Notes

Panel A of figure 2 is isothermal titration calorimetry data that was analyzed outside of R.

Setup packages and plotting for the notebook:

Fig. 2B - WT Colony +/- DNase

Let’s read in the dnase dataset and look at the table:

Phenazine Strain Condition Replicate Material Day RT Area Channel Name Amount calcConc mean
PYO WT DNase 1 cells D4 5.952 78120 313.0nm 2.523 72.32600 71.055111
PCA WT DNase 1 cells D4 3.170 10966 364.0nm 0.698 20.00933 17.601333
PCN WT DNase 1 cells D4 8.799 283489 364.0nm 19.744 565.99467 567.934444
PYO WT DNase 1 agar D4 5.963 13770 313.0nm 0.445 1.42400 1.163733
PCA WT DNase 1 agar D4 3.066 42599 364.0nm 2.712 8.67840 7.740800
PCN WT DNase 1 agar D4 8.807 117981 364.0nm 8.217 26.29440 24.491733
PYO WT DNase 2 agar D4 5.967 9463 313.0nm 0.306 0.97920 NA
PCA WT DNase 2 agar D4 3.065 34587 364.0nm 2.202 7.04640 NA
PCN WT DNase 2 agar D4 8.812 100344 364.0nm 6.989 22.36480 NA
PYO WT DNase 3 agar D4 5.964 10533 313.0nm 0.340 1.08800 NA
PCA WT DNase 3 agar D4 3.105 36801 364.0nm 2.343 7.49760 NA
PCN WT DNase 3 agar D4 8.803 111355 364.0nm 7.755 24.81600 NA
PYO WT none 1 agar D4 5.971 15508 313.0nm 0.501 1.60320 1.349333
PCA WT none 1 agar D4 3.054 43066 364.0nm 2.742 8.77440 8.238933
PCN WT none 1 agar D4 8.815 75035 364.0nm 5.226 16.72320 22.126933
PYO WT none 2 agar D4 5.968 12382 313.0nm 0.400 1.28000 NA
PCA WT none 2 agar D4 3.060 40419 364.0nm 2.573 8.23360 NA
PCN WT none 2 agar D4 8.814 101441 364.0nm 7.065 22.60800 NA
PYO WT none 3 agar D4 5.964 11287 313.0nm 0.364 1.16480 NA
PCA WT none 3 agar D4 3.059 37847 364.0nm 2.409 7.70880 NA
PCN WT none 3 agar D4 8.811 121366 364.0nm 8.453 27.04960 NA
PYO WT DNase 2 cells D4 5.942 75064 313.0nm 2.424 69.48800 NA
PCA WT DNase 2 cells D4 3.132 9362 364.0nm 0.596 17.08533 NA
PCN WT DNase 2 cells D4 8.789 285936 364.0nm 19.915 570.89667 NA
PYO WT DNase 3 cells D4 5.949 77070 313.0nm 2.489 71.35133 NA
PCA WT DNase 3 cells D4 3.119 8611 364.0nm 0.548 15.70933 NA
PCN WT DNase 3 cells D4 8.797 283951 364.0nm 19.776 566.91200 NA
PYO WT none 1 cells D4 5.957 102908 313.0nm 3.323 95.25933 88.847556
PCA WT none 1 cells D4 3.113 13782 364.0nm 0.877 25.14067 20.324667
PCN WT none 1 cells D4 8.804 306236 364.0nm 21.328 611.40267 614.231111
PYO WT none 2 cells D4 5.946 98479 313.0nm 3.180 91.16000 NA
PCA WT none 2 cells D4 3.100 9417 364.0nm 0.599 17.17133 NA
PCN WT none 2 cells D4 8.792 301514 364.0nm 20.999 601.97133 NA
PYO WT none 3 cells D4 5.931 86539 313.0nm 2.795 80.12333 NA
PCA WT none 3 cells D4 3.092 10222 364.0nm 0.651 18.66200 NA
PCN WT none 3 cells D4 8.778 315202 364.0nm 21.953 629.31933 NA

Now, let’s look at an overview of the experiment.

And let’s do a statistical test to compare the DNase +/- treatments.

First, a t-test for whether or not agar with DNase concentrations were higher than the ‘none’ treatment:

## # A tibble: 3 x 5
## # Groups:   Material [1]
##   Material Phenazine conf_int_low conf_int_high p_value
##   <chr>    <chr>            <dbl>         <dbl>   <dbl>
## 1 agar     PCA             -1.79            Inf   0.778
## 2 agar     PCN             -5.71            Inf   0.261
## 3 agar     PYO             -0.585           Inf   0.811

There are no significant differences p<0.05.

Second, a t-test for whether or not biofilm (aka cell) with DNase concentrations were lower than the ‘none’ treatment

## # A tibble: 3 x 5
## # Groups:   Material [1]
##   Material Phenazine conf_int_low conf_int_high p_value
##   <chr>    <chr>            <dbl>         <dbl>   <dbl>
## 1 cells    PCA               -Inf          3.76  0.198 
## 2 cells    PCN               -Inf        -23.5   0.0127
## 3 cells    PYO               -Inf         -4.94  0.0273

There is a significant difference for PYO and PCN.

Here you can see that the agar concentrations between the Dnase treated and untreated don’t differ meaningfully, but the cell/biofilm concentrations might. This might be because for this experiment the colonies were transferred to a fresh agar plate for only 24hrs as opposed to staying on the same plate for 4 days as with the pel experiment. So, let’s ignore the agar concentrations for now. It’s also important to note that by calculating a ratio as we did above for pel we do risk amplifying meaningless differences by dividing large numbers by small numbers.

Here’s the biofilm only:

Fig. 2C - ∆pel colony

Let’s read in and look at the table of pel data.

measured_phenazine strain amount_added added_phenazine material replicate RT Area Channel Name Amount calcConc
PCA dPel NA NA agar 1 3.060 114063 364.0nm 7.487 23.95840
PCN dPel NA NA agar 1 8.858 303178 364.0nm 19.998 63.99360
PYO dPel NA NA agar 1 6.007 24774 313.0nm 0.762 2.43840
PCA dPel NA NA agar 2 3.056 99389 364.0nm 6.524 20.87680
PCN dPel NA NA agar 2 8.870 229828 364.0nm 15.160 48.51200
PYO dPel NA NA agar 2 6.014 23647 313.0nm 0.727 2.32640
PCA dPel NA NA agar 3 3.055 104913 364.0nm 6.887 22.03840
PCN dPel NA NA agar 3 8.861 217660 364.0nm 14.357 45.94240
PYO dPel NA NA agar 3 6.005 25757 313.0nm 0.792 2.53440
PCA dPel NA NA cells 1 3.181 8470 364.0nm 0.556 15.93867
PCN dPel NA NA cells 1 8.852 185244 364.0nm 12.219 350.27800
PYO dPel NA NA cells 1 6.009 113480 313.0nm 3.491 100.07533
PCA dPel NA NA cells 2 3.186 10494 364.0nm 0.689 19.75133
PCN dPel NA NA cells 2 8.850 179462 364.0nm 11.838 339.35600
PYO dPel NA NA cells 2 6.005 117862 313.0nm 3.625 103.91667
PCA dPel NA NA cells 3 3.197 11134 364.0nm 0.731 20.95533
PCN dPel NA NA cells 3 8.840 201283 364.0nm 13.277 380.60733
PYO dPel NA NA cells 3 5.993 124770 313.0nm 3.838 110.02267
PCA WTpar NA NA cells 1 3.234 10524 364.0nm 0.691 19.80867
PCN WTpar NA NA cells 1 8.840 227724 364.0nm 15.021 430.60200
PYO WTpar NA NA cells 1 5.995 86700 313.0nm 2.667 76.45400
PCA WTpar NA NA cells 2 3.222 11003 364.0nm 0.722 20.69733
PCN WTpar NA NA cells 2 8.851 206671 364.0nm 13.632 390.78400
PYO WTpar NA NA cells 2 6.005 84758 313.0nm 2.607 74.73400
PCA WTpar NA NA cells 3 3.178 5777 364.0nm 0.379 10.86467
PCN WTpar NA NA cells 3 8.840 220629 364.0nm 14.553 417.18600
PYO WTpar NA NA cells 3 5.995 88787 313.0nm 2.731 78.28867
PCA WTpar NA NA agar 1 3.056 124641 364.0nm 8.182 26.18240
PCN WTpar NA NA agar 1 8.855 393800 364.0nm 25.976 83.12320
PYO WTpar NA NA agar 1 6.008 25687 313.0nm 0.790 2.52800
PCA WTpar NA NA agar 2 3.043 135302 364.0nm 8.882 28.42240
PCN WTpar NA NA agar 2 8.861 435638 364.0nm 28.736 91.95520
PYO WTpar NA NA agar 2 6.009 27091 313.0nm 0.833 2.66560
PCA WTpar NA NA agar 3 3.040 122418 364.0nm 8.036 25.71520
PCN WTpar NA NA agar 3 8.854 387178 364.0nm 25.539 81.72480
PYO WTpar NA NA agar 3 6.003 24705 313.0nm 0.760 2.43200

Ok, now let’s plot an overview of the dataset.

You can see that for each phenazine the concentration differs between the strains for both the cells aka biofilm and the agar.

And let’s perform the t-test on whether or not ∆pel concentrations are different than WT concentrations.

## # A tibble: 6 x 5
## # Groups:   material [2]
##   material measured_phenazine conf_int_low conf_int_high p_value
##   <chr>    <chr>                     <dbl>         <dbl>   <dbl>
## 1 agar     PCA                      -7.90         -1.07  0.0219 
## 2 agar     PCN                     -52.8         -12.8   0.0131 
## 3 agar     PYO                      -0.362         0.144 0.297  
## 4 cells    PCA                      -9.60         13.1   0.650  
## 5 cells    PCN                    -103.           -8.85  0.0301 
## 6 cells    PYO                      17.2          39.2   0.00553

Both the agar and biofilm concentrations for PYO and PCN are statistically significantly different in ∆pel vs. WT strains.

Let’s calculate the retention ratio.

And let’s perform the t-test on whether or not ∆pel ratios are greater than WT ratios.

## # A tibble: 3 x 4
##   measured_phenazine conf_int_low conf_int_high  p_value
##   <chr>                     <dbl>         <dbl>    <dbl>
## 1 PCA                     -0.0872           Inf 0.101   
## 2 PCN                     -0.128            Inf 0.0561  
## 3 PYO                      9.45             Inf 0.000692

There is a statistically significant difference for PYO.

Fig. 2D - EtBr vs. PHZ in colonies

Let’s read in the data and calculate the concentrations.

And let’s plot:

Fig. 2E - WT eDNA with TOTO-1

Let’s read in the standards, biofilm and metadata:

wavelength well FluorInt read strain toto_added ctDNA_added well_std_conc bio_rep tech_rep
535 A1 55732 1 std TRUE TRUE 50.0000000 1 1
535 A2 57010 1 std TRUE TRUE 25.0000000 1 1
535 A3 52506 1 std TRUE TRUE 12.5000000 1 1
535 A4 43673 1 std TRUE TRUE 6.2500000 1 1
535 A5 29038 1 std TRUE TRUE 3.1250000 1 1
535 A6 19617 1 std TRUE TRUE 1.5625000 1 1
535 A7 11332 1 std TRUE TRUE 0.7812500 1 1
535 A8 5564 1 std TRUE TRUE 0.3906250 1 1
535 A9 2778 1 std TRUE TRUE 0.1953125 1 1
535 A10 1406 1 std TRUE TRUE 0.0976562 1 1
535 A11 682 1 std TRUE TRUE 0.0488281 1 1
535 A12 774 1 std TRUE TRUE 0.0244141 1 1
535 B1 12139 2 WT TRUE TRUE NA 1 1
535 B2 13446 2 WT TRUE TRUE NA 2 1
535 B3 11074 2 WT TRUE TRUE NA 3 1
535 B4 11370 2 WT TRUE TRUE NA 4 1
535 B5 12068 2 WT TRUE TRUE NA 5 1
535 B6 11855 2 WT TRUE TRUE NA 6 1
535 B7 10123 2 WT TRUE FALSE NA 1 2
535 B8 11827 2 WT TRUE FALSE NA 2 2
535 B9 9761 2 WT TRUE FALSE NA 3 2
535 B10 10182 2 WT TRUE FALSE NA 4 2
535 B11 9634 2 WT TRUE FALSE NA 5 2
535 B12 11566 2 WT TRUE FALSE NA 6 2
535 C1 10915 2 WT TRUE FALSE NA 1 3
535 C2 10894 2 WT TRUE FALSE NA 2 3
535 C3 9651 2 WT TRUE FALSE NA 3 3
535 C4 10147 2 WT TRUE FALSE NA 4 3
535 C5 11910 2 WT TRUE FALSE NA 5 3
535 C6 12036 2 WT TRUE FALSE NA 6 3
535 C7 130 2 WT FALSE FALSE NA 1 4
535 C8 190 2 WT FALSE FALSE NA 2 4
535 C9 119 2 WT FALSE FALSE NA 3 4
535 C10 124 2 WT FALSE FALSE NA 4 4
535 C11 144 2 WT FALSE FALSE NA 5 4
535 C12 138 2 WT FALSE FALSE NA 6 4
535 E1 23490 2 dPHZ TRUE TRUE NA 1 1
535 E2 22622 2 dPHZ TRUE TRUE NA 2 1
535 E3 24901 2 dPHZ TRUE TRUE NA 3 1
535 E4 14964 2 dPHZ TRUE TRUE NA 4 1
535 E5 19109 2 dPHZ TRUE TRUE NA 5 1
535 E6 14650 2 dPHZ TRUE TRUE NA 6 1
535 F1 22498 2 dPHZ TRUE FALSE NA 1 2
535 F2 13008 2 dPHZ TRUE FALSE NA 2 2
535 F3 15141 2 dPHZ TRUE FALSE NA 3 2
535 F4 18463 2 dPHZ TRUE FALSE NA 4 2
535 F5 14572 2 dPHZ TRUE FALSE NA 5 2
535 F6 17939 2 dPHZ TRUE FALSE NA 6 2
535 G1 25159 2 dPHZ TRUE FALSE NA 1 3
535 G2 12442 2 dPHZ TRUE FALSE NA 2 3
535 G3 14663 2 dPHZ TRUE FALSE NA 3 3
535 G4 15324 2 dPHZ TRUE FALSE NA 4 3
535 G5 13968 2 dPHZ TRUE FALSE NA 5 3
535 G6 16661 2 dPHZ TRUE FALSE NA 6 3
535 H1 235 2 dPHZ FALSE FALSE NA 1 4
535 H2 164 2 dPHZ FALSE FALSE NA 2 4
535 H3 500 2 dPHZ FALSE FALSE NA 3 4
535 H4 191 2 dPHZ FALSE FALSE NA 4 4
535 H5 208 2 dPHZ FALSE FALSE NA 5 4
535 H6 221 2 dPHZ FALSE FALSE NA 6 4

Now we can make the plot:

Create Figure

Let’s put everything together:

## 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       utf8_1.1.4       rlang_0.4.6      pillar_1.4.2    
## [13] glue_1.3.1       withr_2.1.2      DBI_1.0.0        dbplyr_1.4.2    
## [17] readxl_1.3.1     lifecycle_0.1.0  munsell_0.5.0    gtable_0.3.0    
## [21] cellranger_1.1.0 rvest_0.3.5      evaluate_0.14    labeling_0.3    
## [25] fansi_0.4.0      highr_0.8        Rcpp_1.0.2       scales_1.0.0    
## [29] backports_1.1.4  webshot_0.5.1    jsonlite_1.6     fs_1.3.1        
## [33] gridExtra_2.3    digest_0.6.21    stringi_1.4.3    grid_3.5.3      
## [37] cli_1.1.0        tools_3.5.3      magrittr_1.5     crayon_1.3.4    
## [41] pkgconfig_2.0.3  ellipsis_0.3.0   xml2_1.2.2       reprex_0.3.0    
## [45] assertthat_0.2.1 rmarkdown_1.13   httr_1.4.1       rstudioapi_0.10 
## [49] R6_2.4.0         nlme_3.1-137     compiler_3.5.3