Processes 2019, 7,97
for four combinations of bulk glucose, amino acid, nitrate, and taurocholate concentrations chosen
to mimic a healthy gut environment and three unhealthy nutrient environments (high amino acids,
high primary bile acids, high nitrate) experimentally correlated with C. difficile-associated dysbiosis
(Table 1). We deemed the actual concentrations used to be less important than the concentration trends
(e.g., decreasing glucose and increasing amino acids in the high amino acids case) since our goal was
to assess qualitatively the effects of nutrient levels on community behavior.
5. Conclusions
Clostridium difficile infection (CDI) is a common problem in hospital settings, with almost 500,000
CDI cases diagnosed within the U.S. annually in acute care facilities alone. CDI involves dysbiosis of
the commensal gut microbiota characterized by a significant reduction of butyrate-producing species,
e.g., Faecalibacterium prausnitzii, and a large increase in Proteobacteria, e.g., Escherichia coli, along with
uncontrolled propagation of C. difficile. Motivated by recent experimental studies demonstrating the
ability of C. difficile and commensal gut bacteria to form biofilms, we developed a multispecies biofilm
model with a minimal representation of the gut microbiota containing C. difficile and one species
each from the three dominant phyla (F. prausnitzii, E. coli, Bacteroides thetaiotaomicron). The model
was used to investigate possible metabolic determinants of CDI mediated through host–microbiota
perturbations, modeled as decreased carbohydrate levels and increased amino acid, primary bile acid,
and nitrate levels compared to the healthy gut. These nutrient perturbations were shown to mimic
microbiota changes characteristic of CDI, namely marked increases in C. difficile and E. coli abundances
and a sharp decrease in F. prausnitzii abundance. C. difficile propagation was strongly dependent on
cross-feeding of formate and succinate secreted by the commensal species, a prediction in agreement
with experimental studies and that provides possible targets for the development of novel therapeutic
strategies. While our model is a simplified representation of a complex disease process, the results
presented emphasized the importance of metabolic interactions between C. difficile and commensal
species in CDI progression.
Supplementary Materials: The following are available online at http://www.mdpi.com/2227-9717/7/2/97/s1.
Additional File 1. Model equations and description. Figure S1. Predicted cross-feeding of byproducts with C. difficile
removed from the community. Figure S2. Predicted species abundances at various nutrient concentrations.
Figure S3. Predicted multispecies biofilm dysbiosis resulting from host–microbiota perturbations in the
concentrations of amino acids and the primary bile acid taurocholate. Figure S4. Effect of removing individual
cross-feeding relationships on predicted species abundances. Figure S5. Effect of the biofilm length on predicted
species abundances for the healthy case. Figure S6. Predicted multispecies biofilm behavior under healthy nutrient
conditions for a 30 micron-thick biofilm. Figure S7. Predicted multispecies biofilm behavior under healthy nutrient
conditions for a 60 micron-thick biofilm. Figure S8. Differences between healthy case and bile acid dysbiosis case
internal pathway fluxes.
Author Contributions: P.P. and M.A.H. conceived of the study. P.P. and M.A.H. developed the model and model
solution method. P.P. and M.A.H. performed the simulations and analyzed the results. P.P. and M.A.H. prepared
the manuscript. All authors read and approved the final manuscript.
Funding: This research was partially funded by NIH (Award U01EB019416). This work was funded in part by a
Fellowship from the University of Massachusetts to P.P. as part of an NIH-funded Training Program (National
Research Service Award T32 GM108556). Funds from these grants were used to cover the costs to publish in open
access.
Conflicts of Interest: The authors declare no conflict of interest.
34
for four combinations of bulk glucose, amino acid, nitrate, and taurocholate concentrations chosen
to mimic a healthy gut environment and three unhealthy nutrient environments (high amino acids,
high primary bile acids, high nitrate) experimentally correlated with C. difficile-associated dysbiosis
(Table 1). We deemed the actual concentrations used to be less important than the concentration trends
(e.g., decreasing glucose and increasing amino acids in the high amino acids case) since our goal was
to assess qualitatively the effects of nutrient levels on community behavior.
5. Conclusions
Clostridium difficile infection (CDI) is a common problem in hospital settings, with almost 500,000
CDI cases diagnosed within the U.S. annually in acute care facilities alone. CDI involves dysbiosis of
the commensal gut microbiota characterized by a significant reduction of butyrate-producing species,
e.g., Faecalibacterium prausnitzii, and a large increase in Proteobacteria, e.g., Escherichia coli, along with
uncontrolled propagation of C. difficile. Motivated by recent experimental studies demonstrating the
ability of C. difficile and commensal gut bacteria to form biofilms, we developed a multispecies biofilm
model with a minimal representation of the gut microbiota containing C. difficile and one species
each from the three dominant phyla (F. prausnitzii, E. coli, Bacteroides thetaiotaomicron). The model
was used to investigate possible metabolic determinants of CDI mediated through host–microbiota
perturbations, modeled as decreased carbohydrate levels and increased amino acid, primary bile acid,
and nitrate levels compared to the healthy gut. These nutrient perturbations were shown to mimic
microbiota changes characteristic of CDI, namely marked increases in C. difficile and E. coli abundances
and a sharp decrease in F. prausnitzii abundance. C. difficile propagation was strongly dependent on
cross-feeding of formate and succinate secreted by the commensal species, a prediction in agreement
with experimental studies and that provides possible targets for the development of novel therapeutic
strategies. While our model is a simplified representation of a complex disease process, the results
presented emphasized the importance of metabolic interactions between C. difficile and commensal
species in CDI progression.
Supplementary Materials: The following are available online at http://www.mdpi.com/2227-9717/7/2/97/s1.
Additional File 1. Model equations and description. Figure S1. Predicted cross-feeding of byproducts with C. difficile
removed from the community. Figure S2. Predicted species abundances at various nutrient concentrations.
Figure S3. Predicted multispecies biofilm dysbiosis resulting from host–microbiota perturbations in the
concentrations of amino acids and the primary bile acid taurocholate. Figure S4. Effect of removing individual
cross-feeding relationships on predicted species abundances. Figure S5. Effect of the biofilm length on predicted
species abundances for the healthy case. Figure S6. Predicted multispecies biofilm behavior under healthy nutrient
conditions for a 30 micron-thick biofilm. Figure S7. Predicted multispecies biofilm behavior under healthy nutrient
conditions for a 60 micron-thick biofilm. Figure S8. Differences between healthy case and bile acid dysbiosis case
internal pathway fluxes.
Author Contributions: P.P. and M.A.H. conceived of the study. P.P. and M.A.H. developed the model and model
solution method. P.P. and M.A.H. performed the simulations and analyzed the results. P.P. and M.A.H. prepared
the manuscript. All authors read and approved the final manuscript.
Funding: This research was partially funded by NIH (Award U01EB019416). This work was funded in part by a
Fellowship from the University of Massachusetts to P.P. as part of an NIH-funded Training Program (National
Research Service Award T32 GM108556). Funds from these grants were used to cover the costs to publish in open
access.
Conflicts of Interest: The authors declare no conflict of interest.
34