Characterization of interactions of dietary cholesterol with the murine and human gut microbiome

Abstract

Consumption of dietary lipids, such as cholesterol, modulates the gut microbiome with consequences for host health through the production of microbiome-derived metabolites. Despite the implications for host metabolism, a limited number of specific interactions of the gut microbiome with diet-derived lipids have been characterized. This is partially because obtaining species-level resolution of the responsible taxa can be challenging and additional approaches are needed to identify health-relevant metabolites produced from cholesterol-microbiome interactions. Here we performed bio-orthogonal labelling sort sequence spectrometry, a click chemistry based workflow, to profile cholesterol-specific host-microbe interactions. Mice were exposed to an alkyne-functionalized variant of cholesterol and 16S ribosomal RNA gene amplicon sequencing of faecal samples identified diet-derived cholesterol-interacting microbes from the genera Bacteroides, Bifidobacterium, Enterococcus and Parabacteroides. Shotgun metagenomic analysis provided species-level resolution of diet-derived cholesterol-interacting microbes with enrichment of bile acid-like and sulfotransferase-like activities. Using untargeted metabolomics, we identify that cholesterol is converted to cholesterol sulfate in a Bacteroides-specific manner via the enzyme BT_0416. Mice monocolonized with Bacteroides thetaiotaomicron lacking Bt_0416 showed altered host cholesterol and cholesterol sulfate compared with wild-type mice, identifying a previously uncharacterized microbiome-transformation of cholesterol and a mechanism for microbiome-dependent contributions to host phenotype. Moreover, identification of a cholesterol-responsive sulfotransferase in Bacteroides suggests diet-dependent mechanisms for altering microbiome-specific cholesterol metabolism. Overall, our work identifies numerous cholesterol-interacting microbes with implications for more precise microbiome-conscious regulation of host cholesterol homeostasis.

Fig. 1. Isolation of diet-derived cholesterol-interacting microbes using bio-orthogonal labelling and FACS.

a, Schematic showing treatment of mice with Chol^Alk to identify microbes that take up dietary cholesterol and derivatives of dietary cholesterol. b, Confocal microscopy detecting Chol^Alk-interacting bacteria from mouse caecal contents (n = 6, representative images from one mouse are shown). c, Schematic depicting FACS-based separation of microbial communities into cholesterol-interacting (Alk+) or not interacting (Alk−) populations. d, Confocal microscopy detecting the presence (alkyne positive, Alk+) or absence (alkyne negative, Alk−) of Chol^Alk derivatives in microbiome samples separated using FACS (n = 4, representative images from one mouse are shown). For confocal microscopy staining: blue, Hoechst 33342; red, Alexa Fluor 647-azide (AF647). Scale bar, 20 μm.

Fig. 2. Gut microbes that interact with diet-derived cholesterol.

a, 16S rRNA gene amplicon sequencing identifies taxonomic classification of cholesterol-interacting microbes enriched by FACS using the BOSSS workflow. The fold-enrichment is between relative abundances of taxa in the Alk+ fraction compared with the Alk− fraction and the heatmaps represent square root transformed relative abundances of the ASVs. Relative abundances of Alk+ and Alk− samples were averaged separately for both in vivo and ex vivo experiments (n = 3 for each sorted population). b, Metagenomic analysis identifies UniRef protein clusters associated with cholesterol-interacting microbes. The left bar plot demonstrates the relative abundance of each enriched cluster, in which the background is colour differentiated based on their grouped biochemical annotations, and the bar and dot colours indicate the sample source (n = 3 from biologically independent animals for in vivo and n = 3 for ex vivo biologically independent stool cultures). c.p.m., copies per million. The right bar plot shows the mean contribution of the species identified in the Alk+ to the corresponding UniRef protein clusters. Per-species cluster abundance values were normalized to each cluster within each sample individually, and the mean abundances were further taken across individuals. c, Per-genus contribution of all annotated sulfation processes in the Alk+, cholesterol-interacting microbiome population in both in vivo and ex vivo samples as described in b. Bar chart values are mean ± s.d.

Fig. 3. The murine gut microbiota can transform cholesterol to Chol^Sulf.

a, Representative structure of coprostanol alkyne. Ion chromatograms depicting detection of coprostanol alkyne from caecal content, E. cop cultures, and ex vivo stool cultures. b, Representative structure of Chol^Alk-Sulf. Ion chromatograms depicting detection of Chol^Alk-Sulf in caecal content and ex vivo stool cultures. c, Bacterial cholesterol sulfotransferase candidates identified from metagenomic analysis were screened for Chol^Sulf production. Species-specific cultures were treated with cholesterol. Ion chromatograms represent the detection of Chol^Sulf (n = 2 biological replicates per species, representative traces shown here).

Fig. 4. A gene cluster in Bacteroides converts cholesterol to Chol^Sulf.

a, Heterologous expression of cholesterol sulfotransferase candidates in BL21 E. coli treated with either Chol^Alk or cholesterol and ion chromatograms representing the detection of Chol^Alk-Sulf or Chol^Sulf, respectively (n = 2 biological replicates per gene candidate, representative traces are shown here) b, In vitro conversion of cholesterol to Chol^Sulf and Chol^Alk to Chol^Alk-Sulf via enriched His₆_BT_0416 (n = 2 biological replicates per condition, representative traces are shown here) c, Ion chromatograms demonstrating that the Bt_0416 deletion mutant does not make Chol^Sulf. d, Putative biosynthesis of PAPS followed by the biosynthesis of Chol^Sulf in Bacteroides. Gene products are coloured according to the key in e. e, Biosynthetic clusters representing putative Chol^Sulf biosynthesis genes in B. thetaiotaomicron, B. ovatus and B. fragilis. Chromatograms are scaled to the largest peak in each dataset. The red text highlights metabolic functions attributed to BT_0416. Source data

Fig. 5. B. thetaiotaomicron sulfotransferase activity influences host Chol^Sulf and cholesterol levels in a gnotobiotic mouse model.

a,b, Expression of Bt_0412 (a) and Bt_0416 (b) from B. thetaiotaomicron cultured in 10% tryptone, followed by treatment with vehicle (Veh), lactose (Lac) or cholesterol (Chol) (n = 6 per condition). c–f, Targeted metabolomics performed on GF mice, GF mice monocolonized with B. thetaiotaomicron harbouring either BT_0416-competent (WT: BtΔtdk) or -null (KO: BtΔtdkΔ0416) strains, and CONV-D mice (n = 10 per condition). Chol^Sulf measurements from caecal content (c), stool (d) and whole blood (e) of GF, KO, WT and CONV-D mice. f, Serum total cholesterol measurements of GF, KO, WT and CONV-D mice. Bar chart values are mean ± s.d. and statistical analyses were performed using one-way analysis of variance with Tukey’s multiple comparison correction. NS, P > 0.05; *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001.

Fig. 6. B. thetaiotaomicron-derived Chol^Sulf is taken up into hepatic portal vein blood circulation.

Ion chromatograms of Chol^Alk-Sulf in a B. thetaiotaomicron strain that produces Chol^Alk-Sulf (WT: BtΔtdk(Chol^Alk) cell pellet, black), mice exposed to a B. thetaiotaomicron strain null in sulfotransferase activity (KO: BtΔtdkΔ0416, grey; n = 4), and mice exposed to WT sulfotransferase competent B. thetaiotaomicron (WT: BtΔtdk, pink; n = 5).

Extended Data Fig. 1. Schematic detailing the BOSSS methodology used to identify dietary cholesterol-interacting microbes and their metabolic transformations of cholesterol.

a, An alkyne-modified form of cholesterol is orally introduced to mice which allows for the tracking of dietary cholesterol and derivatives of dietary cholesterol into the microbiome of mice. Isolated microbiome samples from caecal contents are b, sorted to separate interacting (Alk+) versus non-interacting (Alk-) microbes in order to c, identify these microbes using metagenomic sequencing. d, Separately, metabolomes from unsorted microbiome samples from cholesterol-alkyne exposed mice can be compared to metabolomes from mice exposed to native cholesterol to determine differential features found in the cholesterol alkyne exposed state. These features are then queried in the native cholesterol state to make sure that the transformation is not an artifact of the alkyne modification of cholesterol.

Extended Data Fig. 2. Development of FACS gates for detecting and isolating cholesterol-interacting gut microbiota using Eubacterium coprostanoligenes.

a, Fecal microbes were freshly extracted from murine faecal pellets, spiked with E. coprostanoligenes cultured with Chol^Alk and click-stained with AF647-azide (E. cop^Alk). b, Illustration of the FACS gating strategy aiming to isolate cholesterol-interacting gut microbes from complex gut microbiome populations. Representative scatter plots of different ratios of spiked-in E. cop^Alk to faecal microbes validate the gating stringency suitable for downstream FACS application on the in vivo microbial samples.

Extended Data Fig. 3. Ex vivo culture medium selection.

a, The top 30 amplicon sequence variants (ASVs) of the microbial composition of the original mice faecal samples and that of 2-day ex vivo cultures using different media for the corresponding faecal pellets. Different colored circles represent the relative abundance of the individual ASV that are represented above 1% in the original mice faeces. b, Ion chromatograms of cultures containing murine stool treated with either cholesterol alkyne, cholesterol, vehicle, or no treatment, demonstrating the formation of cholesterol alkyne sulfate. c, Ion chromatograms of cultures containing murine stool treated with either cholesterol (chol), vehicle, or no treatment, demonstrating the formation of cholesterol sulfate.

Extended Data Fig. 4. High-resolution tandem mass spectra of cholesterol alkyne sulfate illustrating sulfate fragment.

a, MS2 fragmentation pattern of cholesterol alkyne sulfate and b, MS2 spectrum of cholesterol alkyne sulfate from caecal content of mice treated with cholesterol alkyne.

Extended Data Fig. 5. Bacteroides genus converts cholesterol to cholesterol sulfate.

Ion chromatograms of cholesterol alkyne sulfate in cultures incubated with cholesterol (Chol) or cholesterol alkyne (Chol^Alk) in order to determine cholesterol-interacting taxa that are able to convert cholesterol to cholesterol sulfate. Bacteroides species were displayed on the left to emphasize their ability to convert cholesterol to cholesterol sulfate, with other bacterial strains that failed to do the conversion arranged on the right.

Extended Data Fig. 6. Coprostanol can be sulfated by Bacteroides thetaiotaomicron.

a, Ion chromatograms and b, tandem mass spectra of axenic cultures of B. thetaiotaomicron with either cholesterol or coprostanol revealing that coprostanol can also be sulfated by B. thetaiotaomicron to produce coprostanol sulfate. c, MS1 spectra of coprstanol sulfate (†) and cholesterol sulfate (‡) demonstrating the parent ion m/z difference between the two metabolites. (†) and (‡) denote coprostanol sulfate and cholesterol sulfate respectively.

Extended Data Fig. 7. Phylogenetic tree representing organisms which contain Bt_0416 or Bt_0416-like genes.

Red highlights Bt_0416 and blue highlights others Bacteroides species. The BLASTP analysis was performed using the KEGG BLAST tool with the top 50 resulted selected and subjected to the TREE function. Sequences were aligned using MAFFT and phylogenetic inferences were obtained using the maximum likelihood method within the tool. Scale denoted on the top of the plot indicates the nucleotide sequence divergence.

Extended Data Fig. 8. Colonization of germ-free mice orally inoculated with strains of B. thetaiotaomicron.

a, Monoassociation colonization efficiencies of BtΔtdk and BtΔtdkΔ0416 as measured via stool sample colony forming units (c.f.u) (n = 10, P > 0.05: ns, determined by a two-sided Student’s t-test). b, Bacterial load in the cecal content of monocolonized and conventionalized mice as measured by 16 S rDNA copies (n = 10, P > 0.05: ns, determined by one-way ANOVA with Tukey posttest). Source data

Extended Data Fig. 9. Exogenous cholesterol does not differentially affect growth of BtΔtdkΔ0416 as compared to BtΔtdk.

OD₆₀₀ of BtΔtdk or BtΔtdkΔ0416 cultures in minimal media supplemented with 1% Tween 80 (to increase Chol solubility) treated with various concentrations of Chol. The highest Chol concentration represents the saturation point for Chol for the culture medium. Values are the mean ± s.d. (n = 3 biologically independent cultures per condition for each concentration). Source data

Extended Data Fig. 10. Human gut microbes convert cholesterol to cholesterol sulfate.

Ion chromatograms of cholesterol alkyne sulfate demonstrating that ex vivo cultures of stool from human donors can convert cholesterol alkyne (Chol^Alk) to cholesterol alkyne sulfate (Chol^Alk-Sulf).