="errorbar",colour='black',fun='mean_se',width=.4,size=1)
gp=gp+scale_fill_manual(values=c('grey50','red'))
gp=gp+theme_bw()
gp=gp+theme(legend.title=element_text(size=24,face='bold'),
legend.text=element_text(size=20,face='bold'),
axis.text.y=element_text(size=20),
axis.text.x=element_text(size=20,hjust=1,angle=30),
axis.title=element_text(size=24),
panel.grid = element_blank(),
legend.position='none',
strip.text = element_text(size=24,hjust = 0.5),
plot.title = element_text(size=36,hjust = 0.5))
ggsave(plot=gp,device='pdf',filename='./plots/Luciferase_GAL4_plot.pdf',width=5,height=10)
print(gp)
\[\\[1in]\]
Feeding Assays to measure toxicity
raw.rr=fread('./raw_data/Lc50_RR_table.csv')
raw.rr$Genotype=factor(raw.rr$Genotype,levels=c('Midgut','Tubules','Fat Body','All Tissues'))
raw.rr=arrange(raw.rr,Genotype) %>% mutate(lab=c(rep('A',3),rep('B',3),rep('C',3),rep('D',3)))
gp=ggplot(raw.rr,aes(x=Pesticide,y=ratio,fill=Pesticide))
gp=gp+facet_grid(cols=vars(Genotype))
gp=gp+geom_bar(stat='identity')
gp=gp+geom_errorbar(aes(ymin=lower,ymax=upper))
gp=gp+scale_fill_aaas()
gp=gp+lims(y=c(0,3.5))
gp=gp+labs(y='Resistance Ratio\n',x='\nPesticide')
gp=gp+geom_hline(yintercept = 1,linetype=2)
gp=gp+geom_text(x=.7,y=3.4,size=12,aes(label=lab))
gp=gp+theme_bw()
gp=gp+theme(legend.title=element_text(size=24,face='bold'),
legend.text=element_text(size=20,face='bold'),
axis.text.y=element_text(size=20),
axis.text.x=element_text(size=14,hjust=1,angle=30),
axis.title=element_text(size=24),
#panel.grid = element_blank(),
legend.position='none',
strip.text = element_text(size=24,hjust = 0.5,face='bold'),
plot.title = element_text(size=36,hjust = 0.5))
ggsave(plot=gp,filename='./plots/Lc50_RR.pdf',device='pdf',width=12,height=6)
print(gp)
\[\\[1in]\]
Real-time toxicology assays with the “Wiggle Index”
################### Raw combined #########################
raw.wiggle=fread('./raw_data/Combined_Wiggle_Data.csv')
times=c('0min','5min','10min','15min','30min','45min','60min','90min','120min')
raw.wiggle$Time=factor(raw.wiggle$Time,levels=times)
raw.wiggle$Set=gsub('(^.+) >.+$','\\1',raw.wiggle$Genotype)
raw.wiggle$Expression=gsub('^.+> (.+$)','\\1',raw.wiggle$Genotype)
raw.wiggle$individual=paste0(raw.wiggle$Rep,raw.wiggle$Location)
flat=spread(raw.wiggle,key=Time,value=Wiggle_Index)
for(t in times){
flat[[paste0(t,'__RMR')]]=flat[[t]]/flat[['0min']]
}
rmr.wiggle=select(flat,Date:individual,contains('RMR'))
rmr.melt=melt(rmr.wiggle,
id.vars=c('Date','Genotype','Pesticide','Dose','Rep','Location','Set','individual','Expression'),
variable.name = 'Time',value.name = 'RMR') %>%
mutate(Time=gsub('__RMR','',Time)) %>%
mutate(Time=factor(Time,levels=times))
################## Outliers
ind=select(rmr.melt,Genotype,Pesticide,Dose,Time) %>% unique.data.frame
out.list=list()
for(i in 1:nrow(ind)){
sub=rmr.melt %>% filter(Genotype==ind[i]$Genotype) %>% filter(Pesticide==ind[i]$Pesticide) %>% filter(Dose==ind[i]$Dose) %>% filter(Time==ind[i]$Time)
outs=boxplot.stats(sub$RMR)$out
if(length(outs)>0){
out.list[[paste(ind[i]$Genotype,ind[i]$Pesticide,ind[i]$Dose,ind[i]$Time)]]=sub %>% filter(RMR==outs)
}
}
out.table=rbindlist(out.list) %>% group_by(Date,Genotype,Pesticide,Dose,individual) %>% summarize(coun=n()) %>%
arrange(desc(coun)) %>% filter(coun>=3) %>% select(-coun)
outliers.final=merge(out.table,rmr.melt,by=colnames(out.table),all.x=T)
rmr.clean=anti_join(rmr.melt,outliers.final)
se=function(x) sd(x)/sqrt(length(x))
rmr.sum=rmr.clean %>%
group_by(Date,Genotype,Pesticide,Dose,Time,Expression,Set) %>%
summarize(RMR_mean=mean(RMR),RMR_stderr=se(RMR)) %>% data.table()
plot.rmr.sub=rmr.sum %>% filter(
(Set=='TB' & Dose=='40ppm' & Pesticide=='Bendiocarb') |
(Set=='TB' & Pesticide=='Imidacloprid' & Date=='06Mar21') |
(Set=='MG' & Dose=='40ppm' & Pesticide=='Bendiocarb') |
(Set=='MG' & Dose=='40ppm' & Pesticide=='Imidacloprid') |
(Set=='FB' & Dose=='40ppm' & Pesticide=='Bendiocarb') |
(Set=='FB' & Dose=='40ppm' & Pesticide=='Imidacloprid') |
(Set=='HR' & Pesticide!='Malathion')
)
plot.rmr.sub$Set=gsub('TB','Tubules',plot.rmr.sub$Set)
plot.rmr.sub$Set=gsub('HR','All Tissues',plot.rmr.sub$Set)
plot.rmr.sub$Set=gsub('MG','Midgut',plot.rmr.sub$Set)
plot.rmr.sub$Set=gsub('FB','Fat Body',plot.rmr.sub$Set)
plot.rmr.sub$Set=factor(plot.rmr.sub$Set,levels=c('Midgut','Tubules','Fat Body','All Tissues'))
plot.rmr.sub$Expression=factor(plot.rmr.sub$Expression,levels=c('NULL','Cyp6g1'))
plot.rmr.sub$Time=as.numeric(gsub('min','',plot.rmr.sub$Time))
v=c()
for(i in 1:nrow(plot.rmr.sub)){
sub=plot.rmr.sub[i]
if(sub$Pesticide=="Bendiocarb" & sub$Set=="Midgut"){v[i]="A"}
if(sub$Pesticide=="Bendiocarb" & sub$Set=="Tubules"){v[i]="B"}
if(sub$Pesticide=="Bendiocarb" & sub$Set=="Fat Body"){v[i]="C"}
if(sub$Pesticide=="Bendiocarb" & sub$Set=="All Tissues"){v[i]="D"}
if(sub$Pesticide=="Imidacloprid" & sub$Set=="Midgut"){v[i]="E"}
if(sub$Pesticide=="Imidacloprid" & sub$Set=="Tubules"){v[i]="F"}
if(sub$Pesticide=="Imidacloprid" & sub$Set=="Fat Body"){v[i]="G"}
if(sub$Pesticide=="Imidacloprid" & sub$Set=="All Tissues"){v[i]="H"}
}
plot.rmr.sub$lab=v
gp=ggplot(data=plot.rmr.sub,aes(x=Time,y=RMR_mean,color=Expression))
gp=gp+facet_grid(rows=vars(Pesticide),cols=vars(Set))
gp=gp+scale_y_continuous(breaks=c(0,.25,.5,.75,1,1.25))
gp=gp+scale_color_manual(values=c('black','red'))
gp=gp+labs(x='\nTime (Minutes)',y='Relative Movement Ratio\n')
gp=gp+geom_line(size=1.2)
gp=gp+geom_point(size=4)
gp=gp+geom_errorbar(aes(ymin=RMR_mean-RMR_stderr,ymax=RMR_mean+RMR_stderr))
gp=gp+geom_text(x=1,y=1.5,size=12,aes(label=lab))
gp=gp+theme_bw()
gp=gp+theme(strip.text = element_text(size=24,face='bold'),legend.title = element_text(size=24,face='bold'),
legend.text = element_text(size=18),
axis.title = element_text(size=24,face='bold'),
axis.text=element_text(size=14))
ggsave(filename='./plots/Wiggle_Graph.pdf',plot=gp,device='pdf',width=15,height=10)
print(gp)
\[\\[1in]\]
Figure S2 Showing the lack of effect of Cyp6g1 on Malathion movement
mal.sum=rmr.sum %>% filter(Pesticide=='Malathion' & Set=='HR')
mal.sum$Set=gsub('TB','Tubules',mal.sum$Set)
mal.sum$Set=gsub('HR','All Tissues',mal.sum$Set)
mal.sum$Set=gsub('MG','Midgut',mal.sum$Set)
mal.sum$Set=gsub('FB','Fat Body',mal.sum$Set)
mal.sum$Set=factor(mal.sum$Set,levels=c('Midgut','Tubules','Fat Body','All Tissues'))
mal.sum$Expression=factor(mal.sum$Expression,levels=c('NULL','Cyp6g1'))
mal.sum$Time=as.numeric(gsub('min','',mal.sum$Time))
gp=ggplot(data=mal.sum,aes(x=Time,y=RMR_mean,color=Expression))
gp=gp+facet_grid(rows=vars(Pesticide),cols=vars(Set))
gp=gp+scale_y_continuous(breaks=c(0,.25,.5,.75,1,1.25))
gp=gp+scale_color_manual(values=c('black','red'))
gp=gp+labs(x='Time',y='Relative Movement Ratio')
gp=gp+geom_line(size=1.2)
gp=gp+geom_point(size=4)
gp=gp+geom_errorbar(aes(ymin=RMR_mean-RMR_stderr,ymax=RMR_mean+RMR_stderr))
gp=gp+theme_bw()
gp=gp+theme(strip.text = element_text(size=24,face='bold'),legend.title = element_text(size=24,face='bold'),
legend.text = element_text(size=18),
axis.title = element_text(size=18,face='bold'),
axis.text=element_text(size=14))
ggsave(filename='./plots/Wiggle_Malathion.pdf',plot=gp,device='pdf',width=8,height=5)
print(gp)