Reduced phenazine prep

100uM of PCA, PYO and PCN were reduced in PBS 50 (50mL) in the survival assay chambers. Electrolysis was performed overnight and the expected color changes were observed. Reduced phenazines were transferred into stoppered serum bottles. Serum bottles were transferred to the anaerobic chamber housing the plate reader.

Pre electrolysis (left) & post electrolysis (right)

Clockwise from the bottom right the chambers are PYO, PCA and PCN.

Electrolysis curves (left) & serum bottles (right)

Electrolysis seemed to go to completion and the color changes remained in the serum bottles upon transfer to the other chamber.

Plate reader measurements

Black walled, clear bottomed fluorescence 96 well plates were stored in the chamber in advance. 1mL tubes of oxidized phenazines (100uM in PBS 50), and buffer (PBS 50), were transferred into the chamber the morning of and were shaken / pipetted repeatedly to remove oxygen.

Wells were measured in two fluorescence channels 360/40ex,460/40 em and 360/40ex, 528/40 em and absorbance scans were taken from 300nm - 700nm in increments of 10nm. Wells were scanned with oxidized phenazines before mixing, and then repeatedly scanned in a timecourse following mixing.

Experimental design

Mixing 75uL of reduced phz (or buffer) with 75uL of oxidized phz (or buffer)

1. Buffer

• Buffer –> Buffer
• Buffer –> \(PCA_{ox}\)
• Buffer –> \(PCN_{ox}\)
• Buffer –> \(PYO_{ox}\)

2. \(PCA_{red}\)

• \(PCA_{red}\) –> Buffer
• \(PCA_{red}\) –> \(PYO_{ox}\)
• \(PCA_{red}\) –> \(PCN_{ox}\)

3. \(PCN_{red}\)

• \(PCN_{red}\) –> Buffer
• \(PCN_{red}\) –> \(PYO_{ox}\)
• \(PCN_{red}\) –> \(PCA_{ox}\)

4. \(PYO_{red}\)

• \(PYO_{red}\) –> Buffer
• \(PYO_{red}\) –> \(PCA_{ox}\)
• \(PYO_{red}\) –> \(PCN_{ox}\)

In this way we acquired spectra in the measured channels for all of the oxidized and reduced phenazines in isolation and in combination. Well conditions were repeated in triplicate.

The actual setup and plate reader program was to do the PBS controls first with oxidized and reduced phenazines (wells B1-B6). The plate reader program was to take an absorbance spectra, then do a kinetic run for 2min monitoring all the abs and fluor wavelenghts, then taking another absorbance spectra.

After the controls, then I setup wells C1-C6 with PCN_ox (C1-C3) and PYO_ox (C4 - C6) and added PCA_red. Then I did PCN_red with PCA_ox and PYO_ox in wells D1-D6, followed by PYO_red with PCA_ox and PCN_ox (E1-E6). Finally I ran the same plate reader program on the entire plate. Therefore there are 4 spectra and two kinetic runs per well. Howerver, as you will see, we only need to look at the first spectra and the first kinetic read.

Reference Spectra / values

Let’s start by looking at the reference spectra for the oxidized and reduced phenazines.

df_spectra <- read_csv("data/2019_09_17_solution_ET_abs_spectra_1.csv", 
    skip = 1) %>% gather(key = "well", value = "intensity", -Wavelength)

df_spectra_meta <- left_join(df_spectra, metadata, by = c("well")) %>% 
    mutate(phz = case_when(red == "PYO" | ox == "PYO" ~ "PYO", 
        red == "PCA" | ox == "PCA" ~ "PCA", red == "PCN" | ox == 
            "PCN" ~ "PCN", red_ox == "PBS_PBS" & rep == 1 ~ "PCA", 
        red_ox == "PBS_PBS" & rep == 2 ~ "PCN", red_ox == "PBS_PBS" & 
            rep == 3 ~ "PYO")) %>% mutate(phz_redox = ifelse(red == 
    "PBS", "phz_ox", "phz_red"))

ggplot(df_spectra_meta %>% filter(rep == 1) %>% filter(exp == 
    "ref" & red_ox != "PBS_PBS"), aes(x = Wavelength, color = phz_redox, 
    group = red_ox)) + geom_vline(xintercept = c(310, 360, 500, 
    690), color = "gray", linetype = 2, alpha = 0.4) + geom_path(data = df_spectra_meta %>% 
    filter(red_ox == "PBS_PBS"), aes(x = Wavelength, y = intensity), 
    color = "gray") + geom_path(aes(y = intensity), size = 1) + 
    facet_wrap(~phz, scales = "free") + labs(color = NULL, y = "Absorbance")

Gray is the PBS background and the dashed gray lines are the wavelengths I measured over time (not including the fluorescence channels).

df_ref_1 <- left_join(df_spectra_meta %>% filter(exp == "exp"), 
    df_spectra_meta %>% filter(exp == "ref") %>% select(Wavelength, 
        well, intensity, red, ox, red_ox), by = c("red", "Wavelength"), 
    suffix = c("", "_phz1_red"))

df_ref_2 <- left_join(df_ref_1, df_spectra_meta %>% filter(exp == 
    "ref") %>% select(Wavelength, well, intensity, red, ox, red_ox), 
    by = c(red = "ox", "Wavelength"), suffix = c("", "_phz1_ox"))

df_ref_3 <- left_join(df_ref_2, df_spectra_meta %>% filter(exp == 
    "ref") %>% select(Wavelength, well, intensity, red, ox, red_ox), 
    by = c("ox", "Wavelength"), suffix = c("", "_phz2_ox"))

df_ref_4 <- left_join(df_ref_3, df_spectra_meta %>% filter(exp == 
    "ref") %>% select(Wavelength, well, intensity, red, ox, red_ox), 
    by = c(ox = "red", "Wavelength"), suffix = c("", "_phz2_red"))


legend_segments <- tibble(x = c(500, 500, 500, 500), xend = c(550, 
    550, 550, 550), xlabel = c(525, 525, 525, 525), y = c(0.4, 
    0.3, 0.2, 0.1), color = c("red", "red", "blue", "blue"), 
    linetype = c(1, 2, 1, 2), label = c("PHZ 1 reduced (init)", 
        "PHZ 1 oxidized (final)", "PHZ 2 oxidized (init)", "PHZ 2 reduced (final)"))


color = c("red", "red", "blue", "blue")
linetype = c(1, 2, 1, 2)

spectra_legend <- geom_segment(data = legend_segments, aes(x = x, 
    xend = xend, y = y, yend = y), color = color, linetype = linetype, 
    size = 1)

legend_labs <- geom_text(data = legend_segments, aes(x = xlabel, 
    y = y, label = label), nudge_y = 0.05)

ggplot(df_ref_4 %>% filter(rep == 1 & red_ox == "PCA_PYO"), aes(x = Wavelength)) + 
    geom_path(aes(y = intensity_phz1_red), color = "red", linetype = 1, 
        size = 1) + geom_path(aes(y = intensity_phz1_ox), color = "red", 
    linetype = 2, size = 1) + geom_path(aes(y = intensity_phz2_ox), 
    color = "blue", linetype = 1, size = 1) + geom_path(aes(y = intensity_phz2_red), 
    color = "blue", linetype = 2, size = 1) + geom_path(aes(y = intensity), 
    size = 1) + facet_wrap(~red_ox, scales = "free") + spectra_legend + 
    legend_labs

ggplot(df_ref_4 %>% filter(rep == 1 & red_ox == "PCN_PYO"), aes(x = Wavelength)) + 
    geom_path(aes(y = intensity_phz1_red), color = "red", linetype = 1, 
        size = 1) + geom_path(aes(y = intensity_phz1_ox), color = "red", 
    linetype = 2, size = 1) + geom_path(aes(y = intensity_phz2_ox), 
    color = "blue", linetype = 1, size = 1) + geom_path(aes(y = intensity_phz2_red), 
    color = "blue", linetype = 2, size = 1) + geom_path(aes(y = intensity), 
    size = 1) + facet_wrap(~red_ox, scales = "free") + spectra_legend + 
    legend_labs

ggplot(df_ref_4 %>% filter(rep == 1 & red_ox == "PYO_PCA"), aes(x = Wavelength)) + 
    geom_path(aes(y = intensity_phz1_red), color = "red", linetype = 1, 
        size = 1) + geom_path(aes(y = intensity_phz1_ox), color = "red", 
    linetype = 2, size = 1) + geom_path(aes(y = intensity_phz2_ox), 
    color = "blue", linetype = 1, size = 1) + geom_path(aes(y = intensity_phz2_red), 
    color = "blue", linetype = 2, size = 1) + geom_path(aes(y = intensity), 
    size = 1) + facet_wrap(~red_ox, scales = "free") + spectra_legend + 
    legend_labs

ggplot(df_ref_4 %>% filter(rep == 1 & red_ox == "PYO_PCN"), aes(x = Wavelength)) + 
    geom_path(aes(y = intensity_phz1_red), color = "red", linetype = 1, 
        size = 1) + geom_path(aes(y = intensity_phz1_ox), color = "red", 
    linetype = 2, size = 1) + geom_path(aes(y = intensity_phz2_ox), 
    color = "blue", linetype = 1, size = 1) + geom_path(aes(y = intensity_phz2_red), 
    color = "blue", linetype = 2, size = 1) + geom_path(aes(y = intensity), 
    size = 1) + facet_wrap(~red_ox, scales = "free") + spectra_legend + 
    legend_labs

ggplot(df_ref_4 %>% filter(rep == 1 & red_ox == "PCN_PCA"), aes(x = Wavelength)) + 
    geom_path(aes(y = intensity_phz1_red), color = "red", linetype = 1, 
        size = 1) + geom_path(aes(y = intensity_phz1_ox), color = "red", 
    linetype = 2, size = 1) + geom_path(aes(y = intensity_phz2_ox), 
    color = "blue", linetype = 1, size = 1) + geom_path(aes(y = intensity_phz2_red), 
    color = "blue", linetype = 2, size = 1) + geom_path(aes(y = intensity), 
    size = 1) + facet_wrap(~red_ox, scales = "free") + spectra_legend + 
    legend_labs

ggplot(df_ref_4 %>% filter(rep == 1 & red_ox == "PCA_PCN"), aes(x = Wavelength)) + 
    geom_path(aes(y = intensity_phz1_red), color = "red", linetype = 1, 
        size = 1) + geom_path(aes(y = intensity_phz1_ox), color = "red", 
    linetype = 2, size = 1) + geom_path(aes(y = intensity_phz2_ox), 
    color = "blue", linetype = 1, size = 1) + geom_path(aes(y = intensity_phz2_red), 
    color = "blue", linetype = 2, size = 1) + geom_path(aes(y = intensity), 
    size = 1) + facet_wrap(~red_ox, scales = "free") + spectra_legend + 
    legend_labs

Quantifying and predicting rxns

Now that it doesn’t matter which point values we take from the kinetic run, let’s just average the kinetic run values for each well and take a look:

df_per_rep <- df_kin %>% group_by(wavelength, red, ox, red_ox, 
    rep) %>% summarise(int_per_rep = mean(intensity, na.rm = T), 
    std_per_rep = sd(intensity, na.rm = T)) %>% mutate(std_per_rep = ifelse(is.na(std_per_rep), 
    0, std_per_rep))


ggplot(df_per_rep, aes(x = red_ox, y = int_per_rep)) + geom_pointrange(aes(ymin = int_per_rep - 
    2 * std_per_rep, ymax = int_per_rep + 2 * std_per_rep)) + 
    facet_wrap(~wavelength, scales = "free")

Ok, hard to read, but the point is there are different values and the error bars are small, because the kinetic reads were pretty much all the same. Note that error bars are 2 standard deviations aka approximately a 95% confidence interval. Now let’s average the triplicates from each condition:

df_per_cond <- df_per_rep %>% group_by(wavelength, red, ox, red_ox) %>% 
    summarise(int_per_cond = mean(int_per_rep), std_per_cond = sd(int_per_rep)) %>% 
    mutate(std_per_cond = ifelse(is.na(std_per_cond), 0, std_per_cond))


ggplot(df_per_cond, aes(x = red_ox, y = int_per_cond)) + geom_hline(data = df_per_cond %>% 
    filter(red_ox == "PBS_PBS"), aes(yintercept = int_per_cond), 
    color = "gray") + geom_pointrange(aes(ymin = int_per_cond - 
    2 * std_per_cond, ymax = int_per_cond + 2 * std_per_cond)) + 
    facet_wrap(~wavelength, scales = "free") + ylim(0, NA)

Ok, here you can see that error bars are a little bigger to accomodate the triplicate wells. I have also plotted in a gray line the value of the PBS only control. This gives us a good idea of what the background reading is without any phenazine. Let’s go ahead and subtract out that background - this is equivalent to blanking a spec etc.

df_bg_sub <- df_per_cond %>% group_by(wavelength) %>% mutate(bg = ifelse(red_ox == 
    "PBS_PBS", int_per_cond, NA)) %>% mutate(bg_sub = int_per_cond - 
    mean(bg, na.rm = T))

ggplot(df_bg_sub, aes(x = red_ox, y = bg_sub)) + geom_hline(data = df_bg_sub %>% 
    filter(red_ox == "PBS_PBS"), aes(yintercept = bg_sub), color = "gray") + 
    geom_pointrange(aes(ymin = bg_sub - 2 * std_per_cond, ymax = bg_sub + 
        2 * std_per_cond)) + facet_wrap(~wavelength, scales = "free")  # + ylim(0,NA)

Beautiful, now these should be the intensities of the actual phenazines we were trying to measure.

Now what I’m going to do is setup the data so that we can actually predict the initial and final intensities for each well. For example, the \(PCA_{red} + PYO_{ox}\) condition should start with the abs690 of \(690_{PCA_{red}} + 690_{PYO_{ox}}\) and if the redox reaction proceeds the final values should be \(690_{PCA_{ox}} + 690_{PYO_{red}}\). Now you can look at the predictions vs. the experimental values for each of the conditions and channels:

bg_sub_refs <- df_bg_sub %>% filter(str_detect(red_ox, "PBS")) %>% 
    filter(red_ox != "PBS_PBS")

bg_sub_phz1_red <- left_join(df_bg_sub %>% filter(!str_detect(red_ox, 
    "PBS")), bg_sub_refs, by = c("wavelength", "red"), suffix = c("", 
    "_from_phz1_red"))

bg_sub_phz1_ox <- left_join(bg_sub_phz1_red, bg_sub_refs, by = c("wavelength", 
    red = "ox"), suffix = c("", "_from_phz1_ox"))

bg_sub_phz2_ox <- left_join(bg_sub_phz1_ox, bg_sub_refs, by = c("wavelength", 
    "ox"), suffix = c("", "_from_phz2_ox"))

bg_sub_phz2_red <- left_join(bg_sub_phz2_ox, bg_sub_refs, by = c("wavelength", 
    ox = "red"), suffix = c("", "_from_phz2_red"))

bg_sub_redox <- bg_sub_phz2_red %>% mutate(pred_init = bg_sub_from_phz1_red + 
    bg_sub_from_phz2_ox, pred_final = bg_sub_from_phz1_ox + bg_sub_from_phz2_red)

# red_ox_labs =
# c('PCA[red]~'+\n'*PCN[ox]',''PCA[red]+\nPYO[ox]'',''PCN[red]+\nPCA[ox]'',''PCN[red]+\nPYO[ox]'',''PYO[red]+\nPCA[ox]'',
# ''PYO[red]+\nPCN[ox]'')

red_ox_labs = c("PCA_red+\nPCN_ox", "PCA_red+\nPYO_ox", "PCN_red+\nPCA_ox", 
    "PCN_red+\nPYO_ox", "PYO_red+\nPCA_ox", "PYO_red+\nPCN_ox")


# red_ox_labs = c(expression(PCA[red]~'+\n'PCN[ox]))

Note that the red dot is the initial prediction, blue dot is the final prediction, and the black pointrange (95% CI) is the experimental values.

ggplot(bg_sub_redox %>% filter(wavelength == "abs310"), aes(x = red_ox)) + 
    geom_point(aes(y = pred_init), color = "red") + geom_point(aes(y = pred_final), 
    color = "blue") + geom_pointrange(aes(y = bg_sub, ymin = bg_sub - 
    2 * std_per_cond, ymax = bg_sub + 2 * std_per_cond), shape = 21) + 
    facet_wrap(~wavelength, scales = "free") + scale_x_discrete(labels = red_ox_labs, 
    name = NULL) + theme(axis.text.x = element_text(size = 12)) + 
    labs(y = "Predicted intensity")

ggplot(bg_sub_redox %>% filter(wavelength == "abs360"), aes(x = red_ox)) + 
    geom_point(aes(y = pred_init), color = "red") + geom_point(aes(y = pred_final), 
    color = "blue") + geom_pointrange(aes(y = bg_sub, ymin = bg_sub - 
    2 * std_per_cond, ymax = bg_sub + 2 * std_per_cond), shape = 21) + 
    facet_wrap(~wavelength, scales = "free") + scale_x_discrete(labels = red_ox_labs, 
    name = NULL) + theme(axis.text.x = element_text(size = 12)) + 
    labs(y = "Predicted intensity")

ggplot(bg_sub_redox %>% filter(wavelength == "abs690"), aes(x = red_ox)) + 
    geom_point(aes(y = pred_init), color = "red") + geom_point(aes(y = pred_final), 
    color = "blue") + geom_pointrange(aes(y = bg_sub, ymin = bg_sub - 
    2 * std_per_cond, ymax = bg_sub + 2 * std_per_cond), shape = 21) + 
    facet_wrap(~wavelength, scales = "free") + scale_x_discrete(labels = red_ox_labs, 
    name = NULL) + theme(axis.text.x = element_text(size = 12)) + 
    labs(y = "Predicted intensity")

ggplot(bg_sub_redox %>% filter(wavelength == "abs500"), aes(x = red_ox)) + 
    geom_point(aes(y = pred_init), color = "red") + geom_point(aes(y = pred_final), 
    color = "blue") + geom_pointrange(aes(y = bg_sub, ymin = bg_sub - 
    2 * std_per_cond, ymax = bg_sub + 2 * std_per_cond), shape = 21) + 
    facet_wrap(~wavelength, scales = "free") + scale_x_discrete(labels = red_ox_labs, 
    name = NULL) + theme(axis.text.x = element_text(size = 12)) + 
    labs(y = "Predicted intensity")

ggplot(bg_sub_redox %>% filter(wavelength == "em460"), aes(x = red_ox)) + 
    geom_point(aes(y = pred_init), color = "red") + geom_point(aes(y = pred_final), 
    color = "blue") + geom_pointrange(aes(y = bg_sub, ymin = bg_sub - 
    2 * std_per_cond, ymax = bg_sub + 2 * std_per_cond), shape = 21) + 
    facet_wrap(~wavelength, scales = "free") + scale_x_discrete(labels = red_ox_labs, 
    name = NULL) + theme(axis.text.x = element_text(size = 12)) + 
    labs(y = "Predicted intensity")

ggplot(bg_sub_redox %>% filter(wavelength == "em528"), aes(x = red_ox)) + 
    geom_point(aes(y = pred_init), color = "red") + geom_point(aes(y = pred_final), 
    color = "blue") + geom_pointrange(aes(y = bg_sub, ymin = bg_sub - 
    2 * std_per_cond, ymax = bg_sub + 2 * std_per_cond), shape = 21) + 
    facet_wrap(~wavelength, scales = "free") + scale_x_discrete(labels = red_ox_labs, 
    name = NULL) + theme(axis.text.x = element_text(size = 12)) + 
    labs(y = "Predicted intensity")