Figure 4
Extracellular DNA promotes efficient extracellular electron transfer by pyocyanin in Pseudomonas aeruginosa biofilms.
Scott H. Saunders, Edmund C.M. Tse, Matthew D. Yates, Fernanda Jiménez Otero, Scott A. Trammell, Eric D.A. Stemp, Jacqueline K. Barton, Leonard M. Tender and Dianne K. Newman
Notes
Panels A-F and H are images and diagrams. The plots below are repeated as placeholders for the diagrams in panels F and H.
Setup packages and plotting for the notebook:
# Check packages
source("../../tools/package_setup.R")
# Load packages
library(tidyverse)
library(cowplot)
library(kableExtra)
# Code display options
knitr::opts_chunk$set(tidy.opts=list(width.cutoff=60),tidy=FALSE, echo = TRUE, message=FALSE, warning=FALSE, fig.align="center", fig.retina = 2)
# Load plotting tools
source("../../tools/plotting_tools.R")
#Modify the plot theme
theme_set(theme_notebook())Fig. 4G - Generator collector
Let’s read in the generator collector data files for the three conditions.
wt_gc <- read_csv("../../../data/Electrochemistry/IDA/WT_newMedia_postEquil_GC.txt",
skip = 21,col_names =c('E', 'i1', 'i2', 't')) %>% mutate(strain='WT')
dphz_gc <- read_csv("../../../data/Electrochemistry/IDA/dPHZ_d3_newMedia_GC_3mVs.txt",
skip = 0,col_names =c('E', 'i1', 'i2', 't') ) %>% mutate(strain = 'dPHZ')
dphz_pyo_gc <- read_csv("../../../data/Electrochemistry/IDA/A_75uM_PYO_GC_2.txt",
skip = 21,col_names =c('E', 'i1', 'i2', 't')) %>% mutate(strain = 'dPHZ_PYO')
# combine and extract the first scan by taking the min and max points
basic_gc <- bind_rows(wt_gc,dphz_gc,dphz_pyo_gc) %>%
group_by(strain,E) %>%
mutate(max_i1 = max(i1)) %>%
mutate(min_i2 = min(i2))
ggplot(basic_gc , aes(x = E , color = strain)) +
geom_path( aes(y = i2), size = 0.5) +
geom_path(aes(y = i1), size = 0.5) +
scale_x_reverse()
This is a plot of the raw data. To make it clearer for a non-expert audience and to make it consistent with the later GC acquisitions we will only plot the first scan and show potentials less than zero:
basic_gc$strain = fct_relevel(basic_gc$strain, c("dPHZ","WT","dPHZ_PYO"))
# Plot Layout
plot_gcBasic <- ggplot(basic_gc %>% filter(E<=0), aes(x = E , color = strain)) +
geom_vline(xintercept = -0.265, linetype=2, color = 'gray', size = 0.5) +
geom_path( aes(y = min_i2), size = 0.5) +
geom_path(aes(y = max_i1), size = 0.5)
# Plot Styling
plot_gcBasic_styled <- plot_gcBasic +
scale_x_reverse(labels = mV_label)+
scale_y_continuous(labels = nA_label)+
scale_color_manual(breaks = c("WT","dPHZ","dPHZ_PYO"),
labels = c('WT', expression(Delta*phz), expression(Delta*phz + PYO)),
values = c( "#FFCC66","#66CCFF","gray")) +
labs(x = "E (mV vs. Ag/AgCl)", y = expression(I[gc]~(nA)), color = NULL) +
theme(legend.position = c(0.2,0.8), legend.background = element_blank()) +
annotate('text', x = -0.2, y = -2e-8, label = 'Collector', size = 2) +
annotate('text', x = -0.2, y = 3e-8, label = 'Generator', size = 2)+
annotate('text', x = -0.3, y = 4e-8,label = expression({E^0}[pyo]), size = 2 )
plot_gcBasic_styled
Fig. 4I - Metabolic current
Now let’s read in the metabolic current data - aka chronoamperometry. All of the conditions were repeated multiple times. Here’s a plot that shows some of the replicates:
ca_data <- read_csv("../../../data/Electrochemistry/IDA/metabolic_current.csv", comment = "#") %>%
mutate(Strain = ifelse(Rep==1 & PYO=="+","WT","dPHZ")) %>%
mutate(id = paste(Strain,PYO, Rep, sep = " "))
ggplot(ca_data[seq(1,nrow(ca_data), 100),] %>% filter(time_h<90),
aes(x = time_h, y = I_nA)) +
geom_path(aes(color = id, group = id), size = 1) +
ylim(0,3.5)
the ∆phz replicates look very similar, so for the final figure we just show one replicate of each condition:
plot_ca_3 <- ggplot(ca_data[seq(1,nrow(ca_data), 100),] %>% filter(time_h<90) %>% filter(id %in% c("dPHZ - 2", "WT + 1", "dPHZ + 2")),
aes(x = time_h, y = I_nA)) +
geom_path(aes(color = id, group = id), size = 0.5) +
ylim(0,3.5)
plot_ca_3_styled <- plot_ca_3+
scale_color_manual(breaks = c("dPHZ - 2", "WT + 1", "dPHZ + 2"),
values = c("#FFCC66","gray","#66CCFF"),
labels = c('∆phz - PYO', 'WT','∆phz + PYO'),
guide = F) +
labs(x = 'Time (h)', y = expression(-I~(nA)), color = NULL)
plot_ca_3_styled
Create figure
theme_set(theme_figure())
fig_4 <- plot_grid(plot_gcBasic_styled,plot_gcBasic_styled, plot_ca_3_styled,plot_ca_3_styled,
align = 'hv', axis = 'tblr', rel_widths = c(1.25,2,1.25,2), ncol = 4, scale = 1.0, labels = c('F','G','H','I'), label_size = 12)
fig_4
## R version 3.5.3 (2019-03-11)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS 10.15.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] lubridate_1.7.4 hms_0.5.3 modelr_0.1.5
## [4] broom_0.5.2 kableExtra_1.1.0 cowplot_0.9.4
## [7] viridis_0.5.1 viridisLite_0.3.0 knitr_1.23
## [10] forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3
## [13] purrr_0.3.3 readr_1.3.1 tidyr_1.0.0
## [16] tibble_2.1.3 ggplot2_3.3.0 tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_0.2.5 xfun_0.7 haven_2.2.0 lattice_0.20-38
## [5] colorspace_1.4-1 vctrs_0.3.1 generics_0.0.2 htmltools_0.4.0
## [9] yaml_2.2.0 rlang_0.4.6 pillar_1.4.2 glue_1.3.1
## [13] withr_2.1.2 DBI_1.0.0 dbplyr_1.4.2 readxl_1.3.1
## [17] lifecycle_0.1.0 munsell_0.5.0 gtable_0.3.0 cellranger_1.1.0
## [21] rvest_0.3.5 evaluate_0.14 labeling_0.3 Rcpp_1.0.2
## [25] scales_1.0.0 backports_1.1.4 webshot_0.5.1 jsonlite_1.6
## [29] fs_1.3.1 gridExtra_2.3 digest_0.6.21 stringi_1.4.3
## [33] grid_3.5.3 cli_1.1.0 tools_3.5.3 magrittr_1.5
## [37] crayon_1.3.4 pkgconfig_2.0.3 xml2_1.2.2 reprex_0.3.0
## [41] assertthat_0.2.1 rmarkdown_1.13 httr_1.4.1 rstudioapi_0.10
## [45] R6_2.4.0 nlme_3.1-137 compiler_3.5.3