The human gut microbiome is known to be associated with various human disorders, but a major challenge is to go beyond association studies and elucidate causalities. Mathematical modeling of the human gut microbiome at a genome scale is a useful tool to decipher microbe-microbe, diet-microbe and microbe-host interactions. Here, we describe the CASINO (Community And Systems-level INteractive Optimization) toolbox, a comprehensive computational platform for analysis of microbial communities through metabolic modeling. We first validated the toolbox by simulating and testing the performance of single bacteria and whole communities in vitro. Focusing on metabolic interactions between the diet, gut microbiota, and host metabolism, we demonstrated the predictive power of the toolbox in a diet-intervention study of 45 obese and overweight individuals and validated our predictions by fecal and blood metabolomics data. Thus, modeling could quantitatively describe altered fecal and serum amino acid levels in response to diet intervention.

A genome-scale metabolic model (GEM) is an integrative platform for exploring genotype-phenotype relationships and metabolic differences between different clinical conditions (). We previously reconstructed GEMs to study the interactions between Bacteroides thetaiotaomicron and Eubacterium rectale (), representatives of Bacteroidetes and Firmicutes, the two dominant phyla in the human gut (), and between Bifidobacterium adolescentis and Faecalibacterium prausnitzii (), also dominant and dietary-responsive gut microorganisms (). In both studies, we manually identified the interactions between the bacteria and quantified the consumption and production rates of the defined interacting metabolites for each bacterial species. Although other studies have been conducted for communities of two and three species (), these approaches cannot be expanded to simulate the interactions of a large number of species representing the complex gut ecosystem. Therefore, we developed the CASINO (Community And Systems-level INteractive Optimization) toolbox, which comprises an optimization algorithm integrated with diet analysis to predict the phenotypes and related dietary intake within the human gut microbiota. The toolbox was tested using both data from in vitro experiments and results from a nutritional intervention study of subjects with varying gut microbial gene richness.

By quantifying the release and consumption of metabolites by the gut microbiota, it may be possible to elucidate interactions between the gut microbiota and host metabolism (). This information would allow identification of diagnostic biomarkers and may provide insight into the role of the gut microbiota in disease progression (). A predictive systems-level model of the human gut microbiome is required to elucidate causalities and quantify the interactions between microbes, host, and diet ().

Increasing evidence indicates that changes in the composition of the human gut microbiota affect host metabolism and are associated with a variety of diseases (). Changes in diet have been shown to rapidly affect the composition of the gut microbiota (). Furthermore, microbiota-diet interactions impact host physiology through the generation of a number of bioactive metabolites (). For example, short-chain fatty acids (SCFAs), which are generated by microbial fermentation of dietary polysaccharides in the gut, are an important energy source for colonocytes and also function as signaling molecules, modulating intestinal inflammation and metabolism (). In addition, dietary proteins and amino acids are important substrates for microbial fermentation in the colon (), where they also serve as an important nitrogen source for the microbiota and support the growth of the microbiota and the host ().

From this model analysis, we found that the gut microbiome of HGC individuals had a higher consumption of these eight essential amino acids at week 6 compared to that of the LGC subjects at baseline. An incremental augmentation of these amino acids would permit to acquire a similar metabolism of the gut microbiome in LGC and HGC subjects ( Figure 7 A). Many different combinations of food sources could fulfill such a requirement for essential amino acids. However, in an attempt to identify some overall guidelines, we correlated the difference between the two different requirements of amino acids with the composition of these amino acids in different food types ( Table S8 ). This showed that LGC individuals should significantly increase consumption of dairy products, vegetables, white meat, fish pulses, eggs, oils, and butter. In the meantime, they should considerably reduce intake of pastries, bread, and rice to improve and slightly reduce intake of cereals and nuts ( Figure 7 B).

We then used CASINO and the abundance of the five different species, i.e., B. thetaiotaomicron, B. adolescentis, F. prausnitzi, E. rectale, and L.reuteri, to predict the relative consumption of eight essential amino acids by the gut microbiome in the LGC subjects at baseline (base phenotype in Figure 7 A) and in the HGC subjects at week 6 (improved phenotype in Figure 7 A).

(B) After calculating the required amount of eight essential amino acids at baseline and improved phenotype, both patterns were correlated with composition of amino acids in different food categories. The direction of the Corr Improved − Corr Base indicates the positive/negative effect of different food sources to improve the phenotype of LGC subjects. Corr Base , correlation between pattern amino acids in base phenotype and food; Corr Improved , correlation between pattern amino acids in improved phenotype and food).

(A) The yellow circles specify the simulated consumption of the eight essential amino acids by the gut microbiome of the LGC individuals at baseline (base phenotype), and the green circles specify the simulated consumption of the eight essential amino acids for the HGC individuals at week 6 (improved phenotype).

Finally, assuming that LGC subjects have a non-optimal gut microbiome metabolism (associated with a clinically altered metabolism) we wanted to identify which dietary change would improve the metabolism of their gut microbiome. Therefore, we made the assumption that an adapted dietary recommendation in LGC subjects provided at baseline would enable them to reach the “optimal” gut microbiome metabolism of HGC subjects after 6 weeks of dietary intervention, which is, indeed, associated with an improved metabolic phenotype.

To evaluate whether these changes in serum metabolite levels may have any clinical relevance, we analyzed the correlations between the levels of the ten amino acids in the serum and bioclinical parameters of the subjects at baseline ( Figure 6 C). Here, we found that the serum phenylalanine levels were positively correlated with clinical variables related to body corpulence (BMI [body mass index], DXA [dual-energy X-ray absorptiometry]-measured fat mass, waist circumference, leptin), insulin resistance, blood lipid homeostasis (serum triglycerides and cholesterol), and low-grade inflammation (human sensitive C-reactive protein; hsCRP). The serum levels of valine and leucine were also positively correlated with BMI.

Although the model simulations could correctly predict changes in several of the metabolites in the feces, we noted that the model simulations did not accurately predict changes in all the measured metabolites, which may be a result of differential absorption by the host. Therefore, we evaluated whether the model could predict changes in the serum. We used metabolomics to analyze serum of the 45 subjects at baseline and after 6 weeks of dietary intervention and found an excellent correspondence between the model predictions ( Figure 5 A) and the measured changes ( Figure 6 A). The serum levels of ten detected amino acids decreased in response to the dietary intervention in the LGC subjects ( Figure 6 A). Furthermore, in agreement with the model predictions, there was a decrease in acetate in response to the diet intervention in all subjects ( Figure 6 A). In addition, we observed that phenylalanine levels were higher in LGC subjects, compared to HGC subjects at baseline, but that the level of phenylalanine decreased in LGC subjects after 6 weeks of dietary intervention ( Figures 6 A and 6B). We also observed that levels of valine, leucine, and alanine were higher in LGC subjects at baseline ( Figures 6 A and 6B).

(C) Correlation of the ten quantified amino acids in serum with different clinical parameters of HGC and LGC subjects. The figure shows significant correlations (p < 0.05), with the color code specifying the slope of the correlation. Fat mass was measured by biphotonic absorptiometry (DXA). MIP1b, macrophage inflammatory protein 1b; sCD14, soluble CD14; hsCRP, human sensitive CRP; HOMA-IR, homeostatic model assessment − insulin resistance = G l u c o s e × I n s u l i n / 22.5 ; BMI, body mass index (kg/m 2 ); Disse index = 12 × [ 2.5 × ( H D L / T o t a l C h o l e s t e r o l ) − F F A ] − I n s u l i n ; MIP1b: macrophage inflammatory protein 1b; hsCRP, human sensitive CRP; NEFA, non-esterified fatty acids.

In addition to predicting changes in some of the metabolites in response to dietary intervention, the model could also be used to predict the relative contribution of each bacterial species to production of specific metabolites, allowing us to quantitatively access how a variation in the gut microbiome correlates with metabolite production. Thus, we predicted the contribution of each bacterial species to phenylalanine levels in the gut ecosystem and showed that 23% of the total phenylalanine is produced by B. adolescentis and 26% by E. rectale in HGC individuals at baseline, while this contribution increased for B. adolescentis to 26% and decreased for E. rectale by 21% after 6 weeks of dietary intervention ( Figure S3 ). For LGC individuals, the contribution of E. rectale to phenylalanine production was 29% at baseline and decreased to 15% after 6 weeks of dietary intervention ( Figure S3 ).

To experimentally evaluate our predictions on altered metabolite production by the gut ecosystem, we performed metabolomics analysis of fecal samples obtained from the HGC and LGC individuals at baseline and after 6 weeks of dietary intervention. These data confirmed many of the predicted simulations by CASINO, i.e., alanine, proline, glycine, serine, phenylalanine, and tyrosine all showed decreased levels in response to the diet intervention in both HGC and LGC subjects but with a larger decrease in the LGC subjects ( Figure 5 B, shift down-ward right). To test the significance of these changes for each group of subjects and between the two time points, we calculated p values using a Student’s t test, and, except for alanine, these changes were significant for the different groups ( Figure 5 C). Measured serine levels were significantly higher in LGC than in HGC individuals at baseline but not after 6 weeks of dietary intervention ( Figure 5 B), in agreement with the predicted results ( Figure 5 A). Also, measured phenylalanine levels were significantly higher in LGC individuals than in HGC individuals at baseline but lower in LGC individuals compared to HGC subjects after 6 weeks of dietary intervention, in agreement with predicted results.

With this approach, we could simulate the profile of three SCFAs and 14 amino acids produced by the gut ecosystem, as well as the contribution of each microbial species to the overall metabolite production of the ecosystem at baseline and after 6 weeks of dietary intervention for each individual. By plotting average profiles for all the subjects, we found that the levels of the SCFAs and amino acids produced by the gut microbiota were significantly decreased after dietary intervention when both LGC and HGC groups were combined ( Figure 5 A, decrease in the y axis direction), but the greatest reductions were observed in LGC individuals ( Figure 5 A, increase in the x axis direction).

(A) Summary of average phenotypic predictions for baseline and after 6 weeks. The group of metabolites at the top right denotes the predictions at baseline, and the group at the bottom left represents predictions after 6 weeks of dietary intervention. Subtracting the log 10 average metabolite fluxes for HGC from LGC is represented on the x axis, and the summation is represented on the y axis. The x axis shows the ratio of predicted metabolite levels between HGC and LGC, and the y axis shows the sum of predicted metabolite levels in the two groups. The colors show the metabolites’ distance from zero on the y axis (from dark blue at the top to dark red at the bottom).

For the simulations, we assumed that carbohydrates and fibers were hydrolyzed to glucose to the same degree in all subjects, allowing us to calculate the relative amount of glucose available to the gut microbiome in each subject. We further assumed that glucose was the limiting substrate for the gut microbiome. We first used CASINO to quantify the community interactions and the relative glucose uptake by the individual species. We used the calculated values of species abundance in this process. Thereafter, we used CASINO to repeat the simulations but now allowing the individual species to consume amino acids in the same ratio as their glucose uptake. The amount of available amino acids was calculated from the diet composition using CASINO.

To simulate the effect of the diet on the overall gut microbiota metabolism, we used representatives of the most abundant microbial groups that we had also modeled in vitro, i.e., B. thetaiotaomicron, B. adolescentis, F. prausnitzi, E. rectale as described earlier, and Lactobacillus reuteri, for which we reconstructed a GEM. We also performed simulations with inclusion of E. coli, but as this species had no major impact on the production of SCFAs and amino acids (data not shown), we did not include this species in our further analysis. Using CASINO, we simulated the effect of diet on the human gut microbiota composition at baseline and after the dietary intervention for 44 of the subjects (registered diet information in Table S6 ). To translate the diets into metabolites that can be utilized by the five gut bacterial species, we computed the dietary macronutrients of 24 different food items ( Table S7 ) and used this information in a diet allocation algorithm in CASINO. With this algorithm, CASINO predicted that there was a decrease in carbohydrate consumption and an increase in amino acid consumption for all individuals after 6 weeks of dietary intervention ( Figure 4 C). The intake of fiber from bread and potatoes—and, to a lesser extent, from rice, cereals, and snacks—was decreased, but fiber from fruits and vegetables was increased in agreement with the dietary recommendation given to the patient during the intervention ( Figure 4 C).

Analysis of metagenomics data before and after the diet intervention showed that six species dominated in all subjects: Escherichia coli and F. prausnitzii and four species associated with Clostridia, Bacteroides, Bifidobacteria, and Lactobacillus (). To obtain quantitative data of these species, we analyzed fecal samples by 16S rRNA qPCR before and after the dietary intervention ( Table S5 Supplemental Experimental Procedures ) and used these results to calculate the distribution of biomass between the species ( Figure 4 B). We observed significant differences in abundance for B. adolescentis, F. prausnitzi, and E. rectale at baseline and for B. adolescentis and L. reuteri after 6 weeks between LGC and HGC individuals. In HGC individuals, levels of B. thetaiotaomicron significantly increased and L. reuteri and F. prausnitzii significantly decreased after 6 weeks of dietary intervention compared with baseline, whereas a significant decrease in LGC individuals was only seen for L. reuteri.

To further evaluate CASINO, we examined data from a clinical study where 45 overweight and obese individuals were subjected to an energy-restricted, high-protein diet with low glycemic index for 6 weeks ( Figure 4 A; clinical data in Table S4 ). These patients had previously been stratified based on their gut microbial gene richness into “low gene count” (LGC; n = 18) and “high gene count” (HGC; n = 27), based on a cutoff threshold of 480,000 genes (). LGC demonstrated a worse metabolic profile compared with HGC individuals ().

(C) A diet algorithm was developed and implemented for prediction of the macromolecules present in different food sources, and this allowed further conversion of diets to three main categories of macronutrients carbohydrates, fiber, and amino acids.

(B) Abundance of species before and after diet interventions in HGC and LGC subjects. Data are shown as boxplots with E. coli (red), B. thetaiotaomicron (yellow), B. adolescentis (jade), L. reuteri (brown), F. prausnitzi (blue), and E. rectale (green). The heatmap shows the p values for four different comparisons of the species levels (each row associated with the corresponding species indicated in the left part of the figure). Data are presented as mean ± SD. grBiomass, grams of biomass.

(A) For each food source, the major macronutrients were quantified, and this enabled using the CASINO Toolbox to study the effect of the diet on the gut microbiota composition of subjects classified on the basis of on their microbial gene richness.

Next, we calculated the centrality scores for each bacterial species to identify which species have a dominant role in the overall metabolic conversion in each community ( Supplemental Experimental Procedures ). We observed that E. rectale and B. thetaiotaomicron were the main receptor and effector, respectively, and thus represent key species ( Figure 3 A). We then evaluated the sensitivity of the optimization algorithm in CASINO by adding bacteria in three steps to each of these two bacteria, culminating in the reconstruction of the two in vitro communities. We calculated the SCFA levels for each step ( Figure 3 B). Addition of B. adolescentis to E. rectale in the EBBR community resulted in reduced production of butyrate and increased production of propionate and acetate. Addition of F. prausnitzii to B. thetaiotaomicron in the FBBR community resulted in reduced production of propionate and acetate and increased butyrate production. The levels of the SCFAs changed further when the other species were added ( Figure 3 B).

(B) The sensitivity of CASINO optimization was tested by evaluating the changes in the SCFA profile upon adding different species to the community. First, the most important receptor and effector in the communities were identified using the result of Figure 3 A. 1 mmol/l of glucose was used for all the simulations, and the SCFA profiles were predicted. Following identification of the dominant receptor and effector, the key species, the other species were added to the community one by one until the EBBR and FBBR communities were reconstructed. Comparison between the simulations showed that the SCFA profile is very sensitive of the absence and presence of species with respect to their abundance and interactions.

(A) The community of B. thetaiotaomicron, B. adolescentis, F. prausnitzii, E.rectale, and R.bromii were tested based on being receptors (receiving metabolites from the other microbes) or effectors (producing metabolites where consumed by receptors). Two methods of centrality were tested on these networks (power centrality and degree centrality). Calculated centrality scores determined E. rectale as the most important receptor and B. thetaiotaomicron as the most important effector.

Our model simulations correctly predicted the net production of the metabolites produced by each community and showed that the communities synthesized more essential amino acids than non-essential amino acids ( Figure 2 B). More importantly, the simulations enabled quantification of the contribution of each individual bacterial species to the overall microbial conversion in the communities and showed that two of the species in each community dominated. Specifically, we predicted that B. thetaiotaomicron and E. rectale synthesized 41% and 36% of the amino acids in the EBBR community, respectively, and that B. thetaiotaomicron and F. prausnitzii synthesized 39% and 47% of the amino acids in the FBBR community, respectively ( Figure S2 ). We also predicted that E. rectale mainly contributed to the synthesis of valine, leucine, phenylalanine, and methionine in the EBBR community, while F. prausnitzii was the major contributor to the production of valine and leucine in the FBBR community. Furthermore, the experimental data showed that substitution of E. rectale with F. prausnitzii decreased the level of butyrate in the media, due to the higher capacity of E. rectale for butyrate production (), and also the model simulations showed a slightly lower butyrate production by the FBBR community compared with the EBBR community.

To test the performance of CASINO, we simulated the interactions between the microbes in two microbial communities that differed only in one bacterial species: EBBR (E. rectale, B. adolescentis, B. thetaiotaomicron, and R. bromii) and FBBR (F. prausnitzii, B. adolescentis, B. thetaiotaomicron, and R. bromii) ( Figure 2 A). The simulated values were validated by culturing EBBR and FBBR communities in selected media. We quantified the abundance of individual bacterial species in each community by 16S rRNA-based qPCR ( Table S3 ). We also performed targeted metabolomics to quantify the production of SCFAs and amino acids and the consumption of carbohydrates (starch and cellobiose) for each community ( Supplemental Experimental Procedures ).

(B) Predicted and measured levels of SCFA and amino acids by the two in-silico microbial communities including EBBR (E. rectale, B. adolescentis, B. thetaiotaomicron, and R. bromii) and FBBR (F. prausnitzii, B. adolescentis, B. thetaiotaomicron, and R. bromii). We found that synthesis of essential amino acids (histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, and valine) produced by the communities is higher than the production of non-essential amino acids (alanine, glutamate, glycine, proline, serine, and tyrosine).

(A) Two in silico microbial communities, EBBR (E. rectale + B. adolescentis + B. thetaiotaomicron + R. bromii) and FBBR (F. prausnitzii + B. adolescentis + B. thetaiotaomicron + R. bromii), were designed and simulated using the CASINO Toolbox. The results were compared with data from triplicate in vitro experiments for EBBR and FBBR communities. In CASINO, the interactions of the bacteria as well as the phenotype of the community were identified using an optimization algorithm. Growth of each bacterium had local optimum, whereas the community had global optimum. The community optimum was detected by the intersection point of the fixed constraints for the community and the calculated dynamic constraints, which was obtained by summation of the local and community forces.

The GEMs were functionally validated using experimental data for each of the five bacteria. We quantified the abundance of the bacteria by 16S rRNA qPCR at baseline and after 24 hr of growth in selected media ( Table S2 Supplemental Experimental Procedures ). We performed targeted metabolomics to quantify products of the fermentative activity of the studied bacteria; specifically, the SCFAs butyrate, acetate, and propionate and 15 different amino acids, as well as consumption of carbohydrates (glucose, maltose, cellobiose, and starch). Flux constraints were imposed using the metabolomics profiles of the growth media, and maximum growth of each bacterial species was set as an objective function to simulate the predictive power of the corresponding model ( Figure 1 A; Experimental Procedures ). The experimental data confirmed that the GEMs predicted the metabolism and biomass growth for each bacterial species ( Figures 1 B–1D). The GEMs correctly predicted that acetate can be produced by B. adolescentis, B. thetaiotaomicron, and R. bromii; butyrate can be produced by E. rectale and F. prausnitzii; and propionate can only be produced by B. thetaiotaomicron ( Figure 1 B). Our simulations also predicted that these five bacteria synthesize significantly higher levels of essential amino acids (valine, leucine, methionine, lysine, and phenylalanine) compared to non-essential amino acids (serine, tyrosine, and threonine) ( Figure 1 D).

(D) Predicted and measured levels of amino acids by the individual bacteria. Each model also predicted amino acid levels, and the details of 15 significant amino acids produced are shown for each bacterium. The predicted and experimental values showed that all amino acids could be produced in the range of experimental data with specific optimum solution.

(C) Predicted and measured biomass at the end of the fermentations. Growth was set as an objective function for each model, and the predicted growth was compared with the experimental data.

(B) Predicted and measured SCFA levels by the individual bacteria. Propionate was produced only by B. thetaiotaomicron, while acetate was produced by Actinobacteria and Bacteroidetes phyla. Butyrate production was mainly produced by the bacteria from the class Clostridia.

(A) Each GEM was validated based on the rRNA and metabolomics data generated by in vitro experiments. The byproducts and the substrate usage were constrained in the models, and the growth rate was compared with the experimental data.

To evaluate CASINO, we used the RAVEN toolbox () to update and significantly expand the content of our previously published GEMs for B. thetaiotaomicron, E. rectale, B. adolescentis, and F. prausnitzii and to generate a GEM for Ruminococcus bromii, a representative of Clostridiales and a key gut symbiont (). All GEMs were manually curated for functionality based on literature information. We defined a set of metabolic tasks, e.g., generation of biomass precursors ( Table S1 ), to further investigate the functionality of the GEMs and checked that the resulting models could perform the defined tasks ( Experimental Procedures ).

Simulations using CASINO start with an initialization stage that defines a primary profile of the systems-level topology (i.e., which species are present and how do they interact). This leads to the construction of a community matrix that defines effectors and receptors, with effectors being species that produce metabolites and receptors being species that consume metabolites. Following definition of the topology, the initialization step calculates metabolite production by each species using organism-level optimization. Thereafter, CASINO performs iterative multi-level optimization to calculate the relative uptake of carbohydrates by each species, until the total community biomass production is optimized. In this study, this calculation is constrained by the relative abundance of each species ( Figure S1 Experimental Procedures ).

We first developed an optimization algorithm in the CASINO toolbox, which is based on a collaborative and a multi-dimensional distributed approach (). It takes into account both collaboration between the multiple species and the fact that each individual species seeks to optimize its growth individually. Although GEMs are linear models, the presence of several GEMs in the overall community model means that the optimization of community biomass production becomes a non-linear problem. Therefore, we separated the community model into systems level (representing the community) and organism level (representing each species), which allowed us to linearize the optimization problem.

Discussion

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et al. Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. The overall metabolism of the gut microbiome can be modeled in one of two ways: (1) by using a lumped model of all the metabolic reactions active in the different gut microorganisms or (2) by compartmentalizing the metabolism according to the individual microorganisms. The latter is clearly a better reflection of the true biological system, and it also ensures that redox and energy balances are constrained within each organism considered. Therefore, we used this approach to model the metabolism of the human microbiome and reconstructed GEMs for individual species from the predominant phyla in the human gut. We identified which species to include in our analysis based on their abundance in the gut ecosystem. Thus, we reconstructed GEMs for five species that are representative bacteria of the dominant phyla in the human gut, and we hypothesized that the reactions included in our models cover most of the metabolic functions that are present in the human gut. Compared with earlier attempts to model the human gut metabolism using GEMs, i.e., the COMETS algorithm (), CASINO allows inclusion of several species in the simulations. Furthermore, it is scalable and enables expansion to include even more than the five species that we considered in this study.

To evaluate whether we are covering the main metabolic functions, we simulated the effect of different diets, studied the interactions between the microbes and host in response to the diet, and quantified the contribution of each bacterial species to the fecal metabolite profiling. The model simulations matched fecal metabolomics data, but more importantly, it correctly correlated with changes in serum levels of ten amino acids and one SCFA (acetate). Thus, the model captures some major metabolic functions of the human gut microbiome. In the future, the selection of species to be considered should be expanded, in particular, to ensure representation of more specific metabolic functions, such as vitamin biosynthesis and bile acid metabolism.

The consistency between model predictions of metabolite productions and measurement of changes in metabolite levels in feces and serum suggests that our modeling correctly predicts the overall carbon fluxes in the gut ecosystem. Furthermore, our simulations enabled quantification of how the individual species compete for nutrients and produce different metabolites that may serve as nutrients for other species or be absorbed by the host. Studying the gut metabolism with our holistic approach also enabled understanding of metabolic shifts under different clinical conditions and hereby could provide a direct link between the gut microbiome metabolism and serum chemistry.

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Rensen S.S. The role of microbial amino acid metabolism in host metabolism. Thus, our simulations suggest that the gut microbiome may contribute to altered levels of several amino acids in the serum, including phenylalanine and branched-chain amino acids. This is in line with an early report, using germ-free mice, showing that the microbiota of the large intestine increased the free amino acid level in the gastrointestinal tract (). A later study showed that bacteria in the human large intestine take up peptides and amino acids and convert these to different amino acids and SCFAs (). This study also showed that the production of amino acids was dependent on the composition of starch, proteins, and peptides and, hence, will be dependent on the dietary composition. Further confirmation of our findings is documented in a recent review on the role of microbial amino acid metabolism in host metabolism that provides a summary on a number of findings related to the role of the microbiota in the large intestine on production of not only SCFAs but also amino acids that are subsequently taken up by the host (). However, further experiments are required to validate the direct contribution of the gut microbiome to host amino acid metabolism.

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et al. Gut microbiomes of Malawian twin pairs discordant for kwashiorkor. Using our approach, we also predicted the relative contribution of each bacterial species to the production of specific metabolites and studied how this variation in the gut microbiome is correlated with specific metabolite production. Information generated from CASINO may, therefore, be extended for rational design of prebiotics as well as for identifying novel beneficial bacteria that can be used to fortify the microbiota to improve the gut microbiome metabolism. Importantly, rational design of microbiota interventions requires knowledge of diet, as demonstrated in a study of children with kwashiorkor, which showed that a disrupted microbiome can be reversed by dietary interventions (). Interestingly, transferring the microbiota from children with kwashiorkor to germ-free mice in combination with a Malawian diet resulted in marked weight loss in recipient mice associated with perturbations in amino acids. As we demonstrated, CASINO can also be used to predict dietary changes required to ensure a certain profile of the gut metabolism, here represented as a specific consumption of eight essential amino acids. The gut microbiome may change in response to dietary modulation, something that our simulations are not capturing. This study also emphasizes the importance of developing accurate tools to properly record dietary intakes in different populations. With more data, it will probably be possible to also predict how the diet influences gut microbiome changes, and CASINO may hereby assist in the development of a precision medicine approach to treat metabolic diseases associated with dysfunction of the gut microbiota.

In conclusion, we demonstrate how we can use model simulations to predict metabolic interactions within the gut microbiome and hereby assist in generating mechanistic insight into the contribution of individual species of the gut microbiome to the overall metabolism of the ecosystem and the host. Furthermore, focused on the diet and on host and gut microbiota metabolic interactions, we show how the gut ecosystem and the individual members of the gut microbiota contribute to the host metabolism. CASINO may thus constitute a valuable tool for enriching the information content provided by gut metagenome analysis, hereby advancing our understanding on how this important metabolic organ contributes to disease development, and thus facilitate personalized interventions based on the microbiome.