Supplemental Lipid Figures

Making usable data for ggplot

POS <- read.csv("Blanchard_Lipid_POS.csv", header=TRUE)
NEG <- read.csv("Blanchard_Lipid_NEG.csv", header=TRUE)
names(POS) <- gsub(x = names(POS), pattern = "_POS", replacement = "")  
names(NEG) <- gsub(x=names(POS), pattern="_NEG", replacement="")
POS$charge <- "POS"
NEG$charge <- "NEG"
data <- rbind(POS, NEG) # lipid data with counts

summary_class <- read_csv("significantlipidswithformula.csv")[-1] # made in comparealigned .Rmd
summary_class <- summary_class %>%
  rename(Main_Class = "Main Class") #getting rid of the space in column
summary_class <- summary_class[order(summary_class$Main_Class, summary_class$Standardized_Difference_Control_minus_Heated),] #ordering the metadata

df <- merge(summary_class, data, by="row.identity", all=FALSE)
# For the dflong below:
# columns 17-43 is counts 
# Blanch_Nat_Lip is redundant
# separate by treatment Control vs Heated
dflong <- df[-c(11:16)] %>% 
  gather(plot, count,Blanch_Nat_Lip_C_12_AB_M_17:Blanch_Nat_Lip_H_4_AB_M_05) %>% 
  mutate(plot = gsub("Blanch_Nat_Lip_", "", plot)) %>% 
  separate(plot, c("treatment","plot"), "_", extra = "merge") %>%
  unite("row.id", c(row.identity, soil_type), remove=FALSE) %>%
  mutate(row.id = gsub("_Mineral", "", row.id)) %>%
  mutate(row.id = gsub("_Organic", "", row.id)) %>%
  arrange(soil_type)
dflongMineral <- dflong[which(dflong$soil_type == "Mineral"),]
dflongOrganic <- dflong[which(dflong$soil_type == "Organic"),]

Overarching Lipid Figure

lipidfigureMineral <- ggplot(data=dflongMineral, aes(x=row.id, y=log(count), group_by(Main_Class, treatment), fill = treatment)) + geom_boxplot(show.legend = FALSE) + coord_flip() + scale_fill_manual(values = c( "#082BEA", "#EA0D08")) + scale_color_manual(values = c("#D4E126", "#12EF3A")) + ggtitle("Significant Lipids Heated v. Control Soil Mineral Plots") + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
lipidfigureOrganic <- ggplot(data=dflongOrganic, aes(x=row.id, y=log(count), group_by(Main_Class, treatment), fill = treatment)) + geom_boxplot() + coord_flip() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + scale_fill_manual(values = c( "#082BEA", "#EA0D08")) + scale_color_manual(values = c("#D4E126", "#12EF3A")) + ggtitle("Significant Lipids Heated v. Control Soil Organic Plots")
lipidfigure <- ggarrange(lipidfigureMineral, lipidfigureOrganic, ncol=1)
lipidfigure

Lipidomic data found 31 lipids that were altered in abundance significantly. 1 of the 31 lipid abundances were found to have increased due to an increase in temperature. From the figure, TG is Triacylglyerol

Making plots by Class

list.ggplotsbygroup <- dflong %>% group_by(Main_Class) %>% do(plots=ggplot(data=.) + aes(x=row.id, y=log(count)) + geom_boxplot(aes(fill=treatment)) + xlab("compound") + ylab("abundance (logged)") + ggtitle(unique(.$Main_Class)))

Analysis by Group

DG1 <- list.ggplotsbygroup$plots[[1]] + coord_flip()
DG1

DG; inconclusive due to number of compounds being low and no significant differences between chain lengths or degrees of saturation

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PC2 <- list.ggplotsbygroup$plots[[2]] + coord_flip()
PC2

PC; there’s an interesting difference between PC_A and PC_B which only differ in stereochemistry

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PE3 <- list.ggplotsbygroup$plots[[3]] + coord_flip()
PE3

PE is found to be a plant growth regulator

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DGDG4 <- list.ggplotsbygroup$plots[[4]] + coord_flip()
DGDG4

DGDG; decreased abundance for all compunds

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HexCer5 <- list.ggplotsbygroup$plots[[5]] + coord_flip()
HexCer5

Only 1 hexosylceramides with two different stereochemical forms found to be significantly altered due to Temeprature. The abundance signficantly decreased in the Heated plots

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DGTSA6 <- list.ggplotsbygroup$plots[[6]] + coord_flip()
DGTSA6

Significant DGTSA’s decreased in abundance as well

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TG7 <- list.ggplotsbygroup$plots[[7]] + coord_flip()
TG7

TG’s are most common lipids in soil and TG(58:9) was found to be the only lipid to increase in abundance in the heated plots over the control plots

Chain Length Relation in TG’s

more background info in Literature Review

Literature has stated that soil composition and lipid composition has been shown to adapt to environmental change including temperature. One major strategy by which plants adapt to temperature change is to decrease the degree of unsaturation of membrane lipids (aka increase saturation) under high temperature and increase [unsaturation] under low temperature.

TGchain <- summary_class[c(23:31), c(1,4)] %>%
  separate(row.identity, c("length", "dbond_number"), ":") %>%
  mutate(length = str_replace(length, "TG\\(", "")) %>%
  mutate(dbond_number = str_replace(dbond_number, "\\)", "")) %>%
  mutate(dbond_number = str_replace(dbond_number, "_B", ""))

TGplot <- ggplot(TGchain, aes(x=length, y=Standardized_Difference_Control_minus_Heated, size=dbond_number, color=dbond_number)) + geom_point() + ggtitle("Triacylglyerols") + ylab("Difference [C - H]")
TGplot

TG is the largest group (out of 7), and there was no distinguishable trend between saturation or chain length with abundance. If anything, the most saturated (48:1) has a high Heated Plot count over Control while the least saturated (58:9) has a high Control Plot count over Heated. This is the opposite to findings in plants.