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. 2018 Jul 20;9(1):2872.
doi: 10.1038/s41467-018-05336-9.

Antibiotic-induced microbiome depletion alters metabolic homeostasis by affecting gut signaling and colonic metabolism

Amir Zarrinpar  1   2   3   4 , Amandine Chaix  5 , Zhenjiang Z Xu  6   7   8 , Max W Chang  9   10 , Clarisse A Marotz  6   7 , Alan Saghatelian  11 , Rob Knight  6   7   8 , Satchidananda Panda  12
Affiliations

Antibiotic-induced microbiome depletion alters metabolic homeostasis by affecting gut signaling and colonic metabolism

Amir Zarrinpar et al. Nat Commun. .

Abstract

Antibiotic-induced microbiome depletion (AIMD) has been used frequently to study the role of the gut microbiome in pathological conditions. However, unlike germ-free mice, the effects of AIMD on host metabolism remain incompletely understood. Here we show the effects of AIMD to elucidate its effects on gut homeostasis, luminal signaling, and metabolism. We demonstrate that AIMD, which decreases luminal Firmicutes and Bacteroidetes species, decreases baseline serum glucose levels, reduces glucose surge in a tolerance test, and improves insulin sensitivity without altering adiposity. These changes occur in the setting of decreased luminal short-chain fatty acids (SCFAs), especially butyrate, and the secondary bile acid pool, which affects whole-body bile acid metabolism. In mice, AIMD alters cecal gene expression and gut glucagon-like peptide 1 signaling. Extensive tissue remodeling and decreased availability of SCFAs shift colonocyte metabolism toward glucose utilization. We suggest that AIMD alters glucose homeostasis by potentially shifting colonocyte energy utilization from SCFAs to glucose.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
AIMD depletes the gut microbiome. a Stool cultures from AIMD mice yielded fewer colonies, n = 5/group. For box plot, center is mean, box is 25th to 75th percentile, and whiskers are 5th to 95th percentile. Kruskal–Wallis test, *p < 0.05, **p < 0.01. b, c 16S results show that AIMD mice had a decrease in OTUs detected from the Firmicutes and Bacteroidetes phyla, and an increase in Proteobacteria phyla, n = 6–8/group, mean percent abundance (with SEM). These differences were significant as assessed by ANCOM. d 16S results showing the shifts in microbiome in each treatment condition. e Principal coordinate analysis of the gut microbiome, n = 6–8/group. Fecal specimens were collected approximately 2 weeks after intervention
Fig. 2
Fig. 2
AIMD improves glucose tolerance and insulin sensitivity. a Blood glucose levels after 4 or 16 h of fasting. b, c Blood glucose concentration after IP injection of a glucose bolus (1 g/kg BW) (GTT) (b) and area under the curve quantification (c). d Blood glucose levels after IP injection of insulin (0.75 U/kg BW) (ITT). e Fasting serum insulin levels. f Body weight before and after the start of antibiotics gavage. g Cumulative food consumption (kcal) after acclimation to gavage. h Body composition as a percentage of total body weight. f–h were replicated in a separate experiment (Supplementary Figure 2). GTT and glucose measures were performed 3 weeks after intervention, and ITT was performed 4 weeks after intervention. Unpaired Student’s t test, **p < 0.01, ***p < 0.001, ns nonsignificant. All error bars are SEM
Fig. 3
Fig. 3
AIMD affects SCFA and BA profiles in the cecum. a Absolute quantification of the level of different SCFA in the cecal content. Insert: close-up of butyrate levels. Absolute quantification of primary (b) and secondary BAs (c) in the feces. CA cholic acid, aMCA α-muricholic acid, bMCA β-muricholic acid, TCA taurocholic acid, TaMCA tauro-α-muricholic acid, TbMCA tauro-β-muricholic acid, TCDCA taurochenodeoxycholic acid, DCA deoxycholic acid, HCA hyocholic acid, wMCA ω-muricholic acid, TDCA taurodeoxycholic acid, TUDCA tauroursodeoxycholic acid, TLCA taurolithocholic acid, THDCA taurohyodeoxycholic acid. Fecal samples were collected 2–3 weeks after intervention. Mann–Whitney U test, **p < 0.01, ***p < 0.001. All error bars are SEM
Fig. 4
Fig. 4
AIMD increases GLP-1 and affects other gut hormones. a Fasting serum level of total GLP-1 (n = 9–10/group). b Serum level of active GLP-1 after 16 h of fasting (fasted) or 15 min after an oral bolus of glucose (1 g/kg BW; fed). Blood was collected on Ddp4 inhibitor-coated tubes (n = 5/group). c Quantification by RNA-sequencing of Ggc and Pcsk7 mRNA expression in the cecum (n = 7–8/group). Adjusted p value from negative binomial Wald test (corrected for multiple hypothesis testing with the Benjamini–Hochberg method), *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. d–g Serum endocrine panel—plasmatic levels of GIP (d), ghrelin (e), leptin (f), and glucagon (g) (n = 9–10/group). h Serum level of total GLP-1 in GF mice (n = 4/group). i, j AIMD or vehicle mice were IP injected Exendin-9 (250 nmol/kg) or saline 20 min prior to receiving an oral bolus of glucose (1 g/kg BW) and glucose concentration was monitored over 100 min (n = 8/group). Quantification of the area under the curve (AUC, j). Serum collection and oGTT were performed at 1 week after intervention. Unless otherwise stated, Student’s t test, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, ns nonsignificant. All error bars are SEM
Fig. 5
Fig. 5
AIMD induces major changes in the transcriptome of the cecum. a PCA analysis of the cecum RNA-sequencing data (n = 7/8/groups). b, c Functional annotation of differentially expressed genes using IPA. The top 15 canonical pathways (b) and the top 11 biological functions (c) are shown
Fig. 6
Fig. 6
Anaerobic glycolysis in the cecum is upregulated in AIMD mice. a Heatmap of differentially expressed genes involved in sugar and lipid metabolism in the cecum of vehicle and AIMD mice as assessed by RNA-sequencing analysis (n = 7–8/group). b Mapping of metabolic genes onto specific pathways, including gluconeogenesis and glycolysis, β-oxidation, ketone body metabolism, TCA cycle, fatty acid synthesis, maturation and storage. Adjusted p value from negative binomial Wald test (corrected for multiple hypothesis testing with the Benjamini–Hochberg method), *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. c Quantification by qRT-PCR of Glut2, Gck, G6pc, Pcx, and Pgc1a mRNA expression in the liver (n = 8/group). Tissue collected 4 weeks after intervention. Mann–Whitney U test, *p < 0.05, **p < 0.01, ***p < 0.001, ns nonsignificant. All error bars are SEM
Fig. 7
Fig. 7
AIMD alters BA metabolism which can potentiate GLP-1 response. a Quantification by RNA-sequencing of Slc10a2, Fxr, Rxra, and Slc51b mRNA expression in the cecum (n = 7–8/group). Adjusted p value from negative binomial Wald test (corrected for multiple hypothesis testing with the Benjamini–Hochberg method), *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. b, c Absolute quantification of primary (b) and secondary BAs (c) in the serum. Mann–Whitney U test, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. d Quantification by qRT-PCR of Slc10a1, Fxr, Shp, and Cyp7a1 mRNA expression in the liver (n = 8/group). Mann–Whitney U test, *p < 0.05, **p < 0.01, ***p < 0.001, ns not significant. e Serum GLP-1 level after 4 days of bi-daily TCA gavage (400 mg/kg) (n = 4/group). Tissue and serum samples from AIMD studies collected at 4 weeks after intervention. Serum from TCA study was collected after 1 week intervention. Student’s t test, *p < 0.05. All error bars are SEM
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