Processes 2019, 7,97
host, we mimicked host-microbiota perturbations associated with CDI by varying nutrient levels
guided by experimental observations. More specifically, dysbiosis states were modeled through
changes in the concentrations of available glucose, amino acids [12,61–64], primary bile acids [16,77,81],
and nitrate [17].
Our model predicted that cross-feeding of secreted byproducts plays an important role in
C. difficile sublimation and expansion. C. difficile consumed formate synthesized by F. prausnitzii
and E. coli and succinate synthesized by B. thetaiotaomicron and F. prausnitzii. The existence of both
cross-feeding relationships is supported by the experimental literature [55,56]. In silico removal
of either cross-feeding relationship was predicted to provide C. difficile colonization resistance,
demonstrating the complexity and importance of cross-feeding networks even in this simplified
four-species community. These predictions could be tested experimentally through the development of
an in vitro model system of the four species. More importantly, these results suggest that therapeutic
strategies that target species–species interactions could be promising alternatives to conventional
antibiotics that target C. difficile directly.
Host–microbiota perturbations modeled as increases in glucose and decreases in amino acid
concentrations reproduced several features of C. difficile-associated dysbiosis including substantially
reduced F. prausnitzii and increased C. difficile abundances and an imbalance in SCFA synthesis
characterized by increased acetate and reduced butyrate levels [94]. The predicted decrease in
anti-inflammatory butyrate would be expected to exasperate dysbiosis and accelerate disease
progression [69,74]. Similar results were obtained when glucose and amino acid changes were replaced
by increases in the primary bile acid taurocholate, which was predicted to be used as an electron
acceptor by C. difficile in vivo to provide a growth advantage in the absence of commensal bacteria that
degrade primary bile acids to secondary bile acids [61,95,96]. Taurocholate availability was predicted
to have less effect on butyrate and propionate synthesis, but the SCFA imbalance remained due to high
acetate synthesis. Our model predicted that dysbiosis could be induced with moderate changes in
nutrient concentrations, a prediction that could be tested in vitro and suggesting the possible promise
of therapeutic strategies that aim to alter the gut nutritional environment.
Despite their many consistencies with experimental studies [12,97,98], our simulations with
glucose, amino acids, and taurocholate changes were unable to reproduce the large increase in
E. coli abundance observed during CDI [71,73]. The addition of host-derived nitrate [17,99]t ot h e
other nutrient changes rectified this inconsistency and reproduced the key microbiota signatures of
C. difficile-associated dysbiosis during CDI: large increases in C. difficile and E. coli abundances, large
decreases in health-promoting F. prausnitzii abundance, and moderate changes in B. thetaiotaomicron
abundance. The model generated high acetate levels associated with dysbiosis states, a prediction that
could be tested through in vitro experiments. We believe further development of our multispecies
biofilm model could yield a general computational platform for in silico investigation of CDI, other
gut infections, and chronic inflammation disorders such as inflammatory bowel and Crohn’s diseases.
Some possibilities include the modeling of C. difficile spore formation/germination, the inclusion of
more commensal gut species (e.g., [100]) including those from other phyla [101–103], the addition of
a broader array of gut nutrients including fibers, oligosaccharides, and fats resulting from realistic
diets [12–15,104], and modeling of the human host through incorporation of available metabolic
reconstructions such as Recon 2 or Recon 3D [105–107]. A possible drawback of our modeling approach
is the lack of species-specific parameters for nutrient uptake kinetics and metabolite-dependent mass
transfer coefficients.
4. Materials and Methods
4.1. Biofilm Model Formulation and Solution
The multispecies biofilm model was constructed by combining genome-scale metabolic
reconstructions of C. difficile (Strain 630∆erm) [88] and three commensal gut species: B. thetaiotaomicron [89],
30
host, we mimicked host-microbiota perturbations associated with CDI by varying nutrient levels
guided by experimental observations. More specifically, dysbiosis states were modeled through
changes in the concentrations of available glucose, amino acids [12,61–64], primary bile acids [16,77,81],
and nitrate [17].
Our model predicted that cross-feeding of secreted byproducts plays an important role in
C. difficile sublimation and expansion. C. difficile consumed formate synthesized by F. prausnitzii
and E. coli and succinate synthesized by B. thetaiotaomicron and F. prausnitzii. The existence of both
cross-feeding relationships is supported by the experimental literature [55,56]. In silico removal
of either cross-feeding relationship was predicted to provide C. difficile colonization resistance,
demonstrating the complexity and importance of cross-feeding networks even in this simplified
four-species community. These predictions could be tested experimentally through the development of
an in vitro model system of the four species. More importantly, these results suggest that therapeutic
strategies that target species–species interactions could be promising alternatives to conventional
antibiotics that target C. difficile directly.
Host–microbiota perturbations modeled as increases in glucose and decreases in amino acid
concentrations reproduced several features of C. difficile-associated dysbiosis including substantially
reduced F. prausnitzii and increased C. difficile abundances and an imbalance in SCFA synthesis
characterized by increased acetate and reduced butyrate levels [94]. The predicted decrease in
anti-inflammatory butyrate would be expected to exasperate dysbiosis and accelerate disease
progression [69,74]. Similar results were obtained when glucose and amino acid changes were replaced
by increases in the primary bile acid taurocholate, which was predicted to be used as an electron
acceptor by C. difficile in vivo to provide a growth advantage in the absence of commensal bacteria that
degrade primary bile acids to secondary bile acids [61,95,96]. Taurocholate availability was predicted
to have less effect on butyrate and propionate synthesis, but the SCFA imbalance remained due to high
acetate synthesis. Our model predicted that dysbiosis could be induced with moderate changes in
nutrient concentrations, a prediction that could be tested in vitro and suggesting the possible promise
of therapeutic strategies that aim to alter the gut nutritional environment.
Despite their many consistencies with experimental studies [12,97,98], our simulations with
glucose, amino acids, and taurocholate changes were unable to reproduce the large increase in
E. coli abundance observed during CDI [71,73]. The addition of host-derived nitrate [17,99]t ot h e
other nutrient changes rectified this inconsistency and reproduced the key microbiota signatures of
C. difficile-associated dysbiosis during CDI: large increases in C. difficile and E. coli abundances, large
decreases in health-promoting F. prausnitzii abundance, and moderate changes in B. thetaiotaomicron
abundance. The model generated high acetate levels associated with dysbiosis states, a prediction that
could be tested through in vitro experiments. We believe further development of our multispecies
biofilm model could yield a general computational platform for in silico investigation of CDI, other
gut infections, and chronic inflammation disorders such as inflammatory bowel and Crohn’s diseases.
Some possibilities include the modeling of C. difficile spore formation/germination, the inclusion of
more commensal gut species (e.g., [100]) including those from other phyla [101–103], the addition of
a broader array of gut nutrients including fibers, oligosaccharides, and fats resulting from realistic
diets [12–15,104], and modeling of the human host through incorporation of available metabolic
reconstructions such as Recon 2 or Recon 3D [105–107]. A possible drawback of our modeling approach
is the lack of species-specific parameters for nutrient uptake kinetics and metabolite-dependent mass
transfer coefficients.
4. Materials and Methods
4.1. Biofilm Model Formulation and Solution
The multispecies biofilm model was constructed by combining genome-scale metabolic
reconstructions of C. difficile (Strain 630∆erm) [88] and three commensal gut species: B. thetaiotaomicron [89],
30