An in vitro intestinal model captures immunomodulatory properties of the microbiota in inflammation

Abstract

Considerable effort has been put forth to understand mechanisms by which the microbiota modulates and responds to inflammation. Here, we explored whether oxidation metabolites produced by the host during inflammation, sodium nitrate and trimethylamine oxide, impact the composition of a human stool bacterial population in a gut simulator. We then assessed whether an immune-competent in vitro intestinal model responded differently to spent medium from bacteria exposed to these cues compared to spent medium from a control bacterial population. The host-derived oxidation products were found to decrease levels of Bacteroidaceae and overall microbiota metabolic potential, while increasing levels of proinflammatory Enterobacteriaceae and lipopolysaccharide in bacterial cultures, reflecting shifts that occur in vivo in inflammation. Spent microbiota media induced elevated intracellular mucin levels and reduced intestinal monolayer integrity as reflected in transepithelial electrical resistance relative to fresh medium controls. However, multiplexed cytokine analysis revealed markedly different cytokine signatures from intestinal cultures exposed to spent medium with added oxidation products relative to spent control medium, while cytokine signatures of cultures exposed to fresh media were similar regardless of addition of host-derived cues. Further, the presence of immune cells in the intestinal model was required for this differentiation of cytokine signatures. This study indicates that simple in vitro immune-competent intestinal models can capture bacterial-mammalian cross-talk in response to host-derived oxidation products and supports utility of these systems for mechanistic studies of interactions between the gut microbiome and host in inflammation.

Keywords: Enterobacteriaceae; Gut microbiome; anaerobic respiration; endotoxin; gut simulator; inflammation; innate immune cells; intestinal model.

Figure 1.

(a) Relative abundance at family level of taxonomy of GIFU and pGIFU microbiota. (b) Metagenome functional prediction by PICRUSt analysis at levels 1, 2 and 3 of KEGG pathways. Histogram bars indicate means of Log₂(Level 3 pathway abundance) ± SEM clustered by level 2 (detailed in Supplement A), * p < .05; ** p < .01, *** p < .001, and **** p < .0001. (c) Endotoxin levels of different test media: fresh media GIFU and pGIFU, as well as spent media GIFU-S and pGIFU-S. * p < .05.

Figure 2.

(a) DNA concentration of Caco2/HT29-MTX monolayers with and without dendritic cells (± DC) exposed to different test media. + p < .05 comparing all cultures -DC to +DC (main effect of cell model). (b) Transepithelial electrical resistance (TEER) indicating intestinal barrier function after 24-h exposure of monolayers to test media. * p < .05 comparing spent vs. fresh, ^ p < .05 comparing medium with proinflammatory factors vs. corresponding medium without proinflammatory factors, # p < .05 comparing to control serum-free intestinal cell culture media, with all comparisons within a given cell culture model, and ** p < .05 comparing -DC to +DC exposed to the same medium. All other comparisons are not significant. All measurements were collected from N = 3 biological replicates, and results are reported as an average $\pm$ standard deviation.

Figure 3.

Cocultures with DC stained for tight junction (ZO-1, green) and DNA (DAPI, blue). (←) indicates areas of zig-zag ZO-1 staining pattern and (*) denotes low ZO-1 staining. Scale bar: 20 µm. A similar staining trend was observed for -DC cultures (data not shown).

Figure 4.

(a) Intracellular and (b) secreted mucin concentration from Caco2/HT29-MTX monolayers with and without dendritic cells (± DC) after exposure to test media. # p < .05 comparing to control serum-free intestinal cell culture media. All measurements were collected from N = 3 biological replicates and results are reported as an average $\pm$ standard deviation.

Figure 5.

Hierarchical clustering of cytokine z-scores in different experimental groups. The cytokine levels are normalized to DNA concentration, mean-centered, and divided by standard deviation to generate z scores. Color bar indicates the relative cytokine amount, where red and blue correspond to high and low concentrations, respectively.

Figure 6.

(a, b) Scores and (c, d) Loadings plots for a two-component PCA model constructed using measured cytokines in cultures (a, c) +DC and (b, d) – DC. The percentages noted reflect the percentage of variance in the data set captured in the latent variable.