Effect of antibiotic treatment on the intestinal metabolome

Abstract

The importance of the mammalian intestinal microbiota to human health has been intensely studied over the past few years. It is now clear that the interactions between human hosts and their associated microbial communities need to be characterized in molecular detail if we are to truly understand human physiology. Additionally, the study of such host-microbe interactions is likely to provide us with new strategies to manipulate these complex systems to maintain or restore homeostasis in order to prevent or cure pathological states. Here, we describe the use of high-throughput metabolomics to shed light on the interactions between the intestinal microbiota and the host. We show that antibiotic treatment disrupts intestinal homeostasis and has a profound impact on the intestinal metabolome, affecting the levels of over 87% of all metabolites detected. Many metabolic pathways that are critical for host physiology were affected, including bile acid, eicosanoid, and steroid hormone synthesis. Dissecting the molecular mechanisms involved in the impact of beneficial microbes on some of these pathways will be instrumental in understanding the interplay between the host and its complex resident microbiota and may aid in the design of new therapeutic strategies that target these interactions.

Figures

FIG. 1.

Dynamics of killing and recovery of intestinal microbial populations upon antibiotic treatment. The numbers of microbial cells present in feces were determined through Sybr green staining before and several time points after mice received 20 mg of streptomycin through oral gavage. The numbers of mice used were 3 (0.25 and 1 day after treatment), 4 (0.5, 4, and 6 days after treatment), and 7 (0 and 2 days after treatment). ns, not significant (P > 0.05); *, P < 0.04; **, P < 0.002.

FIG. 2.

Antibiotic treatment has a profound impact on the chemical composition of feces. (A) The heat maps show the impact of streptomycin treatment on the levels of metabolites from mouse feces. Data were median centered using cluster analysis, and heat maps were constructed using Java TreeView (http://rana.lbl.gov/eisensoftware.htm). Masses are presented from lowest (top) to highest (bottom). Green, masses with signal intensities higher than the median; red, signal intensities lower than the median; black, missed values or values with no difference from the median signal intensity. Each of the letters above the map (A, B, C, and D) indicates one mouse used. Each letter corresponds to the two columns of data under it in the heat map due to the duplicate data acquisitions performed. (B) Distribution of metabolites affected, based on fold changes. Numbers inside and around the pie charts represent the percentage of the total number of metabolites affected.

FIG. 3.

Multiple metabolic pathways are affected by antibiotic treatment. Masses of interest were searched against the KEGG database using MassTrix (http://masstrix.org). Bars indicate the percentage of metabolites from each KEGG pathway that was affected by treatment.

FIG. 4.

Bile acid metabolism is disturbed by antibiotic treatment. (A) Schematic of the bile acid synthetic pathway. Red, metabolites decreased by antibiotic treatment; green, metabolites increased upon treatment; black, metabolites not detected or unchanged. Masses (Da) for metabolites affected are shown in parentheses. Solid arrows, direct steps; dashed arrows, multiple steps that are not shown. (B) Levels of masses affected by antibiotic treatment. Masses (Da) are shown at the top of each graph. The y axis indicates mass signal intensity. Dark gray bars, levels before treatment; light gray bars, levels after antibiotic treatment. Four mice (n = 4) were used, and averages with standard errors of the means are shown. Because masses 392.2925, 408.2876, 430.3083, 432.324, and 499.2965 were detected in both positive and negative ionization modes, the intensities of these masses in both ionization modes were combined before analysis (n = 8). All differences were statistically significant (P < 0.05). CoA, coenzyme A.

FIG. 5.

Antibiotic treatment disrupts steroid hormone metabolism. (A) Schematic of the steroid hormone metabolic pathway. Red, metabolites decreased by antibiotic treatment; green, metabolites increased upon treatment; black, metabolites not detected or unchanged. Masses (Da) for metabolites affected are shown in parentheses. Solid arrows, direct steps; dashed arrows, multiple steps that are not shown. (B) Levels of masses affected by antibiotic treatment. Masses (Da) are shown at the top of each graph. The y axis indicates mass signal intensity. Dark gray bars, levels before treatment; light gray bars, levels after antibiotic treatment. Four mice (n = 4) were used, and averages with standard errors of the means are shown. All differences were statistically significant (P < 0.025).

FIG. 6.

Antibiotic treatment has a profound impact on eicosanoid hormone metabolism. (A) Schematic of the eicosanoid hormone metabolic pathway. Red indicates metabolites decreased by antibiotic treatment whereas green indicates metabolites increased upon treatment. Black indicates metabolites not detected or unchanged. Masses (Da) for metabolites affected are shown in parentheses. Solid arrows indicate direct steps and dashed arrows indicate multiple steps that are not shown. (B) Levels of masses affected by antibiotic treatment. Masses (Da) are shown at the top of each graph. The y axis indicates mass signal intensity. Dark gray bars, levels before treatment; light gray bars, levels after antibiotic treatment. Four mice (n = 4) were used, and averages with standard errors of the means are shown. Mass 326.2093 was detected in both positive- and negative-ion modes, and therefore, its intensity values from both ionization modes were combined before analysis (n = 8). All differences were statistically significant (P ≤ 0.006). PG, prostaglandin; LT, leukotriene; TX, thromboxane; EET, epoxyeicosatrienoic acid; HETE, hydroxyeicosatetraenoic acid; HPETE, hydroperoxyeicosatetraenoic acid; DHET, dihydroxyeicosatrienoic acid.

FIG. 7.

Clinically relevant doses of antibiotics affect eicosanoid metabolism. (A) Fecal levels of multiple eicosanoids were measured through ELISAs before and after high-dose streptomycin treatment. Dark gray bars, levels before treatment; light gray bars, levels after antibiotic treatment. Averages with standard errors of the means are shown. The numbers of mice used were as follows: CysLT before treatment, 11; CysLT after treatment, 6; PGE2 before and after treatment, 4 each; PGF2α before and after treatment, 6 each; LTB4 before and after treatment, 6 each. (B) Fecal levels of LTB4 were measured in groups of untreated mice and mice treated with clinically relevant doses of streptomycin (Strep), metronidazole (Met), vancomycin (Van), and tetracycline (Tet). Each dot represents one mouse, and bars indicate the averages of the results. **, P < 0.009; ***, P < 0.0001.