- Background
- Bioinformatics
- Data analysis and statistics in R
- Load in the data and libraries needed in the analyses
- Figure 1 - crAssphage and ARG dynamics in human feacal metagenomes
- Figure 2 - Industrially polluted sediment is a hotspot for ARG selection
- Figure 3 - ARG abundance is largely explained by fecal matter, not selection
- Predicting antibiotic resistance gene abundance with crAssphage
- Figure 4 - Antibiotic resistance gene dynamics in waste water treatment plants
- Estimated resistance risk correlates with fecal pollution
- Supplementary figures
- References
Supplementary data analysis for the paper:
Karkman, A., Pärnänen, K., Larsson, DGJ. 2019. Fecal pollution explains antibiotic resistance gene abundances in anthropogenically impacted environments. Nature Communications 10:80. DOI: 10.1038/s41467-018-07992-3
Discharge of treated sewage introduces antibiotic resistance genes (ARGs) and resistant bacteria (ARBs) to the environment (Karkman et al. 2018). It has been speculated that the resistance determinants could be selected and/or disseminated to other bacteria in the downstream environments due to the antibiotcs and other selective agents released in the effluents (Rizzo et al. 2013; Guo et al. 2017). However, the increased abundance of ARGs in downstream environments could as well be explained by the amount of fecal pollution without any large scale selection and/or dissemination of the genes.
Recently a human feacal phage, crAssphage, was discovered from human fecal metagenomes (Dutilh et al. 2014) and was shown to infect Bacteroides intestinalis (Shkoporov et al. 2018). It has been shown to be very abundant in human fecal material and mostly specific to humans (García-Aljaro et al. 2017). Due to these facts, it has already been used as fecal marker in various studies (Stachler et al. 2018; Ahmed, Zhang, et al. 2018; Stachler and Bibby 2014; Stachler et al. 2017; Ahmed, Lobos, et al. 2018). Another fecal Bacteroides phage, ɸB124-14, has also been used for microbial source tracking. Unlike crAssphage, it should be abundant in porcine and bovine guts as well (Ogilvie et al. 2017). However, we did not find it to perform as well as crAssphage and it won't be included in the analyses.
In this study we show that in most of the the studied environments the abundance of ARGs, intI1 integrase gene and mobile genetic elements correlates well with fecal pollution levels with no evident signs of selection or dissemination of the resistance genes. The only exception being sediments polluted with wastewater from drug manufacturing containing exceptionally high levels of antibiotics (Bengtsson-Palme et al. 2014; Kristiansson et al. 2011).
This supplementary data analysis file is meant to document the analyses. For more detailed information about the background, methods, results and conclusions we of course recommend the original article.
The bioinformartics part shows only example commands and is not meant to be run as such. The results from the bioinformatics part are available in the data folder and will be used in the data analysis part with R.
All metagenomic samples were downloaded from public repositories as described in the methods.
The crAssphage (NC_024711.1) and ɸB124-14 (HE608841.1) genomes were downloaded from GenBank as fasta files.
The phage genomes were indexed using bowtie2-build
bowtie2-build phage_genome.fasta phage_genomeAfter indexing the phage genomes each sample was mapped against the genomes. The average genome coverage was used as a proxy for phage abundance. Paired-end reads:
bowtie2 -x phage_genome -1 SampleX_R1_reads.fastq.gz -2 SampleX_R2_reads.fastq.gz -S SampleX.sam
samtools view -Sb -f 2 SampleX.sam > SampleX.bam
samtools sort SampleX.bam -o SampleX_sort.bam
samtools index SampleX_sort.bam
export GEN_COV=$(samtools depth -a SampleX_sort.bam |\
awk '{ sum += $3; n++ } END { if (n > 0) print sum / n; }')
echo 'SampleX\t'$GEN_COVSingle reads:
bowtie2 -x phage_genome -U SampleY.fastq -S SampleY.sam
samtools view -Sb -q 10 SampleY.sam > SampleY.bam
samtools sort SampleY.bam -o SampleY_sort.bam
samtools index SampleY_sort.bam
export GEN_COV=$(samtools depth -a SampleY_sort.bam |\
awk '{ sum += $3; n++ } END { if (n > 0) print sum / n; }')
echo 'SampleY\t'$GEN_COV The fastq files were converted to fasta. The sample name was added to each sequence header before concatenating all fasta files to one file for annotation with DIAMOND against the ARG and MGE databases.
The count tables are generated from the DIAMOND outputs using custom scripts. The scripts for single (parse_diamond.py) and paired-end reads (parse_diamondPE.py) can be downloaded from my other Github repository.
Paired-end reads:
diamond blastx -d ResFinder -q SampleX_R1.fasta --max-target-seqs 1 -o SampleX_R1_res.txt \
-f 6 --id 90 --min-orf 20 -p 24 --masking 0
diamond blastx -d ResFinder -q SampleX_R2.fasta --max-target-seqs 1 -o SampleX_R2_res.txt \
-f 6 --id 90 --min-orf 20 -p 24 --masking 0
python parse_diamondPE.py -1 SampleX_R1_res.txt -2 SampleX_R2_res.txt -o SampleX_ResFinder.csvSingle reads:
diamond blastx -d ResFinder -q SampleY.fasta --max-target-seqs 1 -o SampleY_res.txt \
-f 6 --id 90 --min-orf 20 -p 24 --masking 0
python parse_diamondPE.py -i SampleY_res.txt -o SampleY_ResFinder.csvTo bp count in each metagenome was used for normalization.
The results from mapping against crAssphage and the gene annotations were imported to R and combined in data frames. Resulting data frames for each part of the study can be found from the datafolder.
Packages tidyverse, vegan, grid and gridExtra are needed for the analyses
(they can be installed with install.packages function).
In here the results are read to R and the colors used in the figures are defined.
library(tidyverse)
library(vegan)
library(grid)
library(gridExtra)
cols <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
HMP <- read.table("data/HMP.txt")
crass_impact <- read.table("data/crass_impact.txt")
MG_RAST <- read.table("data/MG-RAST.txt")
crass_wwtp <- read.table("data/crass_wwtp.txt")
res_risk <- read.table("data/res_risk.txt")The first figure shows the correlation between crAssphage and ARGs and intI1 integrase gene in human fecal metagenomes in different populations.
par(fig=c(0,0.45,0,0.8), new=TRUE)
plot(log10(rel_res)~log10(rel_crAss), data=HMP, bg=cols[as.factor(HMP$country)], pch=21,
ylab = "Normalized ARG abundance (log10)",
xlab="Normalized crAssphage abundance (log10)", cex=2, ylim=c(2.5, 4.5))
par(fig=c(0,0.45,0.5,1), new=TRUE)
boxplot(log10(rel_crAss)~country, data=HMP, horizontal=TRUE, col=cols, axes=F)
axis(2, at=1:3, labels=c("China", "Europe", "US"), las=1)
title("A", adj = 0, line = 0)
par(fig=c(0.45,0.9,0,0.8), new=TRUE)
tmp <- subset(HMP, rel_int>0)
plot(log10(rel_res)~log10(rel_int), data=tmp, bg=cols[as.factor(tmp$country)], pch=21,
ylab = "", xlab="Normalized intI1 abundance (log10)", cex=2, ylim=c(2.5, 4.5))
par(fig=c(0.45,0.9,0.5,1), new=TRUE)
boxplot(log10(rel_int)~country, data=tmp, horizontal=TRUE, col=cols, axes=F)
axis(2, at=1:3, labels=c("China", "Europe", "US"), las=1)
title("B", adj = 0, line = 0)
par(fig=c(0.8,1,0,0.8),new=TRUE)
boxplot(log10(rel_res)~country, data=HMP, col=cols, axes=F)
axis(1, at=1:3, labels=c("China", "Europe", "US"), las=3)
Figure 1. Abundance of antibiotic resistance genes, intI1 gene and crAssphage in human fecal metagenomes.
The regression model between ARGs and crAssphage shows no significant correlation.
crass_mod <- lm(log10(rel_res)~country+log10(rel_crAss), data=HMP)
summary(crass_mod)##
## Call:
## lm(formula = log10(rel_res) ~ country + log10(rel_crAss), data = HMP)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.82305 -0.13730 0.02065 0.15526 0.92042
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.832371 0.036734 104.328 < 2e-16 ***
## countryEuropean -0.331988 0.041913 -7.921 5.47e-14 ***
## countryUS -0.363815 0.044071 -8.255 5.91e-15 ***
## log10(rel_crAss) 0.003295 0.007420 0.444 0.657
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2447 on 282 degrees of freedom
## (163 observations deleted due to missingness)
## Multiple R-squared: 0.2134, Adjusted R-squared: 0.2051
## F-statistic: 25.51 on 3 and 282 DF, p-value: 1.25e-14
However, for ARGs and intI1 integrase gene the correlation was significant.
int_mod <- lm(log10(rel_res)~country+log10(rel_int), data=HMP)
summary(int_mod)##
## Call:
## lm(formula = log10(rel_res) ~ country + log10(rel_int), data = HMP)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.74900 -0.13778 -0.00554 0.12871 0.73835
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.78587 0.03091 122.496 < 2e-16 ***
## countryEuropean -0.21379 0.04025 -5.311 4.15e-07 ***
## countryUS -0.27632 0.05666 -4.876 2.88e-06 ***
## log10(rel_int) 0.06083 0.02896 2.100 0.0375 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2148 on 141 degrees of freedom
## (304 observations deleted due to missingness)
## Multiple R-squared: 0.3042, Adjusted R-squared: 0.2894
## F-statistic: 20.55 on 3 and 141 DF, p-value: 4.174e-11
Figure 2 shows the correlation between crAssphage and ARGs in impacted environments. The only environenmt where selection possibly is happening are the heavily polluted Indian sediments having therapeutic concemntrations of antibioitcs.
ggplot(crass_impact, aes(x=rel_crAss, y=rel_res, color=country)) +
geom_smooth(method="lm") +
geom_point(aes(shape=crAss_detection), size=5) +
scale_x_log10() +
scale_y_log10() +
theme_classic() +
labs(y = "Normalized ARG abundance", x="Normalized crAssphage abundance",
color="Study", shape="crAssphage detection") + scale_colour_manual(values=cols)
Figure 2. Correlation between ARG abundance and crAssphage abundance in environments with 642 pollution from WWTPs, hospitals or drug manufacturing.
The regression model for ARGs and crAssphage with different intercepts for different studies.
impact_mod <- lm(log10(rel_res)~country+log10(rel_crAss), data=crass_impact)
summary(impact_mod)##
## Call:
## lm(formula = log10(rel_res) ~ country + log10(rel_crAss), data = crass_impact)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.38795 -0.22124 0.02787 0.24639 1.15404
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.99570 0.21579 18.517 < 2e-16 ***
## countryIndia polluted 2.38322 0.23584 10.105 2.10e-14 ***
## countrySingapore -0.93755 0.22206 -4.222 8.64e-05 ***
## countrySpain -0.76029 0.20643 -3.683 0.000508 ***
## countryUK -0.21895 0.23083 -0.949 0.346778
## countryUS -1.41900 0.22595 -6.280 4.68e-08 ***
## log10(rel_crAss) 0.57113 0.05509 10.368 8.01e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4702 on 58 degrees of freedom
## Multiple R-squared: 0.8215, Adjusted R-squared: 0.803
## F-statistic: 44.48 on 6 and 58 DF, p-value: < 2.2e-16
In figure 3 the link between fecal pollution and ARG abundance was studied in MG-RAST metagenomes. Only part of the metagenomes were from human impacted environemnts, so all samples where crAssphage was not detected can be removed. Also samples that were the only representative from the environemnt where they came where removed. The annotations in the resulting samples were manually curated ands revised.
MG_RAST_crass <- subset(MG_RAST, crAss!="NA")
MG_RAST_crass <- MG_RAST_crass[MG_RAST_crass$feature %in%
levels(MG_RAST_crass$feature)[table(MG_RAST_crass$feature)>2],]
MG_RAST_crass$revised_source <- "WWTP"
MG_RAST_crass[MG_RAST_crass$project_id=="mgp9679",]$revised_source <- "High ammonia AS"
MG_RAST_crass[MG_RAST_crass$project_id=="mgp9798",]$revised_source <- "High ammonia AS"
MG_RAST_crass[MG_RAST_crass$project_id=="mgp6153",]$revised_source <- "Mouse gut"
MG_RAST_crass[MG_RAST_crass$project_id=="mgp6698",]$revised_source <- "Mouse gut"
MG_RAST_crass[MG_RAST_crass$project_id=="mgp3907",]$revised_source <- "Mouse gut"
MG_RAST_crass[MG_RAST_crass$project_id=="mgp3190",]$revised_source <- "River water"
MG_RAST_crass[MG_RAST_crass$project_id=="mgp3756",]$revised_source <- "Beijing air"
ggplot(MG_RAST_crass, aes(x=rel_crAss, y=rel_res, color=revised_source)) +
geom_point(size=5) +
scale_x_log10() +
scale_y_log10() +
geom_smooth(method="lm") +
theme_classic() +
labs(y = "Normalized ARG abundance", x="Normalized crAssphage abundance",
color = "Revised source") + scale_colour_manual(values=cols)
Figure 3. The correlation between crAssphage abundance and total ARG abundance in MG-RAST 651 metagenomes where crAssphage was detected.
The results from the impacted environemnts were used to build a regression model and that model was used to predict the ARG abundance using the crAssphage abundance. For the figure, see under Supplementary figures.
crass_df <- data.frame(crass=log10(crass_impact$rel_crAss), res=log10(crass_impact$rel_res))
crass_mod <- lm(res~crass, data=crass_df)
pos_crass <- subset(MG_RAST_crass, revised_source=="River water" |
revised_source == "Beijing air" | revised_source == "WWTP" )
pos_crass <- data.frame(crass=log10(pos_crass$rel_crAss), res=log10(pos_crass$rel_res),
sample=row.names(pos_crass), revised_source=pos_crass$revised_source)
pos_crass$predicted <- predict(crass_mod, pos_crass)
pred_mod <- (lm(res~predicted, data=pos_crass))
summary(pred_mod)##
## Call:
## lm(formula = res ~ predicted, data = pos_crass)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4272 -0.4254 0.1773 0.4796 0.9121
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -11.7612 2.3693 -4.964 3.06e-05 ***
## predicted 5.4630 0.9267 5.895 2.43e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.627 on 28 degrees of freedom
## Multiple R-squared: 0.5538, Adjusted R-squared: 0.5379
## F-statistic: 34.76 on 1 and 28 DF, p-value: 2.426e-06
ggplot(crass_wwtp, aes(rel_crAss, rel_res, color=country_wwtp)) +
geom_smooth(method="lm") +
geom_point(size=5) +
scale_x_log10() +
scale_y_log10() +
theme_classic() +
scale_colour_manual(values=cols) +
labs(y = "Normalized ARG abundance", x="Normalized crAssphage abundance",
color="Country:WWTP")
Figure 4. ARG and crAssphage abundance in two US and three Swedish waste water treatment 658 plants showing similar correlation with different base level of resistance.
The correlation was significant and similar in both countries. Only the intercept differed.
wwtp_mod <- lm(log10(rel_res)~country+log10(rel_crAss), data=crass_wwtp)
summary(wwtp_mod)##
## Call:
## lm(formula = log10(rel_res) ~ country + log10(rel_crAss), data = crass_wwtp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9168 -0.1962 0.0885 0.2079 0.5715
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.25786 0.06297 51.739 < 2e-16 ***
## countryUS -0.49316 0.08295 -5.945 4.45e-08 ***
## log10(rel_crAss) 0.70270 0.05939 11.832 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2979 on 96 degrees of freedom
## Multiple R-squared: 0.5975, Adjusted R-squared: 0.5891
## F-statistic: 71.26 on 2 and 96 DF, p-value: < 2.2e-16
The resistance risk values were taken from the original publication (see main article) and ARG and crAssphage abundances were measured in this publication as described earlier.
The results show that due to the one outlier (hospital effluent) with higher ARG abundance than would be estimated from the crAssphage abundance the regression is not significant.
The regression model between the ARG abundance and resistance risk is significant revealing the main driver behind the resistasnce risk calculations. With the resistance risk approach the hospital effluent, a possible hotspot for ARGs, would not have been spotted. For the figures, see under Supplementary figures.
risk_mod_crAss <- lm(ResRisk~log10(rel_crAss), data=res_risk)
summary(risk_mod_crAss)##
## Call:
## lm(formula = ResRisk ~ log10(rel_crAss), data = res_risk)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.2531 -4.0653 -0.8415 5.7602 13.5574
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 34.553 3.046 11.345 9.26e-06 ***
## log10(rel_crAss) 4.729 2.851 1.659 0.141
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.087 on 7 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.2822, Adjusted R-squared: 0.1796
## F-statistic: 2.752 on 1 and 7 DF, p-value: 0.1411
risk_mod_res <- lm(ResRisk~log10(rel_res), data=res_risk)
summary(risk_mod_res)##
## Call:
## lm(formula = ResRisk ~ log10(rel_res), data = res_risk)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.5922 -2.6137 -0.5924 3.5710 5.0694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.087 5.375 -0.946 0.363
## log10(rel_res) 10.163 1.537 6.613 2.49e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.802 on 12 degrees of freedom
## Multiple R-squared: 0.7847, Adjusted R-squared: 0.7667
## F-statistic: 43.73 on 1 and 12 DF, p-value: 2.492e-05
The supplementary figures are not described in detail. Only the data and codes are provided below.
A function for shared legend in multi panel plots copied from here.
grid_arrange_shared_legend <- function(..., ncol = length(list(...)), nrow = 1,
position = c("bottom", "right")) {
require(gridExtra)
require(grid)
plots <- list(...)
position <- match.arg(position)
g <- ggplotGrob(plots[[1]] + theme(legend.position = position))$grobs
legend <- g[[which(sapply(g, function(x) x$name) == "guide-box")]]
lheight <- sum(legend$height)
lwidth <- sum(legend$width)
gl <- lapply(plots, function(x) x + theme(legend.position="none"))
gl <- c(gl, ncol = ncol, nrow = nrow)
combined <- switch(position,
"bottom" = arrangeGrob(do.call(arrangeGrob, gl),
legend,
ncol = 1,
heights = unit.c(unit(1, "npc") - lheight, lheight)),
"right" = arrangeGrob(do.call(arrangeGrob, gl),
legend,
ncol = 2,
widths = unit.c(unit(1, "npc") - lwidth, lwidth)))
grid.newpage()
grid.draw(combined)
# return gtable invisibly
invisible(combined)
}The ARG categories are based on the ResFinder annotations.
crass_categ <- read.table("data/crAss_categ.txt")
# Tetracycline
p1 <- ggplot(crass_categ, aes(rel_crAss, rel_tet, color=country)) +
geom_smooth(method="lm") +
geom_point(aes(shape=crAss_detection), size=5) +
scale_x_log10() + scale_y_log10() +
labs(y = "Normalized ARG abundance", x="Normalized crAssphage abundance",
title="Tetracycline", shape="crAssphage detection") +
theme_classic()
# Aminoglycoside
p2 <- ggplot(crass_categ, aes(rel_crAss, rel_amino, color=country)) +
geom_smooth(method="lm") +
geom_point(aes(shape=crAss_detection), size=5) +
scale_x_log10() + scale_y_log10() +
labs(y = "Normalized ARG abundance", x="Normalized crAssphage abundance",
title="Aminoglycoside", shape="crAssphage detection") +
theme_classic()
# MLSB
p3 <- ggplot(crass_categ, aes(rel_crAss, rel_mls, color=country)) +
geom_smooth(method="lm") + geom_point(aes(shape=crAss_detection), size=5) +
scale_x_log10() + scale_y_log10() +
labs(y = "Normalized ARG abundance", x="Normalized crAssphage abundance",
title="MLSB", shape="crAssphage detection") +
theme_classic()
# Beta_lactam
p4 <- ggplot(crass_categ, aes(rel_crAss, rel_beta, color=country)) +
geom_smooth(method="lm") + geom_point(aes(shape=crAss_detection), size=5) +
scale_x_log10() + scale_y_log10() +
labs(y = "Normalized ARG abundance", x="Normalized crAssphage abundance",
title="Beta-lactam", shape="crAssphage detection") +
theme_classic()
# Trimethoprim
p5 <- ggplot(crass_categ, aes(rel_crAss, rel_tri, color=country)) +
geom_smooth(method="lm") + geom_point(aes(shape=crAss_detection), size=5) +
scale_x_log10() + scale_y_log10() +
labs(y = "Normalized ARG abundance", x="Normalized crAssphage abundance",
title="Trimethoprim", shape="crAssphage detection") +
theme_classic()
# Sulphonamide
p6 <- ggplot(crass_categ, aes(rel_crAss, rel_sul, color=country)) +
geom_smooth(method="lm") + geom_point(aes(shape=crAss_detection), size=5) +
scale_x_log10() + scale_y_log10() +
labs(y = "Normalized ARG abundance", x="Normalized crAssphage abundance",
title="Sulphonamide", shape="crAssphage detection") +
theme_classic()
# Vancomycin
p7 <- ggplot(crass_categ, aes(rel_crAss, rel_van, color=country)) +
geom_smooth(method="lm") + geom_point(aes(shape=crAss_detection), size=5) +
scale_x_log10() + scale_y_log10() +
labs(y = "Normalized ARG abundance", x="Normalized crAssphage abundance",
title="Vancomycin", shape="crAssphage detection") +
theme_classic()
# Chloramphenicol
p8 <- ggplot(crass_categ, aes(rel_crAss, rel_clo, color=country)) +
geom_smooth(method="lm") + geom_point(aes(shape=crAss_detection), size=5) +
scale_x_log10() + scale_y_log10() +
labs(y = "Normalized ARG abundance", x="Normalized crAssphage abundance",
title="Chloramphenicol", shape="crAssphage detection") +
theme_classic()
# Quinolone
p9 <- ggplot(crass_categ, aes(rel_crAss, rel_qui, color=country)) +
geom_smooth(method="lm") + geom_point(aes(shape=crAss_detection), size=5) +
scale_x_log10() + scale_y_log10() +
labs(y = "Normalized ARG abundance", x="Normalized crAssphage abundance",
title="Quinolone", shape="crAssphage detection") +
theme_classic()
grid_arrange_shared_legend(p1, p2, p3, p4, p5, p6, p7, p8, p9, ncol=3, nrow=3)
p1 <- ggplot(crass_impact, aes(x=rel_crAss, y=rel_res, color=country)) +
geom_smooth(method="lm") +
geom_point(aes(shape=crAss_detection), size=5) +
scale_x_log10() +
scale_y_log10() +
theme_classic() +
labs(y = "Normalized ARG abundance", x="Normalized crAssphage abundance",
color="Study", shape="crAssphage detection")
p2 <- ggplot(crass_impact, aes(x=rel_crAss, y=rel_rich, color=country)) +
geom_smooth(method="lm") +
geom_point(aes(shape=crAss_detection), size=5) +
scale_x_log10() +
scale_y_log10() +
theme_classic() +
labs(y = "Normalized ARG richness", x="Normalized crAssphage abundance",
color="Study", shape="crAssphage detection")
p3 <- ggplot(crass_impact, aes(x=rel_crAss, y=rel_mge, color=country)) +
geom_smooth(method="lm") +
geom_point(aes(shape=crAss_detection), size=5) +
scale_x_log10() +
scale_y_log10() +
theme_classic() +
labs(y = "Normalized MGE abundance", x="Normalized crAssphage abundance",
color="Study", shape="crAssphage detection")
p4 <- ggplot(crass_impact, aes(x=rel_crAss, y=rel_int, color=country)) +
geom_smooth(method="lm") +
geom_point(aes(shape=crAss_detection), size=5) +
scale_x_log10() +
scale_y_log10() +
theme_classic() +
labs(y = "Normalized ARG abundance", x="Normalized crAssphage abundance",
color="Study", shape="crAssphage detection")
grid_arrange_shared_legend(p1, p2, p3, p4, ncol=2, nrow=2)
The most abundant ARGs were removed due to possible bias introduced by the whole genome amplification used in the study.
| Gene | ResFinder accessions |
|---|---|
| sul2 | sul2_1_AF542061, sul2_2_GQ421466, sul2_12_AF497970 |
| strA | strA_1_M96392, |
| strB | strB_1_M96392, |
| qnrD | QnrD_1_FJ228229, |
| msr(E) | msr(E)_4_EU294228 |
| qnrVC | QnrVC4_1_GQ891757 |
crass_reduced <- read.table("data/crass_reduced.txt")
ggplot(crass_reduced, aes(x=rel_crAss, y=rel_res, color=country)) +
geom_smooth(method="lm") +
geom_point(aes(shape=crAss_detection), size=5) +
scale_x_log10() +
scale_y_log10() +
theme_classic() +
labs(y = "Normalized ARG abundance", x="Normalized crAssphage abundance",
color="Study", shape="crAssphage detection")
MG_RAST_NocrAss <- MG_RAST[is.na(MG_RAST$crAss),]
MG_RAST_NocrAss <- subset(MG_RAST_NocrAss, feature %in%
names(table(MG_RAST_NocrAss$feature)[table(MG_RAST_NocrAss$feature)>2]))
ggplot(MG_RAST_NocrAss, aes(x=feature, y=rel_res)) +
geom_boxplot() +
theme_classic() +
theme(axis.text.x=element_text(angle=45, hjust=1, size=10)) +
labs(y = "Normalized ARG abundance", x="")
ggplot(pos_crass, aes(x=predicted, y=res, color=pos_crass$revised_source)) +
geom_point(size=5) + theme_classic() +
labs(x="Predicted ARG abundance", y="Measured ARG abundance", color="Revised source")
More detailed annotations for the WWTPs taken from the original publications. Note the slightly different samples annotations between US and SWE due to differences in the processes. Also the SWE WWTP sample annotations have been modified.
| Original annotation | Modified annotation |
|---|---|
| Primary/Surplus | Primary sludge |
| Primary | Primary sludge |
| Surplus | Sludge |
| Digested | Digested sludge |
| Inlet - ... | Raw Sewage |
| Treated | Treated sewage |
| Sand-filtered | Sand-filtered treated sewage |
us_wwtp <- read.table("data/us_wwtp.txt")
swe_wwtp <- read.table("data/swe_wwtp.txt")
p1 <- ggplot(us_wwtp, aes(rel_crAss, rel_res, shape=geo_loc_name)) +
geom_smooth(method="lm") +
geom_point(aes(color=Sample_loc), size=5) +
scale_x_log10() + scale_y_log10() +
labs(y = "Normalized ARG abundance", x="Normalized crAssphage abundance") +
theme_classic() +
guides(shape=guide_legend(title="WWTP"), color=guide_legend(title="Sample"))
p2 <- ggplot(swe_wwtp, aes(rel_crAss, rel_res, shape=WWTP)) +
geom_smooth(method="lm") +
geom_point(aes(color=Sample), size=5) +
scale_x_log10() + scale_y_log10() +
labs(y = "Normalized ARG abundance", x="Normalized crAssphage abundance") +
theme_classic() +
guides(shape=guide_legend(title="WWTP"), color=guide_legend(title="Sample"))
grid.arrange(p1, p2, ncol=2)
p1 <- ggplot(res_risk,aes(y=ResRisk,x=log10(rel_res),color=Environment)) +
geom_point(size=5) +
labs(y = "Resistance risk", x="Normalized ARG abundance") +
theme_classic()
p2 <- ggplot(res_risk,aes(y=ResRisk,x=log10(rel_crAss),color=Environment)) +
geom_point(size=5) +
labs(y = "Resistance risk", x="Normalized crAssphage abundance") +
theme_classic()
grid_arrange_shared_legend(p1,p2, ncol=2)
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