Effects of Vancomycin and Ciprofloxacin on the NMRI Mouse

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Effects of Vancomycin and Ciprofloxacin on the NMRI Mouse Metabolism Zhigang Liu, Bing Xia, Jasmina Saric, Jurg Utzinger, Elaine Holmes, Jennifer Keiser, and Jia V. Li J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.8b00583 • Publication Date (Web): 05 Sep 2018 Downloaded from http://pubs.acs.org on September 7, 2018

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Journal of Proteome Research

Effects of Vancomycin and Ciprofloxacin on the NMRI Mouse Metabolism

Zhigang Liu,†,# Bing Xia,†,# Jasmina Saric,‡,§ Jürg Utzinger,‡,§ Elaine Holmes,† Jennifer Keiser,‡,§ and Jia V. Li†,*

†

Division of Integrative Systems Medicine and Digestive Disease, Department of Surgery and

Cancer, Faculty of Medicine, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom ‡

Swiss Tropical and Public Health Institute, P.O. Box, CH-4002, Basel, Switzerland

§

University of Basel, P.O. Box, CH-4003, Basel, Switzerland

#

These two authors contributed equally to the work reported here

* Corresponding author. Email: [email protected]; Telephone: +44 207 5943230

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ABSTRACT: The reduction in gut microbiota diversity is associated with a range of human diseases. Overuse of antibiotics has been associated with a diminished gut-microbial diversity in humans, and may promote microbiota-associated negative effects to physical health, such as the metabolic syndrome-cluster of diseases and mental illnesses. There is a pressing need to deepen the understanding of the effects of antibiotics at the biochemical level. The current study investigated metabolic effects of two widely prescribed antibiotics – vancomycin and ciprofloxacin – on biofluids and brain tissue samples of NMRI female mice, using a 1H nuclear magnetic resonance (NMR) spectroscopy-based metabolic profiling approach. While both antibiotics significantly affected the host metabolic signatures of urine and feces, only ciprofloxacin induced metabolic changes in plasma. Metabolic perturbations were pronounced one day post-treatment, reverting back to baseline at day 20 post-treatment. Both antibiotics induced changes in the choline metabolism, host-microbial co-metabolites, short chain fatty acid production, and protein/purine degradation. The metabolic profiles of brain tissue aqueous extracts did not show any antibiotics-related changes by day 20 post-treatment. The data suggest that the metabolic disruptions in biofluids caused by antibiotics are reversed by day 20 post-treatment when compared to the pre-treatment profiles, and that there is no noticeable effect on the brain metabolic profile.

KEYWORDS: antibiotics, ciprofloxacin, vancomycin,

1

H nuclear magnetic resonance (NMR)

spectroscopy, metabolic profiling, multivariate statistical analysis

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INTRODUCTION The mammalian digestive tract contains approximately 38 trillion microbes composed of up to 1000 species, the majority of which are bacteria.1, 2 These gut microbes are an indispensable part of the mammalian homeostasis, and most of them exhibit symbiotic relationships with the host during a life-long co-evolutionary process.3 Although there are strong inter-person differences between the gut microbial composition, within a healthy adult, there is surprisingly little variation over time. The mammalian gut provides a nutrient-rich habitat for these microorganisms; in turn, the microbes and microbial metabolites, such as oligosaccharides, short-chain fatty acids (SCFAs), and amino acids, can shape and enhance the host’s immune system, gut health, food digestion, and nutrient absorption.4 Recent evidence suggests that microbial activity and gut-microbial interactions are involved in human health and disease. Most notably, they have been linked to the development of inflammatory bowel disease, obesity, diabetes, and autism via multiple metabolic networks, such as energy homeostasis, lipid metabolism, and immune regulation.3, 5, 6 It has also been shown that bidirectional signaling between the gastrointestinal tract and the brain, the so-called ‘gut–brain axis’, involves various afferent and efferent pathways such as the vagus nerve signaling and the hypothalamic–pituitary–adrenal pathway to regulate aspects of homeostasis such as satiety and hunger, and inflammation.7,8 In addition, the microbial colonization process has been shown to initiate signaling mechanisms that affect neuronal circuits involved in motor control and anxiety behavior.9 The same study also reported a significantly higher turnover rate of noradrenaline, dopamine, and serotonin in the brain of germ-free mice compared with specific pathogen-free mice with a normal gut microbiota.9 Recently, antibiotic exposure has been reported to be associated with increased risk of depression.10 Biotech companies have mushroomed in recent years to explore and exploit the potential of gut microbiota (GMB)-based treatments, moving from fecal GMB transplants inserted by colonoscopy to oral capsules. However, the single biggest challenge in maintaining a healthy GMB composition and function arises from the over- and misuse of antibiotics, for medical, veterinary, and agricultural purposes, that has been going on for more than half a century. Hence, additional mechanistic information on the GMB networks that are disrupted by a given antibiotics treatment may strengthen the case for tightening drug prescription guidelines and policies for agricultural and veterinary use. 2 ACS Paragon Plus Environment


Vancomycin is a type of glycopeptide antibiotic that is usually used to treat Gram-positive bacterial infections, such as endocarditis, septicaemia, and pseudomembranous colitis caused by penicillin-resistant Staphylococcus aureus and Clostridium difficile.11, 12 Ciprofloxacin, on the other hand, is a synthetic second-generation fluoroquinolone antibiotic that exhibits broad-spectrum antibacterial activities.13 It can cause irreversible nerve damage, tendon damage, cardiac effects, pseudomembranous colitis, and rhabdomyolysis, among other adverse events.14 Previously, we co-assessed the effects of vancomycin on the murine metabolic profile and the gut microbial diversity, to unravel the influence of the Gram-negative bacterial population on the host metabolism. We found that decreased fecal excretion of uracil, amino acids, and SCFAs was associated with vancomycin treatment, highlighting the contribution of the GMB to the production and metabolism of these dietary compounds.15 The current study aimed to characterize the metabolic disturbances of two widely prescribed antibiotics in more detail, by (1) assessing the metabolic differences in the effects of vancomycin and ciprofloxacin and (2) studying metabolic trajectory along a 20-day recovery in biofluids from NMRI female mice, using a metabolic profiling approach. In addition, based on the aforementioned link between the brain and the gut microbiota, the metabolic profiles of the brain tissue were also assessed following antibiotic treatment.

MATERIALS AND METHODS Experimental Design and Sample Collection The investigations in the animal model were pursued at the animal research facility of the Swiss Tropical and Public Health Institute (Swiss TPH; Basel, Switzerland), adhering to Swiss animal welfare regulations (permission no. 2081). A total of 17 female outbred NMRI mice, aged 3 weeks, were purchased from RCC (Füllinsdorf, Switzerland) and kept under standard environmental conditions (temperature, 22 °C; humidity, 60–70%; day/night cycle, 12/12 h) for 2 weeks for acclimatization prior to the start of the experiment. All mice were fed with standard rodent food and water ad libitum. The mice were randomly divided into three groups and received two dosages of antibiotics or drug vehicle orally at 8 a.m. and 8 p.m.: (1) five animals were treated with drug vehicle (7% Tween 80 and 3% ethanol) serving as a control (Ctr) group; (2) six mice were treated with vancomycin hydrochloride (2 x 100 mg/kg plus drug vehicle), designated as Van group; and (3) six mice were treated with ciprofloxacin hydrochloride (2 x 100 mg/kg plus drug vehicle), designated as Cip group. The dosage for mice was converted using the equation: Animal dose 3 ACS Paragon Plus Environment

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(mg/kg) = Human equivalent dosage (mg/kg) × (Human Km/Mouse Km),16 where Km, the correction factor estimated by dividing the average body weight (kg) of species to its body surface area (m2), is 37 and 3 for human and mice, respectively. To monitor antibiotic-induced metabolic changes and recovery, plasma, urinary, and fecal samples were collected from mice at three time points (day 1 pre-treatment [D-1], and days 1 [D1] and 20 [D20] post-treatment). Our previous studies have shown the most significant metabolic changes 1 day after antibiotics treatment and the recovery after 3 weeks. Urinary (≥20 µL) and fecal (1 to 2 pellets) samples were collected into Petri dishes by gently rubbing the mouse abdomen. Approximately 50 µL of blood was collected from the tail of each mouse into a heparincoated capillary tube and centrifuged at 3600 g for 10 min. A total of 25 µL of plasma was then transferred into a 1.5 mL Eppendorf tube. All samples were placed on dry ice immediately after collection and stored at -80°C. On day 20, animals were culled by cervical dislocation. The brains were collected and separated into frontal cortex, cerebellum, brain stem, and rest (all remaining brain tissue) and snap-frozen in liquid N2 within 5 min of culling. Sample Preparation for 1H Nuclear Magnetic Resonance (NMR) Spectroscopy Fecal samples were homogenized in 500 µL water and underwent three cycles of sonication (10 min, 25°C) and vortexing (20 s) to maximize the solubility of metabolites. Then, the samples were centrifuged at 10 000 g for 10 min and 400 µL of supernatant was transferred into 1.5 mL Eppendorf tubes. The extraction was repeated on the remaining sediment using the same procedure and another 400 µL of supernatant was taken out and combined with the previous one. The resulting fecal water extracts were dried using a speed vacuum (Eppendorf concentrator plus, V-AL model) centrifuging for 10 h at 30°C. A volume of 200 µL of water and 450 µL of 0.2 M sodium phosphate buffer, which contained 100% D2O serving as field lock, 0.01% 3-trimethylsilyl1-[2, 2, 3, 3-d4] sodium propionate (TSP) for spectral calibration, and 3 mM antibacterial reagent sodium azide (NaN3), were added to each sample, sonicated and vortexed until all extracts in the tubes were dissolved. The tubes were subsequently centrifuged (10 min, 10 000 g) and 600 µL of supernatant was transferred into 5 mm (outer diameter) NMR tubes. Urine and plasma samples were thawed at room temperature and vortexed for 5 s to homogenize. A total of 30 µL of each urine sample was well mixed with 25 µL of the 4 ACS Paragon Plus Environment


aforementioned 0.2 M sodium phosphate buffer. The resulting mixture was centrifuged (10 min, 10 000 g) and 50 µL was subsequently transferred into 1.7 mm (outer diameter) NMR capillary tubes. For plasma samples, 25 µL was taken to be mixed with 30 µL of saline (0.9% NaCl in D2O), centrifuged at 10 000 g for 10 min, transferring 50 µL of the supernatant into 1.7 mm capillary NMR tubes pending NMR analysis. The brain tissue samples were placed into a 2 mL Eppendorf tube containing a metal ball, 0.5 mL of a pre-chilled chloroform/methanol mixture (v/v, 2:1) and 0.5 mL of pre-chilled water (HPLC-grade). The samples were then placed into a TissueLyser and shaken for 8 min at 25 Hz, followed by centrifugation at 4°C for 10 min at 10 000 g. The aqueous and lipid phases were separated and each was transferred into a new tube. The extraction procedure was repeated again on the remaining pellet and extracts from the same phase of the same sample were combined. The aqueous phase was dried in a speed vacuumed centrifuge at 40°C for 10 h. The organic phase was left in the fume hood to evaporate overnight. The dry aqueous extracts were resuspended in 600 µL of the sodium phosphate buffer and centrifuged for 10 min at 10 000 g, and 580 µL of supernatant was transferred into an NMR tube for NMR analysis. The organic phase was not analyzed since we were interested in the metabolites in the aqueous phase. 1

H NMR Spectroscopy of Biofluids

NMR detection of all biofluids was conducted using a Bruker DRX 600 spectrometer (Bruker; Rheinstetten, Germany) at 600.13 MHz operating at a temperature of 27°C. The NMR pulse sequence [recycle delay (RD)-90°-t1-90°-tm-90°-free induction decay (FID) acquisition] was set to monitor the standard 1-dimensional pulse sequence. An irradiation of 2 s during RD and a mixing time (tm) of 100 ms was used to suppress the water peak. For each sample, a total of 128 scans were accumulated into 64 k data points with a spectral width of 20 ppm. Prior to Fourier transformation, an exponential function equivalent to a line broadening of 0.3 Hz was used to multiply FIDs. In addition, plasma spectra were also acquired using a Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence [RD-90°-(τ-180°-τ)n-acquisition] to allow visualization of small molecular components without the substantial interference of macromolecular signals. The assignment of the peaks was analyzed by statistical total correlation spectroscopy (STOCSY)17 in MATLAB and published spectral databases.18, 19 Spectral Data Processing and Statistical Analysis of the Spectral Data 5 ACS Paragon Plus Environment

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Spectral phase and baseline were automatically corrected and referenced to the TSP peak at δ 0.0 using nmrproc version 0.3 (edited by Tim Ebbels, Imperial College London). The spectra (δ 0–10) were imported into MATLAB (version R2016a, The Mathworks, Inc.; Natwick, MA) with a resolution of 0.0005 ppm. Regions including the TSP peak (δ 0.0), water regions (δ 4.55–5.00), and the urea peak (δ 5.535–6.075) were cut out. Remaining spectra were aligned due to peak shifts likely to be caused by different sample pH values20 and probabilistic quotient normalization was applied. The processed spectral data were analyzed using a set of multivariate statistical methods initially composed of principal component analysis (PCA) and orthogonal projection to latent structure discriminant analysis (O-PLS-DA) in SIMCA-P+ version 15.0 (Umetrics, Sartorius Stedim Biotech). CV-ANOVA was applied to evaluate the robustness of the O-PLS-DA models and p-value

Effects of Vancomycin and Ciprofloxacin on the NMRI Mouse

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