Abstract
Antibiotic tolerance, the ability of a typically susceptible microorganism to survive extended periods of exposure to antibiotics, has a critical role in chronic and recurrent bacterial infections, and facilitates the evolution of antibiotic resistance. However, the physiological factors that contribute to the development of antibiotic tolerance, particularly in vivo, are not fully known. Despite the fact that a high-fat diet (HFD) is implicated in several human diseases, the relationship between HFD and antibiotic efficacy is still poorly understood. Here, we evaluated the efficacy of multiple clinically relevant bactericidal antibiotics in HFD-fed mice infected with methicillin-resistant Staphylococcus aureus (MRSA) or Escherichia coli. We found that HFD-fed mice had higher bacterial burdens and these bacteria displayed lower susceptibility to bactericidal antibiotic treatment compared with mice that were fed a standard diet, while microbiota-depleted standard-diet- or HFD-fed mice showed similar susceptibility. Faecal microbiota transplantation from HFD-fed mice impaired antibiotic activity in mice fed a standard diet, indicating that alteration of the gut microbiota and related metabolites in HFD-fed mice may account for the decreased antibiotic activity. 16S rRNA sequencing and metabolomics analysis of faecal samples revealed decreased microbial diversity and differential metabolite profiles in HFD-fed mice. Notably, the tryptophan metabolite indole-3-acetic acid (IAA) was significantly decreased in HFD-fed mice. Further in vitro studies showed that IAA supplementation inhibited the formation of bacterial persisters and promoted the elimination of persisters in combination with antibiotic treatment, potentially through the activation of bacterial metabolic pathways. In vivo, the combination of IAA and ciprofloxacin increased the survival rate of HFD-fed mice infected with MRSA persisters. Overall, our data reveal that a HFD has an antagonistic effect on antibiotic treatment in a mouse model, and this is associated with the alteration of the gut microbiota and IAA production.
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Data availability
Metabolomics data have been deposited in Mendeley Data (https://doi.org/10.17632/hgskyckxb7.1). 16S rRNA gene and RNA-sequencing data have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (PRJNA722131 and PRJNA704755). Source data are provided with this paper.
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Acknowledgements
This work was supported by the National Key Research and Development Program of China (2018YFA0903400, to Y.L.), National Natural Science Foundation of China (32002331, to Y.L.; 31872526, to Z.W.), Natural Science Foundation of Jiangsu Province of China (BK20190893, to Y.L.), Agricultural Science and Technology Independent Innovation Fund of Jiangsu Province (CX(20)3091, to Y.L.), China Postdoctoral Science Foundation (2019M651984, to Y.L.), A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD, to Z.W.) and Lift Engineering of Young Talents of Jiangsu Association for Science and Technology (to Y.L.).
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Y.L. and Z.W. conceived and supervised the project. Y.L., K.Y., Y.J., J.S., Z.T., D.F., B.Y. and C.S. performed all of the experiments. Y.L., K.Y. and Y.J. performed antibiotic susceptibility determination, antibiotic efficacy evaluation, mouse infection studies, gut microbiota profiling, untargeted metabolomics studies, LC–MS/MS analysis and transcriptome assays. J.S. and Z.T. performed the persisters studies and ATP and ROS production assays. D.F., B.Y. and C.S. performed the pharmacokinetic studies. Y.L., K.Y., R.L. and X.X. analysed the data. Y.L. and K.Y. generated the figures and wrote the manuscript. All of the authors reviewed, revised and approved the final manuscript.
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Extended data
Extended Data Fig. 1 High-fat diet (HFD) partially boosts bacterial burden of E. coli B2 in mice organs, whereas has no effect on MRSA T144 infection.
CD-1 female mice fed with a standard diet or HFD after 64 days (n = 6 biologically independent animals per group) were intraperitoneally infected with MRSA T144 (a) or E. coli B2 (b, 1.0 × 107 CFUs per mouse). At 2 h or 24 h post-infection, organ samples including heart, live, spleen, lung and kidney were collected and the bacterial loads were determined by plating serial dilutions of sample homogenates on MHA. Data are presented as mean ± SD, and P values were determined using an unpaired, two-tailed Student’s t test. NS, not significant (P > 0.05).
Extended Data Fig. 2 HFD-fed mice displays lower susceptibility to intraperitoneal ciprofloxacin therapy.
CD-1 female mice fed with a standard diet or HFD (n = 6 biologically independent animals per group) after 64 days were intraperitoneally infected with MRSA T144 at a non-lethal dose (1.0 × 107 CFUs per mouse). At 2-hours post-infection, these infected mice were administered with a single intraperitoneal (i.p.) dose of ciprofloxacin (20 mg/kg) or PBS as control. At 24 h post-infection, mice were euthanized through cervical dislocation and the CFUs in organs, including lung, liver and kidney, were determined. CFUs reduction of bacterial loads in the organs of mice in the absence and presence of ciprofloxacin treatment was then calculated. P values were determined using an unpaired, two-tailed Student’s t test.
Extended Data Fig. 3 Effect of microbiota depletion on obesity model and pathogen loads in standard diet and HFD-fed mice.
a, Dynamic changes of body weight in microbiota-depleted mice after feeding with a standard diet or HFD during 64 days (n = 10 biologically independent animals per group). b, The increase of body weight in microbiota-depleted mice after 64 days of feeding with a standard diet or HFD (n = 10 biologically independent animals per group). c, Concentrations of serum biochemical factors, including TG, CHOL, HDL and LDL, in microbiota-depleted mice after 64-days of feeding with a standard diet or HFD (n = 10 biologically independent animals per group). d, MRSA T144 loads in microbiota-depleted standard diet or HFD-fed mice lungs at 24 h-post infection (n = 6 biologically independent animals per group). All data were expressed as mean ± SD, and P values were determined using an unpaired, two-tailed Student’s t test (NS, not significant).
Extended Data Fig. 4 Effect of microbiota transplantation on weight increase, serum biochemical factors and pathogen loads in mice fed with a standard diet (n = 6 biologically independent animals per group).
a, The increase of body weight in control and gut microbiota-transplanted mice after 64-days of feeding with a standard diet. b, Concentrations of serum biochemical factors, including TG, CHOL, HDL and LDL, in control and microbiota-transplanted mice after 64-days of feeding with a standard diet. c, MRSA T144 loads in control and microbiota-transplanted mice lungs at 24 h-post infection. All data were expressed as mean ± SD, and P values were determined using an unpaired, two-tailed Student’s t test (NS, not significant).
Extended Data Fig. 5 Metabolomics analysis of fecal samples from mice fed with a standard diet or HFD.
Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) of positive and negative ionization dataset for mice fed with a standard diet or HFD. H, HFD-fed mice; S, standard diet-fed mice.
Extended Data Fig. 6 Differential metabolite profiles of fecal samples from mice fed with a standard diet and HFD.
a, Volcanic map of all differential metabolites and known metabolites in positive and negative ionization models (P < 0.05). Indole-3-acetic acid is shown. P values were determined using a two-sided, Fisher’s exact test with Benjamini-Hochberg multiple-testing correction. FC, fold change; nosig, no significant changes. b, Heatmap analysis of the fecal metabolite patterns in the standard diet and HFD-fed mice. 349 significantly changed known metabolites, including 92 up-regulated and 257 down-regulated metabolites, between the two groups were shown. These metabolites can be clustered into 10 subclusters. Each column in the figure represents a sample, and each row represents a metabolite. The color in the figure indicates the relative abundance of the metabolite in this group of samples. The colorbar showing blue to deep red colors indicates the relative content of metabolites. The tree diagram of metabolite clustering on the left and the name of metabolite on the right, the closer the two metabolites branches are, the closer their expression patterns are. The top is the tree diagram of sample clustering, and the lower is the name of the sample. The closer the two sample branches are, the closer the expression patterns of all metabolites in the two samples are. H_1 to H_6, HFD-fed mice; S_1 to S_6, standard diet-fed mice.
Extended Data Fig. 7 Decreased IAA concentrations in fecal samples from HFD-fed mice.
a, Dynamics of IAA concentrations in fecal samples from standard diet or HFD-fed mice during nine weeks of feeding, determined by HPLC-MS/MS. Data were presented as mean ± SD (n = 6 biologically independent animals per group), and P values were determined using a two-way analysis of variance (ANOVA) with Sidak’s multiple comparison test. (b and c) IAA concentrations in serum (b) and lung tissue samples (c) from mice fed with a standard diet or HFD after nine weeks of feeding. Data were presented as mean ± SD (n = 6 biologically independent animals per group), and P values were determined using an unpaired, two-tailed Student’s t test. d, Biosynthesis of IAA from tryptophan through the indole-3-acetamide pathway. Tryptophan is an essential amino acid for mammals, and is supplied by dietary protein by the aid of gut microbiota. Tryptophan 2-monooxygenase would catalyze the synthesis of indoleacetamide from tryptophan. Indoleacetamide hydrolase then converts indoleacetamide to IAA.
Extended Data Fig. 8
Drug-drug interaction between IAA and antibiotics against MRSA T144 (a) or E. coli B2 (b). Checkerboard assay was used to evaluate the drug-drug interactions between IAA and antibiotics. Each drug was serially diluted at eight concentrations to create an 8 × 8 matrix, and mixed with bacterial suspensions (1.5 × 106 CFUs per mL). After 18 h incubation at 37 °C, the absorbance at 600 nm was measured. Dark blue regions represent higher cell density. Data represent the mean OD (600 nm) of two biological replicates. FICI was calculated using the formula as follows: FICI = FICA + FICB; FICA = (MICA in the presence of B / MICA alone); FICB = (MICB in the presence of A / MICB alone). Synergism was defined with an FICI ≤ 0.5. FICI, fractional inhibitory concentration index.
Extended Data Fig. 9
KEGG analysis of differentially expressed genes (DEGs) of MRSA T144 after exposure to IAA at the beginning (a) or at the time point of 12 h (b) during 16 h of culture. The name of each KEGG pathway is shown on the left, and the total number of DEGs in this pathway are indicated on the right.
Extended Data Fig. 10
IAA alone triggers the production of ATP (a) and ROS (b) in MRSA T144 persister cells in a dose-dependent manner. The intracellular ATP level and ROS production in MRSA T144 after treatment with the increasing concentrations of IAA were determined. For ATP level analysis, MRSA T144 persister cells (OD600 = 0.5) were incubated with various concentrations of IAA ranging from 0 to 100 mM for 1 h. Subsequently, the supernatant was removed and bacterial precipitates were lysed by lysozyme. After centrifugation, the supernatant was prepared for intracellular ATP level measurement using a firefly luciferase-based assay. An fluorescent probe 2′,7′-dichlorodihydrofluorescein diacetate (DCFH-DA, 10 µM) was used to evaluate the production of ROS in MRSA T144 persister cells after treatment with different concentrations of IAA ranging from 0 to 100 mM. After 1 h incubation, the fluorescence intensity (λexcitation = 488 nm, λemission = 525 nm) was immediately determined using an Microplate reader. The mean of three biological replicates is shown and error bars represent the SD, with individual data points shown. P values were determined using a non-parametric one-way ANOVA. NS, not significant.
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Liu, Y., Yang, K., Jia, Y. et al. Gut microbiome alterations in high-fat-diet-fed mice are associated with antibiotic tolerance. Nat Microbiol 6, 874–884 (2021). https://doi.org/10.1038/s41564-021-00912-0
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DOI: https://doi.org/10.1038/s41564-021-00912-0
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