Notes

Panels A-D of Figure 1 are diagrams and images. The remaining panels were generated in R as shown here. Note all LC-MS data was quantified via absorbance. Chromatogram peaks were integrated in the Waters Empower software and integrations were exported and are contained in the csv files used here.

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. 1E - Wildtype phenazine concentrations

Read in the data - concentrations quantified from LC (Absorbance) peaks with standard curve.

wt_extracts <- read_csv('../../../data/LC-MS/HPLC_data_colonies_02_16_18.csv')


wt_extracts %>% kable() %>% kable_styling() %>% scroll_box(height = '250px')

Name	strain	material	replicate	RT	Area	Channel Name	Amount
PYO	wt	agar	1	6.233	48978	313.0nm	2.069
PCA	wt	agar	1	3.726	200697	364.0nm	13.352
PCN	wt	agar	1	9.071	478348	364.0nm	31.599
PYO	wt	agar	2	6.226	43440	313.0nm	1.835
PCA	wt	agar	2	3.685	150747	364.0nm	10.029
PCN	wt	agar	2	9.065	419476	364.0nm	27.710
PYO	wt	agar	3	6.243	41791	313.0nm	1.765
PCA	wt	agar	3	3.678	145807	364.0nm	9.700
PCN	wt	agar	3	9.073	390771	364.0nm	25.813
PYO	wt	biofilm	1	6.233	144296	313.0nm	6.095
PCA	wt	biofilm	1	3.804	18731	364.0nm	1.246
PCN	wt	biofilm	1	9.071	428081	364.0nm	28.278
PYO	wt	biofilm	2	6.236	133341	313.0nm	5.632
PCA	wt	biofilm	2	3.786	18272	364.0nm	1.216
PCN	wt	biofilm	2	9.064	408496	364.0nm	26.984
PYO	wt	biofilm	3	6.237	135965	313.0nm	5.743
PCA	wt	biofilm	3	3.772	18141	364.0nm	1.207
PCN	wt	biofilm	3	9.062	421803	364.0nm	27.863

Now we need to convert the concentration (Amount column) into the concentration that existed in the biofilm or the agar, not the sample tube. For the agar, we know that there is 5mL and in this case 2mL of solution was added for extraction. For the biofilm, we estimate that the volume of the colony is about 60uL, and we resuspended it in 800uL. Here, I’ll also calculate the means, so we can look at the mean and individual measurements.

# Calculate concentration from Amount. For biofilm: Amount * 800 / 62, for agar: Amount * 7/5
wt_conc <- wt_extracts %>% 
  mutate(phz_conc = ifelse(material=='biofilm', Amount * (800 / 62), Amount * (7 / 5)))

# Calculate the mean concentration for each group - 'Name' is the measured phenazine
# Only assign mean to first observation, because we only need one bar
wt_conc_means <- wt_conc %>% 
  group_by(material, Name) %>% 
  mutate(mean = ifelse(replicate==1, mean(phz_conc), NA))

Let’s plot the data with a fixed scale, so we can see the pattern of phenazine production by the WT cells:

#Plot layout
plot_wt_fixed <- ggplot(wt_conc_means, aes(x = material, fill = material)) + 
  geom_col(aes(y = mean)) + 
  geom_jitter(aes(y = phz_conc), height = 0, width = 0.1, shape = 21) +
  facet_wrap(~Name)

#Plot styling
plot_wt_fixed +
  labs(x = NULL, y = expression("Phenazine concentration" ~ ( mu*M ))) + 
  theme(axis.title.x = element_text(size = 14), line = element_line(color = 'black', size = 0.25))

And now we’ll plot each phenazine on its own Y-axis.

#Plot layout
plot_wt_free <- ggplot(wt_conc_means, aes(x = material)) + 
  geom_col(aes(y = mean, fill = material)) + 
  geom_jitter(aes(y = phz_conc), height = 0, width = 0.1, shape = 21,color = 'black', size = 1) +
  facet_wrap(~Name, scales = 'free')

#Plot styling
plot_wt_free_styled <- plot_wt_free +
  labs(x = NULL, y = expression("Phenazine\nconcentration" ~ ( mu*M ))) + 
  theme(axis.title.x = element_text(size = 14)) + 
  scale_fill_manual(guide = F, values = c("#66CCFF","#FFCC66"))+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
    
plot_wt_free_styled

Fig. 1F - WT phenazine retention ratio

# Split dataset by material
wt_conc_means_agar <- wt_conc_means %>% 
  filter(material=='agar')

wt_conc_means_biofilm <- wt_conc_means %>% 
  filter(material=='biofilm')

# Join agar and cell observations and calculate retention ratios = biofilm / agar
wt_conc_means_join <- left_join(wt_conc_means_biofilm, wt_conc_means_agar, 
                                by = c('strain','replicate','Name'), 
                                suffix = c('_from_biofilm','_from_agar')) %>% 
  mutate(retention_ratio = phz_conc_from_biofilm / phz_conc_from_agar) %>% 
  mutate(mean_retention_ratio = mean_from_biofilm / mean_from_agar)

# Plot Layout
wt_ret_ratio <- ggplot(wt_conc_means_join ,aes(x=Name,y=retention_ratio))+
  geom_col( aes(y = mean_retention_ratio), fill = 'light gray') +
  geom_jitter(width=0.1,height=0,shape=21,size=1)

# Plot Stlying
wt_ret_ratio_styled <- wt_ret_ratio +
  guides(fill=F) +
  scale_y_continuous(labels = fold_label) + 
  labs(x = NULL, y = "[Biofilm] / [Agar]") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

wt_ret_ratio_styled

Fig. 1G - ∆phz* phenazine accumlation

Let’s first read in the data. This csv contains the data for multiple experiments that were run on the LC-MS on the same day, so let’s just look at the dPHZ* colonies.

# Read in only the dPHZstar data, and convert the amount of phenazine added to a numeric
dphz_extracts <- read_csv('../../../data/LC-MS/2018_10_30_HPLC_concentrations_df.csv',comment = "#") %>% 
  filter(strain == 'dPHZstar') %>% 
  mutate(added_phz_num=as.double(str_extract(amount_added,"^[0-9.]+")))

dphz_extracts %>% 
  kable() %>% 
  kable_styling() %>% 
  scroll_box(height = '250px')

measured_phenazine	strain	amount_added	added_phenazine	material	replicate	RT	Area	Channel Name	Amount	calcConc	added_phz_num
PCA	dPHZstar	0.1uM	PCA	cells	1	3.183	61	364.0nm	0.004	0.1146667	0.1
PCN	dPHZstar	0.1uM	PCA	cells	1	8.693	653	364.0nm	0.043	1.2326667	0.1
PYO	dPHZstar	0.1uM	PCA	cells	1	5.976	20	313.0nm	0.001	0.0286667	0.1
PCA	dPHZstar	0.1uM	PCA	cells	2	3.044	52	364.0nm	0.003	0.0860000	0.1
PCN	dPHZstar	0.1uM	PCA	cells	2	8.700	744	364.0nm	0.049	1.4046667	0.1
PCA	dPHZstar	0.1uM	PCA	cells	3	3.154	55	364.0nm	0.004	0.1146667	0.1
PCN	dPHZstar	0.1uM	PCA	cells	3	8.686	756	364.0nm	0.050	1.4333333	0.1
PCA	dPHZstar	0.1uM	PCN	cells	1	2.972	56	364.0nm	0.004	0.1146667	0.1
PCN	dPHZstar	0.1uM	PCN	cells	1	8.696	880	364.0nm	0.058	1.6626667	0.1
PYO	dPHZstar	0.1uM	PCN	cells	1	6.050	20	313.0nm	0.001	0.0286667	0.1
PCA	dPHZstar	0.1uM	PCN	cells	2	3.294	70	364.0nm	0.005	0.1433333	0.1
PCN	dPHZstar	0.1uM	PCN	cells	2	8.703	562	364.0nm	0.037	1.0606667	0.1
PYO	dPHZstar	0.1uM	PCN	cells	2	5.950	21	313.0nm	0.001	0.0286667	0.1
PCA	dPHZstar	0.1uM	PCN	cells	3	2.957	39	364.0nm	0.003	0.0860000	0.1
PCN	dPHZstar	0.1uM	PCN	cells	3	8.695	487	364.0nm	0.032	0.9173333	0.1
PYO	dPHZstar	0.1uM	PCN	cells	3	5.911	27	313.0nm	0.001	0.0286667	0.1
PCA	dPHZstar	0.1uM	PYO	cells	1	3.365	66	364.0nm	0.004	0.1146667	0.1
PCN	dPHZstar	0.1uM	PYO	cells	1	8.690	682	364.0nm	0.045	1.2900000	0.1
PCA	dPHZstar	0.1uM	PYO	cells	2	3.191	90	364.0nm	0.006	0.1720000	0.1
PCN	dPHZstar	0.1uM	PYO	cells	2	8.696	414	364.0nm	0.027	0.7740000	0.1
PYO	dPHZstar	0.1uM	PYO	cells	2	6.010	448	313.0nm	0.014	0.4013333	0.1
PCA	dPHZstar	0.1uM	PYO	cells	3	3.344	50	364.0nm	0.003	0.0860000	0.1
PCN	dPHZstar	0.1uM	PYO	cells	3	8.689	1331	364.0nm	0.088	2.5226667	0.1
PYO	dPHZstar	0.1uM	PYO	cells	3	6.006	471	313.0nm	0.014	0.4013333	0.1
PCA	dPHZstar	100uM	PCA	cells	1	3.203	11969	364.0nm	0.786	22.5320000	100.0
PCN	dPHZstar	100uM	PCA	cells	1	8.699	1089	364.0nm	0.072	2.0640000	100.0
PYO	dPHZstar	100uM	PCA	cells	1	6.008	22	313.0nm	0.001	0.0286667	100.0
PCA	dPHZstar	100uM	PCA	cells	2	3.182	10593	364.0nm	0.695	19.9233333	100.0
PCN	dPHZstar	100uM	PCA	cells	2	8.689	752	364.0nm	0.050	1.4333333	100.0
PYO	dPHZstar	100uM	PCA	cells	2	5.933	37	313.0nm	0.001	0.0286667	100.0
PCA	dPHZstar	100uM	PCA	cells	3	3.183	7457	364.0nm	0.490	14.0466667	100.0
PCN	dPHZstar	100uM	PCA	cells	3	8.686	821	364.0nm	0.054	1.5480000	100.0
PCA	dPHZstar	100uM	PCN	cells	1	3.434	167	364.0nm	0.011	0.3153333	100.0
PCN	dPHZstar	100uM	PCN	cells	1	8.856	55528	364.0nm	3.663	105.0060000	100.0
PYO	dPHZstar	100uM	PCN	cells	1	5.962	23	313.0nm	0.001	0.0286667	100.0
PCA	dPHZstar	100uM	PCN	cells	2	3.445	162	364.0nm	0.011	0.3153333	100.0
PCN	dPHZstar	100uM	PCN	cells	2	8.861	57086	364.0nm	3.766	107.9586667	100.0
PCA	dPHZstar	100uM	PCN	cells	3	3.153	69	364.0nm	0.005	0.1433333	100.0
PCN	dPHZstar	100uM	PCN	cells	3	8.847	61182	364.0nm	4.036	115.6986667	100.0
PYO	dPHZstar	100uM	PCN	cells	3	6.035	27	313.0nm	0.001	0.0286667	100.0
PCA	dPHZstar	100uM	PYO	cells	1	3.206	79	364.0nm	0.005	0.1433333	100.0
PCN	dPHZstar	100uM	PYO	cells	1	8.695	863	364.0nm	0.057	1.6340000	100.0
PYO	dPHZstar	100uM	PYO	cells	1	6.010	470878	313.0nm	14.484	415.2080000	100.0
PCA	dPHZstar	100uM	PYO	cells	2	3.447	61	364.0nm	0.004	0.1146667	100.0
PCN	dPHZstar	100uM	PYO	cells	2	8.694	977	364.0nm	0.064	1.8346667	100.0
PYO	dPHZstar	100uM	PYO	cells	2	6.010	402519	313.0nm	12.382	354.9506667	100.0
PCA	dPHZstar	100uM	PYO	cells	3	2.987	95	364.0nm	0.006	0.1720000	100.0
PCN	dPHZstar	100uM	PYO	cells	3	8.696	800	364.0nm	0.053	1.5193333	100.0
PYO	dPHZstar	100uM	PYO	cells	3	6.005	364574	313.0nm	11.214	321.4680000	100.0
PCA	dPHZstar	10uM	PCA	cells	1	3.215	602	364.0nm	0.039	1.1180000	10.0
PCN	dPHZstar	10uM	PCA	cells	1	8.699	810	364.0nm	0.053	1.5193333	10.0
PYO	dPHZstar	10uM	PCA	cells	1	6.083	23	313.0nm	0.001	0.0286667	10.0
PCA	dPHZstar	10uM	PCA	cells	2	3.197	151	364.0nm	0.010	0.2866667	10.0
PCN	dPHZstar	10uM	PCA	cells	2	8.691	465	364.0nm	0.031	0.8886667	10.0
PCA	dPHZstar	10uM	PCA	cells	3	3.167	767	364.0nm	0.050	1.4333333	10.0
PCN	dPHZstar	10uM	PCA	cells	3	8.681	714	364.0nm	0.047	1.3473333	10.0
PYO	dPHZstar	10uM	PCA	cells	3	6.058	18	313.0nm	0.001	0.0286667	10.0
PCA	dPHZstar	10uM	PCN	cells	1	3.450	69	364.0nm	0.005	0.1433333	10.0
PCN	dPHZstar	10uM	PCN	cells	1	8.858	6130	364.0nm	0.404	11.5813333	10.0
PYO	dPHZstar	10uM	PCN	cells	1	6.010	33	313.0nm	0.001	0.0286667	10.0
PCA	dPHZstar	10uM	PCN	cells	2	3.321	105	364.0nm	0.007	0.2006667	10.0
PCN	dPHZstar	10uM	PCN	cells	2	8.864	6627	364.0nm	0.437	12.5273333	10.0
PCA	dPHZstar	10uM	PCN	cells	3	3.446	78	364.0nm	0.005	0.1433333	10.0
PCN	dPHZstar	10uM	PCN	cells	3	8.837	7021	364.0nm	0.463	13.2726667	10.0
PYO	dPHZstar	10uM	PCN	cells	3	5.946	25	313.0nm	0.001	0.0286667	10.0
PCA	dPHZstar	10uM	PYO	cells	1	3.206	80	364.0nm	0.005	0.1433333	10.0
PCN	dPHZstar	10uM	PYO	cells	1	8.680	377	364.0nm	0.025	0.7166667	10.0
PYO	dPHZstar	10uM	PYO	cells	1	6.004	38908	313.0nm	1.197	34.3140000	10.0
PCA	dPHZstar	10uM	PYO	cells	2	3.394	76	364.0nm	0.005	0.1433333	10.0
PCN	dPHZstar	10uM	PYO	cells	2	8.687	921	364.0nm	0.061	1.7486667	10.0
PYO	dPHZstar	10uM	PYO	cells	2	6.012	44695	313.0nm	1.375	39.4166667	10.0
PCA	dPHZstar	10uM	PYO	cells	3	3.267	38	364.0nm	0.002	0.0573333	10.0
PCN	dPHZstar	10uM	PYO	cells	3	8.693	1124	364.0nm	0.074	2.1213333	10.0
PYO	dPHZstar	10uM	PYO	cells	3	6.011	42861	313.0nm	1.318	37.7826667	10.0
PCA	dPHZstar	1uM	PCA	cells	1	3.400	62	364.0nm	0.004	0.1146667	1.0
PCN	dPHZstar	1uM	PCA	cells	1	8.699	822	364.0nm	0.054	1.5480000	1.0
PYO	dPHZstar	1uM	PCA	cells	1	6.056	44	313.0nm	0.001	0.0286667	1.0
PCA	dPHZstar	1uM	PCA	cells	2	2.981	69	364.0nm	0.005	0.1433333	1.0
PCN	dPHZstar	1uM	PCA	cells	2	8.687	851	364.0nm	0.056	1.6053333	1.0
PYO	dPHZstar	1uM	PCA	cells	2	6.039	22	313.0nm	0.001	0.0286667	1.0
PCA	dPHZstar	1uM	PCA	cells	3	3.049	61	364.0nm	0.004	0.1146667	1.0
PCN	dPHZstar	1uM	PCA	cells	3	8.688	786	364.0nm	0.052	1.4906667	1.0
PYO	dPHZstar	1uM	PCA	cells	3	6.095	17	313.0nm	0.001	0.0286667	1.0
PCA	dPHZstar	1uM	PCN	cells	1	3.066	46	364.0nm	0.003	0.0860000	1.0
PCN	dPHZstar	1uM	PCN	cells	1	8.694	863	364.0nm	0.057	1.6340000	1.0
PYO	dPHZstar	1uM	PCN	cells	1	5.903	16	313.0nm	0.000	0.0000000	1.0
PCA	dPHZstar	1uM	PCN	cells	2	2.998	72	364.0nm	0.005	0.1433333	1.0
PCN	dPHZstar	1uM	PCN	cells	2	8.703	845	364.0nm	0.056	1.6053333	1.0
PYO	dPHZstar	1uM	PCN	cells	2	5.985	24	313.0nm	0.001	0.0286667	1.0
PCA	dPHZstar	1uM	PCN	cells	3	3.066	69	364.0nm	0.004	0.1146667	1.0
PCN	dPHZstar	1uM	PCN	cells	3	8.853	653	364.0nm	0.043	1.2326667	1.0
PYO	dPHZstar	1uM	PCN	cells	3	6.048	44	313.0nm	0.001	0.0286667	1.0
PCA	dPHZstar	1uM	PYO	cells	1	3.398	52	364.0nm	0.003	0.0860000	1.0
PCN	dPHZstar	1uM	PYO	cells	1	8.698	729	364.0nm	0.048	1.3760000	1.0
PYO	dPHZstar	1uM	PYO	cells	1	6.012	1991	313.0nm	0.061	1.7486667	1.0
PCA	dPHZstar	1uM	PYO	cells	2	3.168	50	364.0nm	0.003	0.0860000	1.0
PCN	dPHZstar	1uM	PYO	cells	2	8.691	1344	364.0nm	0.089	2.5513333	1.0
PYO	dPHZstar	1uM	PYO	cells	2	6.004	2238	313.0nm	0.069	1.9780000	1.0
PCA	dPHZstar	1uM	PYO	cells	3	3.004	115	364.0nm	0.008	0.2293333	1.0
PCN	dPHZstar	1uM	PYO	cells	3	8.701	1335	364.0nm	0.088	2.5226667	1.0
PYO	dPHZstar	1uM	PYO	cells	3	6.021	2294	313.0nm	0.071	2.0353333	1.0
PCA	dPHZstar	200uM	PCA	cells	1	3.185	22915	364.0nm	1.504	43.1146667	200.0
PCN	dPHZstar	200uM	PCA	cells	1	8.675	859	364.0nm	0.057	1.6340000	200.0
PYO	dPHZstar	200uM	PCA	cells	1	6.027	42	313.0nm	0.001	0.0286667	200.0
PCA	dPHZstar	200uM	PCA	cells	2	3.182	24033	364.0nm	1.578	45.2360000	200.0
PCN	dPHZstar	200uM	PCA	cells	2	8.684	668	364.0nm	0.044	1.2613333	200.0
PYO	dPHZstar	200uM	PCA	cells	2	5.920	24	313.0nm	0.001	0.0286667	200.0
PCA	dPHZstar	200uM	PCA	cells	3	3.178	18464	364.0nm	1.212	34.7440000	200.0
PCN	dPHZstar	200uM	PCA	cells	3	8.680	928	364.0nm	0.061	1.7486667	200.0
PCA	dPHZstar	200uM	PCN	cells	1	3.241	176	364.0nm	0.012	0.3440000	200.0
PCN	dPHZstar	200uM	PCN	cells	1	8.663	101344	364.0nm	6.685	191.6366667	200.0
PYO	dPHZstar	200uM	PCN	cells	1	6.013	31	313.0nm	0.001	0.0286667	200.0
PCA	dPHZstar	200uM	PCN	cells	2	3.137	113	364.0nm	0.007	0.2006667	200.0
PCN	dPHZstar	200uM	PCN	cells	2	8.857	95428	364.0nm	6.295	180.4566667	200.0
PYO	dPHZstar	200uM	PCN	cells	2	5.946	20	313.0nm	0.001	0.0286667	200.0
PCA	dPHZstar	200uM	PCN	cells	3	3.064	127	364.0nm	0.008	0.2293333	200.0
PCN	dPHZstar	200uM	PCN	cells	3	8.866	120549	364.0nm	7.952	227.9573333	200.0
PYO	dPHZstar	200uM	PCN	cells	3	6.067	26	313.0nm	0.001	0.0286667	200.0
PCA	dPHZstar	200uM	PYO	cells	1	3.142	81	364.0nm	0.005	0.1433333	200.0
PCN	dPHZstar	200uM	PYO	cells	1	8.692	586	364.0nm	0.039	1.1180000	200.0
PYO	dPHZstar	200uM	PYO	cells	1	6.002	822507	313.0nm	25.301	725.2953333	200.0
PCA	dPHZstar	200uM	PYO	cells	2	3.173	94	364.0nm	0.006	0.1720000	200.0
PCN	dPHZstar	200uM	PYO	cells	2	8.682	603	364.0nm	0.040	1.1466667	200.0
PYO	dPHZstar	200uM	PYO	cells	2	5.997	809545	313.0nm	24.902	713.8573333	200.0
PCA	dPHZstar	200uM	PYO	cells	3	3.392	78	364.0nm	0.005	0.1433333	200.0
PCN	dPHZstar	200uM	PYO	cells	3	8.699	625	364.0nm	0.041	1.1753333	200.0
PYO	dPHZstar	200uM	PYO	cells	3	6.001	763048	313.0nm	23.472	672.8640000	200.0
PCA	dPHZstar	50uM	PCA	cells	1	3.212	5893	364.0nm	0.387	11.0940000	50.0
PCN	dPHZstar	50uM	PCA	cells	1	8.686	915	364.0nm	0.060	1.7200000	50.0
PYO	dPHZstar	50uM	PCA	cells	1	5.963	21	313.0nm	0.001	0.0286667	50.0
PCA	dPHZstar	50uM	PCA	cells	2	3.190	2312	364.0nm	0.152	4.3573333	50.0
PCN	dPHZstar	50uM	PCA	cells	2	8.689	356	364.0nm	0.023	0.6593333	50.0
PYO	dPHZstar	50uM	PCA	cells	2	5.930	25	313.0nm	0.001	0.0286667	50.0
PCA	dPHZstar	50uM	PCA	cells	3	3.172	3501	364.0nm	0.230	6.5933333	50.0
PCN	dPHZstar	50uM	PCA	cells	3	8.692	660	364.0nm	0.044	1.2613333	50.0
PCA	dPHZstar	50uM	PCN	cells	1	3.444	191	364.0nm	0.013	0.3726667	50.0
PCN	dPHZstar	50uM	PCN	cells	1	8.852	33228	364.0nm	2.192	62.8373333	50.0
PYO	dPHZstar	50uM	PCN	cells	1	5.963	29	313.0nm	0.001	0.0286667	50.0
PCA	dPHZstar	50uM	PCN	cells	2	3.442	110	364.0nm	0.007	0.2006667	50.0
PCN	dPHZstar	50uM	PCN	cells	2	8.853	34817	364.0nm	2.297	65.8473333	50.0
PYO	dPHZstar	50uM	PCN	cells	2	5.907	32	313.0nm	0.001	0.0286667	50.0
PCA	dPHZstar	50uM	PCN	cells	3	3.444	109	364.0nm	0.007	0.2006667	50.0
PCN	dPHZstar	50uM	PCN	cells	3	8.856	32346	364.0nm	2.134	61.1746667	50.0
PYO	dPHZstar	50uM	PCN	cells	3	6.071	27	313.0nm	0.001	0.0286667	50.0
PCA	dPHZstar	50uM	PYO	cells	1	2.959	114	364.0nm	0.007	0.2006667	50.0
PCN	dPHZstar	50uM	PYO	cells	1	8.692	1036	364.0nm	0.068	1.9493333	50.0
PYO	dPHZstar	50uM	PYO	cells	1	6.008	251039	313.0nm	7.722	221.3640000	50.0
PCA	dPHZstar	50uM	PYO	cells	2	3.014	91	364.0nm	0.006	0.1720000	50.0
PCN	dPHZstar	50uM	PYO	cells	2	8.687	365	364.0nm	0.024	0.6880000	50.0
PYO	dPHZstar	50uM	PYO	cells	2	6.000	307890	313.0nm	9.471	271.5020000	50.0
PCA	dPHZstar	50uM	PYO	cells	3	3.192	71	364.0nm	0.005	0.1433333	50.0
PCN	dPHZstar	50uM	PYO	cells	3	8.690	841	364.0nm	0.055	1.5766667	50.0
PYO	dPHZstar	50uM	PYO	cells	3	6.002	227059	313.0nm	6.984	200.2080000	50.0

You can see that I’ve already calculated the biofilm concentrations for this dataset. You can also see that each colony was recorded with the amount of synthetic phenazine added to the agar. Let’s calculate means for each group and look at an overview of the measurements.

# Names for facet panels
facet_labels = c(PCA = "PCA added", PCN = "PCN added", PYO = "PYO added")

# Plot layout
plot_dphz_overview <- ggplot(dphz_extracts, aes(x = measured_phenazine, y = calcConc, fill = amount_added)) + 
  geom_jitter(shape = 21, height = 0, width = 0.1, size = 2)+
  facet_wrap(~added_phenazine, scale = 'free', labeller = labeller(added_phenazine = facet_labels))

# Plot styling
plot_dphz_overview +
  scale_fill_viridis(discrete = T, breaks = c('0.1uM','1uM','10uM','50uM','100uM','200uM')) +
  labs(fill = "Amount of\nphenazine added" , 
       x = "Phenazine measured", 
       y = expression("Biofilm concentration" ~ ( mu*M )) )

Ok, so we can see that when we add a phenazine to this strain, we only measure significant amounts of that phenazine. Therefore, this mutant is “phenazine inert.” There is a little bit of PCN detected, but upon inspection of the chromatograms and MS, it is a persistent background peak that is not actually PCN.

So, let’s proceed and just look at the measured phenazines that match the added phenazine, and we’ll calculate some means.

# Calculate Means
dphz_extracts_means <- dphz_extracts %>% 
  filter(measured_phenazine==added_phenazine) %>% 
  group_by(measured_phenazine,amount_added) %>% 
  mutate(mean = mean(calcConc))


# Plot layout
plot_dphz_binding <- ggplot(dphz_extracts_means, aes(x=added_phz_num,y=calcConc))+
  geom_line(size=0.5, aes(y=mean, group = measured_phenazine))+
  geom_point(size=1, aes(shape=measured_phenazine)) +
  geom_abline(slope=1, intercept =0, linetype = 2)

# Plot styling
plot_dphz_binding_styled <- plot_dphz_binding + 
  scale_shape_discrete(solid=F, guide = F)+
  labs(x=expression('Phenazine added to agar ('~mu*"M)"), 
       y = expression("Biofilm\nconcentration" ~ ( mu*M )) ) +
  theme(legend.position = c(0.5, 0.9),
        legend.title = element_blank(),
        legend.background = element_rect(fill=NA),
        axis.text.y = element_text(angle = 45, hjust = 1))

plot_dphz_binding_styled

Fig. 1H - ∆phz* phenazine retention

pdaMan_data <- read_csv("../../../data/LC-MS/dPHZstar_PDAmanual_PHZretention_08_29_18.csv") %>% 
  mutate(calcConc = Amount * 2 *(800 / 62) ) %>% 
  group_by(Condition,Day,measured_phenazine) %>% 
  mutate(mean = ifelse(Rep==1,mean(calcConc),NA))

dphz_ret_pdaMan_plot <- ggplot(pdaMan_data %>% filter(Condition =='PHZ'), aes(x = Day, y = calcConc, )) + 
  geom_col(aes(y = mean,fill = Day))+
  geom_jitter(height = 0, width = 0.1, shape = 21) +
  facet_wrap(~measured_phenazine, scales = 'free') + 
  ylim(0,NA)

#Plot styling
dphz_ret_pdaMan_plot_styled <- dphz_ret_pdaMan_plot +
  labs(x = NULL, y = "Phenazine Concentration") + 
  theme(axis.title.x = element_text(size = 14)) + 
  scale_fill_manual(guide = F, values = c("#66CCFF","#FFCC66")) + 
  scale_x_discrete(breaks = c('D3','D4'), 
                   labels=c("Day 3","Day 4"))

dphz_ret_pdaMan_plot_styled

For dPHZ* incubated with individual phenazines

pdaMan_indPhz_plot <- ggplot(pdaMan_data %>% filter(Condition == measured_phenazine), aes(x = Day, y = calcConc )) + 
  geom_col(aes(y = mean), fill='light gray')+
  geom_jitter(height = 0, width = 0.1, shape = 21, size = 1) +
  facet_wrap(~measured_phenazine, scales = 'free') + 
  ylim(0,NA)

#Plot styling
pdaMan_indPhz_plot_styled <- pdaMan_indPhz_plot +
  labs(x = NULL, y = expression("Biofilm concentration" ~ (mu*M ))) + 
  theme(axis.title.x = element_text(size = 14)) + 
  scale_fill_manual(guide = F, values = c("#66CCFF","#FFCC66")) + 
  scale_x_discrete(breaks = c('D3','D4'), labels=c("Day 3","Day 4"))+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

pdaMan_indPhz_plot_styled

df_d3 <- pdaMan_data %>% filter(Condition == measured_phenazine) %>% filter(Day == 'D3') %>% mutate(d3_conc = calcConc) %>% select(measured_phenazine, Rep, d3_conc, )

df_d4 <- pdaMan_data %>% filter(Condition == measured_phenazine) %>% filter(Day == 'D4')%>% mutate(d4_conc = calcConc) %>% select(measured_phenazine, Rep, d4_conc)

df_d3_d4 <- left_join(df_d3, df_d4, by = c('measured_phenazine','Rep')) %>% 
  mutate(percent_retained = d4_conc / d3_conc) %>% 
  group_by(measured_phenazine) %>% 
  mutate(mean = ifelse(Rep == 1, mean(percent_retained), NA))

per_ret_plot <- ggplot(df_d3_d4, aes(x = measured_phenazine, y = percent_retained, )) + 
  geom_col(aes(y = mean), fill='light gray')+
  geom_jitter(height = 0, width = 0.1, shape = 21, size = 1) +
  ylim(0,NA)

#Plot styling
per_ret_plot_styled <- per_ret_plot +
  labs(x = NULL, y = 'Biofilm retained\nphenazine', title = '24 hrs post incubation') + 
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

per_ret_plot_styled

Create Fig. 1

theme_set(theme_figure())

fig_1 <- plot_grid(plot_wt_free_styled,wt_ret_ratio_styled, 
                   plot_dphz_binding_styled, per_ret_plot_styled, 
                   ncol = 2, rel_heights = c(1,1), rel_widths = c(1.5,1), 
                   align = 'hv', axis = 'lr', scale = 1.0,
                   labels = c('E','F','G','H'))

fig_1

save_plot("../../../figures/phz2019_Fig_1.pdf", fig_1, base_width = 3.42, base_height = 3)

sessionInfo()

## 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     highr_0.8       
## [25] Rcpp_1.0.2       scales_1.0.0     backports_1.1.4  webshot_0.5.1   
## [29] jsonlite_1.6     fs_1.3.1         gridExtra_2.3    digest_0.6.21   
## [33] stringi_1.4.3    grid_3.5.3       cli_1.1.0        tools_3.5.3     
## [37] magrittr_1.5     crayon_1.3.4     pkgconfig_2.0.3  xml2_1.2.2      
## [41] reprex_0.3.0     assertthat_0.2.1 rmarkdown_1.13   httr_1.4.1      
## [45] rstudioapi_0.10  R6_2.4.0         nlme_3.1-137     compiler_3.5.3

Figure 1

Extracellular DNA promotes efficient extracellular electron transfer by pyocyanin in Pseudomonas aeruginosa biofilms.