Repo associated with Jenior et. al. (2017). Clostridium difficile Colonizes Alternative Nutrient Niches during Infection across Distinct Murine Gut Microbiomes. mSystems.
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Jenior_Modeling_mSystems_2017.Rmd
Jenior_Modeling_mSystems_2017.docx
Jenior_Modeling_mSystems_2017.md
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README.md

Clostridium difficile colonizes alternative nutrient niches during infection across distinct murine gut microbiomes

Abstract

Clostridium difficile is the largest single cause of hospital-acquired infection in the United States. A major risk factor for Clostridium difficile infection (CDI) is prior exposure to antibiotics, as they disrupt the gut bacterial community which protects from C. difficile colonization. Multiple antibiotic classes have been associated with CDI susceptibility; many leading to distinct community structures stemming from variation in bacterial targets of action. These microbiomes present separate metabolic challenges to C. difficile, therefore we hypothesized that the pathogen adapts its physiology to available nutrients within different gut environments. Utilizing an in vivo CDI model, we demonstrated C. difficile highly colonized ceca of mice pretreated with any of three antibiotics from distinct classes. Levels of C. difficile spore formation and toxin activity varied between animals based on the antibiotic administered. These physiologic processes in C. difficile are partially regulated by environmental nutrient concentrations. To investigate metabolic responses of the bacterium in vivo, we performed transcriptomic analysis of C. difficile from ceca of infected mice across pretreatments. This revealed heterogeneous expression in numerous catabolic pathways for diverse growth substrates. To assess which resources C. difficile exploited, we developed a genome-scale metabolic model with a transcriptomic-enabled metabolite scoring algorithm integrating network architecture. This platform identified nutrients C. difficile used preferentially between infections. These findings were validated through untargeted mass spectrometry of each microbiome. Our results supported the hypothesis that C. difficile inhabits alternative nutrient niches across cecal microbiomes with increased preference for nitrogen-containing carbon sources, particularly Stickland fermentation substrates and host-derived aminoglycans.

Importance

Infection by the bacterium Clostridium difficile causes an inflammatory diarrheal disease which can become life-threatening, and has grown to be the most prevalent nosocomial pathogen. Susceptibility to C. difficile infection is strongly associated with previous antibiotic treatment, that disrupts the gut microbiota and reduces its ability to prevent colonization. In this study we demonstrate that not only does C. difficile alter pathogenesis between hosts pretreated with separate classes of antibiotics, but also exploits different nutrient sources across these environments. Our metabolite importance calculation also provides a platform to study nutrient requirements of pathogens during the context of infection. Our results suggest that C. difficile colonization resistance is mediated by multiple groups of bacteria competing for several subsets of nutrients, and could explain why total reintroduction of competitors through fecal microbial transplant is the most effective treatment to date. This work could ultimately contribute to the identification of targeted measures that prevent or reduce C. difficile colonization including pre- and probiotic therapies.

Overview

|- README          # Overview of all content
|
|- LICENSE         # Copyright information
|
|- Jenior_Modeling_mSystems_2017.Rmd 	   # executable Rmarkdown for this study
|
|- Jenior_Modeling_mSystems_2017.md 	   # Markdown version of manuscript
|
|- manuscript_format.docx 	   # Text tyle-formatting file used for generated docx
|
|- mSystems.csl 	   # Journal-specific formatting csl file
|
|- references.bib 	   # Bibtex formatted refereces cited in manuscript
|
|- protocols/            # Wet lab and dry lab step-by-step protocols for this study
|
|- data            # Raw and primary data
|  |- README.md         # More specific overview of subdirectory
|  |- metadata.tsv         # Metadata for mouse experimental groups
|  |- kegg/  # Reference files used in analysis
|  |- mapping/         # Normalized read counts mapping to *C. difficile* genes
|  |- metabolic models/         # Output from bigSMALL analysis
|  +- wetlab_assays/     # Raw data from wet lab experiments
|
|- code/           # Data analysis scripts
|  |- README.md         # More specific overview of subdirectory
|  |- R/           # R scipts for figures and analysis
|      |- figures/      # Code to generatre main body figures
|      |- supplement/      # Code to generatre supplementary figures
|      |- support/      # Code to perform additional tasks needed for analysis
|  |- python/      # python scripts
|  +- pbs/         # pbs scripts
|
|- results         # All output from workflows and analyses
|  |- README.md         # More specific overview of subdirectory
|  |- tables/      # Text version of tables (main body)
|  |- figures/     # Manuscript figures (main body)
|  +- supplement/  # Supplementary data presentation
|      |- figures/     # Supplementary figures
|      +- tables/      # Excel versions of supplementary tables

Running the analysis

# Clone bigSMALL to Desktop
git clone git@github.com:mjenior/bigsmall.git
git clone git@github.com:SchlossLab/Jenior_Modeling_mSystems_2017.git

# Download raw transcriptomic sequencing reads from the SRA (PRJNA354635) to data/fastqs/

qsub code/pbs/db_build.pbs
for transcriptome in cefoperazone_630 clindamycin_630 streptomycin_630 germfree
do
	bash code/pbs/transcriptome.bash transcriptome
done

python ~/Desktop/bigsmall/bigsmall.py data/metabolic_models/expression/streptomycin_630.RNA_reads2cdf630.norm.ko.pick.txt --iters 10000 --name streptomycin_630
python ~/Desktop/bigsmall/bigsmall.py data/metabolic_models/expression/cefoperazone_630.RNA_reads2cdf630.norm.ko.pick.txt --iters 10000 --name cefoperazone_630
python ~/Desktop/bigsmall/bigsmall.py data/metabolic_models/expression/clindamycin_630.RNA_reads2cdf630.norm.ko.pick.txt --iters 10000 --name clindamycin_630
python ~/Desktop/bigsmall/bigsmall.py data/metabolic_models/expression/germfree.RNA_reads2cdf630.norm.ko.pick.txt --iters 10000 --name germfree_630

Rscript code/R/support/subsample.R
Rscript code/R/figures/fig_1.R
Rscript code/R/figures/fig_2.R
Rscript code/R/figures/fig_3.R
Rscript code/R/figures/fig_4.R
Rscript code/R/figures/fig_5.R
Rscript code/R/supplement/fig_S1.R
Rscript code/R/supplement/fig_S2.R
Rscript code/R/supplement/fig_S3.R
Rscript code/R/supplement/fig_S4.R
Rscript code/R/supplement/fig_S5.R