Gut microbiota from coronary artery disease patients contributes to vascular dysfunction in mice by regulating bile acid metabolism and immune activation

Background The gut microbiota was shown to play a crucial role in the development of vascular dysfunction, and the bacterial composition differed between healthy controls and coronary artery disease patients. The goal of this study was to investigate how the gut microbiota affects host metabolic homeostasis at the organism scale. Methods We colonized germ-free C57BL/6 J mice with faeces from healthy control donors (Con) and coronary artery disease (CAD) patients and fed both groups a high fat diet for 12 weeks. We monitored cholesterol and vascular function in the transplanted mice. We analysed bile acids profiles and gut microbiota composition. Transcriptome sequencing and flow cytometry were performed to evaluate inflammatory and immune response. Results CAD mice showed increased reactive oxygen species generation and intensive arterial stiffness. Microbiota profiles in recipient mice clustered according to the microbiota structure of the human donors. Clostridium symbiosum and Eggerthella colonization from CAD patients modulated the secondary bile acids pool, leading to an increase in lithocholic acid and keto-derivatives. Subsequently, bile acids imbalance in the CAD mice inhibited hepatic bile acids synthesis and resulted in elevated circulatory cholesterol. Moreover, the faecal microbiota from the CAD patients caused a significant induction of abnormal immune responses at both the transcriptome level and through the enhanced secretion of cytokines. In addition, microbes belonging to CAD promoted intestinal inflammation by contributing to lamina propria Th17/Treg imbalance and worsened gut barrier permeability. Conclusions In summary, our findings elucidated that the gut microbiota impacts cholesterol homeostasis by modulating bile acids. In addition, the CAD-associated bacterial community was shown to function as an important regulator of systemic inflammation and to influence arterial stiffness.


Figure S2 | PWV measurement details.
The left common carotid artery in the mouse imaged at 40 MHz, from the aortic arch to the bifurcation. Transit-time measurements were performed at distal and proximal sites, located 1.5 mm upstream from the bifurcation and 1 mm downstream from the aortic arch, respectively. Typical pulsed-wave doppler waveform measured at the proximal measurement location. The arrival time of the velocity upstroke relative to the ECG R-wave peak is denoted T1. Pulsed-wave doppler waveform observed at the distal measurement location. The arrival time is denoted T2. a Expression of genes involved in bile acid transporters in the ileum. b Expression of genes involved in Fxr signaling in the ileum. c Ileal FGF15 protein levels. Means ± SEM are plotted; * P<0.05, ** P<0.01, *** P<0.001, Mann-Whitney U test. n = 11 or 12 for each group. GF, germ-free mice; Con, GF mice colonized with microbiota from healthy donors; CAD, GF mice colonized with microbiota from CAD patients. Figure S4 | Gut microbiota taxonomic and functional composition between Con and CAD mice. a Gene richness in Con and CAD mice, boxes show the medians and the interquartile ranges (IQRs). b α-diversity analysis showing Con mice was characterized by lower microbial richness in Simpson indexes based on genera profiles relative. * P<0.05, Wilcoxon rank sum test. c Alterations in gut microbial functional modules in Con and CAD groups. Dashed lines indicate a reporter score of 1.96, corresponding to 95% confidence in a normal distribution. d Boxplot of bacterial baiB gene abundance in HC donors and CAD patients and the spearman correlations between bacterial baiB gene abundance and serum LCA levels. * P<0.05, Wilcoxon rank sum test. e Spearman correlations between species abundance and serum cholesterol level. The colour represents positive (red) or negative (blue) correlations, and FDRs are denoted as follows: * FDR < 0.05, ** FDR < 0.01. n = 11 or 12 for each group.

Figure S5 | Transcriptional results of KEGG enrichment analysis of DEGs in the liver and ileum between CAD and Con groups, respectively.
Dot size indicate gene ratio for each KEGG pathway. n = 3 for each group.   a CD4 + IFN-γ + T cells (CD3e + CD4 + CD8a − IFN-γ + ) distributions of Con and CAD groups in small intestine. b CD4 + RORγt + T cells distributions of Con and CAD groups in small intestine. c CD4 + IL-22 + T cells (CD3e + CD4 + CD8a − IL-22 + ) proportions of Con and CAD groups in the spleen and lamina propria, respectively. n = 10-12 for each group. siLPLs, small intestine lamina propria lymphocytes.

Sample preparation of bile acid profiles
The stock solution of bile acids was mixed and prepared in bile acid-free serum matrix to obtain a series of bile acid calibrators at a concentration of 2500, 500, 250, 50, 10, 2.5, or 1 nM. Quality control samples were prepared in BAFUM at three different concentrations of 1500, 150, and 5 nM, respectively. Internal Standard (IS) concentrations were kept constant at all the calibration points (150 nM for GCA-d4, TCA-d4, TCDCA-d9, UDCA-d4, CA-d4, GCDCA-d4, GDCA-d4, DCA-d4, LCA-d4, and β-CA-d5). Internal standards were added to monitor the data quality and compensate for matrix effects.

Analytical Quality Control Procedures
The rapid turnover of many intracellular metabolites makes immediate metabolism quenching necessary. The extraction solvents are stored in -20°C freezer overnight and added to the samples immediately after the samples were thawed. We use icesalt bath to keep the samples at a low temperature and minimize sample degradation during sample preparation. All the prepared samples should be analyzed within 48 hours.
Reproducible and accurate results are critical for quantitative metabolomics work. To achieve this, three types of quality control samples i.e., test mixtures, stable isotope-labelled internal standards, and quality controls at three different levels (low, middle, and high) are routinely used in our metabolomics platform. In addition to the quality controls, solvent blank samples are also required for obtaining optimal instrument performance.
The BAP kit assay includes a test mixture comprising all of the bile acid reference standards. The test samples were analyzed at the beginning and end of each batch run to ensure that the instruments were performing within laboratory specifications (retention time stability, chromatographic peak shape, and peak signal intensity). The retention time shift for a batch of 84 samples should be within 4 sec. and the difference of peak intensity should be within 15%.
Internal standards were added to the test samples in order to monitor analytical variations during the entire sample preparation and analysis processes. Three levels of QC samples (high, middle, and low concentrations) that are prepared in the BAFM are used to ensure control of each 96-well plate for BAP Ultra assay and analyzed in triplicates across the sample set. At least 67% (6 out of 9) of QC samples should be within 15% of their respective nominal value, 33% of the QC samples (not all replicates at the same concentration) may be outside 15% of nominal value but within 30%.
Reagent blank samples are a mixture of solvents used for sample preparation and are commonly processed using the same procedures as the samples to be analyzed. The reagent blanks serve as a useful alert to systematic contamination. As the reagent blanks consist of high purity solvents and are analyzed using the same methods as the study samples, they are also used to wash the column and remove cumulative matrix effects throughout the study.
The calibrators consist of a blank sample (matrix sample processed without internal standard), a zero sample (matrix sample processed with internal standard), and a series of seven concentrations covering the expected range for the metabolites present in the specific biological samples. LLOQ and ULOQ are the lowest and highest concentration of the standard curve that can be measured with acceptable accuracy and precision.