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. Author manuscript; available in PMC: 2023 May 1.
Published in final edited form as: Alcohol. 2022 Jan 6;100:1–9. doi: 10.1016/j.alcohol.2021.12.003

SUPPLEMENTATION WITH SODIUM BUTYRATE PROTECTS AGAINST ANTIBIOTIC-INDUCED INCREASES IN ETHANOL CONSUMPTION BEHAVIOR IN MICE

RE Reyes 1,*, L Gao 1,*, Z Zhang 1,*, DL Davies 1, L Asatryan 1,$
PMCID: PMC8983552  NIHMSID: NIHMS1778171  PMID: 34999234

Abstract

Background:

We have recently reported that oral treatment of adult male C57BL/6J mice with a non-absorbable antibiotic cocktail resulted in an increase in ethanol intake and in significant reductions in butyrate-producing gut microbiota populations. This work led us to hypothesize that reduction in butyrate levels within the gut is linked to antibiotic-induced increases in voluntary ethanol consumption.

Objective:

This study tested whether ad libitum sodium butyrate supplementation can prevent antibiotic-induced ethanol consumption in mice.

Methods:

Sodium butyrate was provided to adult male C57BL/6J mice in drinking water alone or in combination with antibiotic cocktail. Effects on ethanol (20%) intake were measured using drinking in the dark and modified 2-bottle choice paradigms. Body parameters, food and liquid intake, cecum and adipose tissues were measured during and/or at the conclusion of the drinking in the dark study. Cecal 16s rRNA was analyzed for microbiota diversity and changes in specific bacterial phyla/species.

Results:

In drinking in the dark, sodium butyrate supplementation prevented antibiotic-induced increases in ethanol intake without altering basal ethanol consumption. Furthermore, sodium butyrate supplementation lowered ethanol preference in the 2-bottle choice study. Ethanol intake was correlated to specific bacterial phyla/species. Sodium butyrate did not affect the changes in microbiota diversity and composition induced by antibiotic cocktail.

Conclusions:

The findings support a role of gut microbiota-derived butyrate in regulating alcohol-induced behaviors. Additionally, the work contributes to efforts in development of novel microbiome-based strategies as novel preventative and intervention-based therapeutics to address alcohol use disorder.

Keywords: Gut-brain axis, antibiotic and sodium butyrate supplementation, ethanol intake and preference, gut microbiota diversity, short-chain fatty acids

Introduction

Alcohol is the most commonly misused substance in the world (Peacock et al., 2018), and its chronic abuse can lead to alcohol use disorder (AUD). Although widespread, AUD has only limited psychosocial and pharmacotherapeutic treatment options with low efficacy and compliance rates due to its poorly understood pathophysiology (Tawa, Hall, & Lohoff, 2016; Walters et al., 2018). These shortcomings illustrate the need to identify and develop novel strategies towards AUD prevention and treatment.

The gut microbiota is composed of various microorganisms, such as bacteria, archaea, fungi, and viruses, which populate the gastrointestinal environment throughout development. Approximately 90% of human gastrointestinal bacterial populations belong to Firmicutes and Bacteroidetes phyla, while other minor components belong to Actinobacteria, Fusobacteria, Proteobacteria and Verrucomicrobia populations (Sarin, Pande, & Schnabl, 2019). Building evidence suggests that, in addition to its known impacts on the liver and other organ systems, continuous alcohol abuse negatively impacts the gastrointestinal system and its native microbiota (Leclercq et al., 2014; Tobiasz et al., 2018; Zhang et al., 2019). Studies in both humans and rodent models observed chronic alcohol-induced bacterial overgrowth and dysbiosis within the gastrointestinal system (Bjørkhaug et al., 2019; Mutlu et al., 2012; Yan et al., 2011). Additional studies described alterations in the composition of the gut microbiome (the combined genetic material of microbiota; used interchangeably), i.e. dominant Bacteroidetes and Firmicutes phyla, following ethanol exposure (Wang et al., 2018). Alcohol consumption has also been shown to impair the intestinal barrier (Keshavarzian et al., 1999) and increase permeability by disrupting tight junctions, which can lead to translocation of proinflammatory/pathogenic microbial products, including endotoxin LPS, from the gastrointestinal lumen into circulation (Frazier, DiBaise, & McClain, 2011; Rao, Seth, & Sheth, 2004).

Our understanding of the “gut-brain axis” is beginning to reveal the intimate relationship between the gut microbiota and the brain and behavior. As such, studies provided links between changes in gut microbiota and alcohol-induced behaviors and alcohol dependence (Meroni, Longo, & Dongiovanni, 2019; Wang et al., 2018; Xu et al., 2019). In support of this, we recently reported that disrupting the gut microbiota composition with a non-absorbable antibiotic cocktail (ABX) resulted in an increased voluntary consumption of ethanol in adult male C57BL/6J mice in a binge-like drinking model (Reyes et al., 2020). ABX-treated mice also expressed altered gut microbiome, including significant decreases in diversity and large-scale reductions in Firmicutes populations (Reyes et al., 2020).

Growing evidence suggests that gut microbiota may take part in diverse host metabolic activities through metabolites (Rooks & Garrett, 2016; Schoeler & Caesar, 2019). Short-chain fatty acids are a major class of lipid metabolites resulting from bacterial fermentation of dietary fibers in the colon. Short-chain fatty acids such as butyrate, propionate and acetate can function as metabolic substrates and signaling molecules (Dinan & Cryan, 2017; Schoeler & Caesar, 2019), perform anti-inflammatory and anti-proliferative functions (Puertollano, Kolida, & Yaqoob, 2014). Firmicutes belonging Clostridium species, including those belonging to clusters IV, XIVa, and XVIII, are among the main producers of short-chain fatty acids (Narushima et al., 2014).

In our initial study (Reyes et al., 2020), we found a correlation between reduced short-chain fatty acid-producing microbiota populations and increased ethanol consumption in ABX-treated mice, suggesting a link between short-chain fatty acids and alcohol drinking behavior. In the current study, we set forth a hypothesis that butyrate administration in mice could prevent ABX-induced increases in voluntary ethanol consumption. To test this hypothesis, we administered C57BL/6J mice with sodium butyrate (SB) via ad libitum and determined its effect on ABX-induced voluntary ethanol consumption within the binge-like drinking-in-dark (DID) paradigm. A second drinking model, 2-bottle choice, was incorporated to investigate the effects of SB supplementation on ethanol preference.

Methods

Animals

Adult male C57BL/6J mice, aged 6–8 weeks (wks), were purchased from Jackson Labs (California, USA). Mice were single-housed and allowed to acclimate for at least 2 wks before being randomly assigned to a specific treatment group. Mice were housed under an automatic humidity, temperature and light controlled room and a reversed 12 hour (hr) light/dark cycle (lights on 12:00 am-12:00 pm). Facility-provided mouse chow and drinking water were available at all times, unless stated otherwise. Body weight (g) was measured 5 days a week (Monday-Friday) and monitored to ensure mouse health. Food consumption (g) and liquid intake (ml) were measured every other day at weekdays (Monday, Wednesday, Friday). Measurements were recorded during the light phase, in order to minimize influencing nocturnal dark phase activities. All animals were treated in accordance with the National Institutes of Health Guide for Care and Use of Laboratory Animals and protocols approved by the USC Institutional Animal Care and Use Committee.

ABX and SB treatments

We chose an ABX cocktail with limited bioavailability that had previously been shown to broadly reduce gut microbiota populations in adult rodents without entering circulation and directly affecting host tissues when administered orally (Bercik et al., 2011; Kiraly et al., 2016). The ABX cocktail, consisting of 0.5 mg/ml bacitracin (Sigma, USA), 2.0 mg/ml neomycin (GoldBio, USA), and 0.2 mg/ml vancomycin (Thermo Fisher, USA) was added to drinking water for the duration of the experiment. The anti-fungal pimaricin (1.2 μg/ml) was also added to the ABX cocktail solution, in order to decrease gut fungal overgrowth due to prolonged antibiotic use. SB (Sigma, USA) was provided to the mice ad libitum. SB solution was prepared in drinking water at concentration of 8 mg/ml. For ABX and SB (ABX+SB) co-treatment, SB was added to freshly prepared ABX solution. The SB and ABX+SB solutions were pH matched to ABX solution in order to control for acidity effects on consumption. All solutions were freshly prepared every 2 days.

Drinking in the dark (DID) ethanol seeking-behavior

Mice were randomly assigned to 4 treatment groups: H2O, ABX, SB and ABX+SB as shown in Table 1. We assigned more animals to the SB and ABX+SB groups compared to H2O and ABX groups (11 vs 9), because we had performed several studies with ABX treatment and have already reported consistent findings (Reyes et al., 2020). All mice had 24 hr access to their respective drinking bottles for 2 wks before the start of ethanol experiments and throughout the duration of the study, except during ethanol exposure hours. Vivarium-provided rodent chow and a single bottle of their respective drinking was continuously available throughout the duration of the study except during ethanol exposure hours. The DID model is widely used to assess differences in binge-like drinking behaviors. We used a modified version of this procedure in our current study where mice had daily limited access (2 hr) to one bottle containing 20% ethanol beginning at 3 hrs into the circadian dark phase (3:00pm to 5:00pm) 5 days a week (Monday-Friday) for 4 wks. This approach was shown to maintain consistent high ethanol intake levels within this strain of mice (Reyes et al., 2020). During ethanol exposure, each drinking bottle was exchanged with a designated bottle containing 20% ethanol. Ethanol intake (ml) was recorded at the end of each session.

Table 1. Treatment groups for the DID study.

There were 4 treatment groups - with water (H2O), antibiotic cocktail (ABX), sodium butyrate (SB) and combination of ABX and SB (ABX+SB). After 2 weeks of respective treatments, mice had daily 2 hr access to a bottle, containing 20% ethanol solution (denoted as 20E), 5 days a week (M-F) for 4 wks.

Treatment (24 hrs) Drinking in the Dark (DID) Study (M-F, 3–5 pm) Sample Size
Drinking bottle Bottle n
H2O 20E 9
ABX 20E 9
SB 20E 11
ABX+SB 20E 11
2 wks 4 wks
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Limited access 2-bottle choice procedure for ethanol preference

In addition to intake levels, we tested the effects of SB on ethanol taste preference using a modified schedule of 2-bottle choice model (Rhodes et al., 2005). A separate cohort of adult male C57BL6 mice were randomly assigned to 4 treatment groups: H2O, ABX, SB and ABX+SB as shown in Table 2. All mice were single-housed and had 24 hr access to their respective drinking bottles with or without treatment for 2 wks before the start of the 2-bottle choice procedure. Vivarium-provided rodent chow was always available throughout the study. During the procedure, mice had limited access to two bottles (2 hr/day during the dark phase as opposed to the regular 24 hr access (Yuri A. Blednov et al., 2005), one with their assigned treatment solution (Bottle 1 - H2O, ABX, SB or ABX+SB) and the second one containing 20% ethanol solution (Bottle 2 – 20E). The access to 2 bottles started at 3 hrs into the dark cycle and lasted for 2 hrs (3:00 – 5:00 pm), 5 days a week (Monday-Friday) for 2 wks, paralleling the schedule used during the DID studies. Testing of mice in the SB group was extended to compare their preference for SB versus sucrose (Table 2). During the 2 hr access with 2 bottles, mice had access to SB containing bottle (Bottle 1) and a bottle containing either H2O or 5% sucrose in the drinking water (Bottle 2). Each bottle contained 10–12 ml of drinking liquid that was freshly made every two days. The position of the test and control bottles was alternated daily to avoid any potential positional preference. After 2 hrs, test and control bottles were replaced with daily drinking bottles. Liquid intake (ml) was recorded at the end of each session.

Table 2. Treatment groups for the 2-bottle choice study.

There were 4 treatment groups - with water (H2O), antibiotic cocktail (ABX), sodium butyrate (SB) and combination of ABX and SB (ABX+SB). After 2 wks of treatments, mice had 2 hr daily access to 2 bottles, one with their respective treatment solution (Bottle 1) and the second one (Bottle 2) containing 20% ethanol solution (denoted as 20E), 5 days a week (Mon-Fri) for 2 wks. For comparing taste preferences for SB versus sucrose, mice had access to 2 bottles, one of which contained SB solution, the other – H2O or 5% sucrose.

Treatment (24 hrs) 2-Bottle Choice Study (Bottle 1, 2 = M-F, 3–5 pm) Sample size
Drinking bottle Bottle 1 Bottle 2 N
H2O H2O 20E 5
ABX ABX 20E 5
SB SB 20E 6
ABX+SB ABX+SB 20E 6
SB SB SB 6
SB H2O Sucrose 6
2 wks 2 wks
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Serum blood ethanol concentration (BEC) levels

Following 2 wks of H2O, ABX, SB and ABX+SB treatments, mice were administered an intraperitoneal injection of ethanol at a concentration of 3.5 g/kg per mouse weight (n=5–7). At 45 min after each injection, mice were euthanized, blood collected, serum prepared and stored in −80°C until further analysis. Serum samples were processed on the ANALOX AM1 machine according to manufacturer’s instructions (Analox Instruments Ltd., UK).

16S rRNA metagenomic sequencing and taxonomic analysis of gut microbiome metagenomes

Cecum samples (n= 6/group) were collected, weighed and stored in −80°C until analysis. DNA isolated from cecal contents were analyzed using Mag-Bind Universal Pathogen DNA Kit (Omega Bio-tek, Inc., Norcross, GA) and quantified using QuantiFlour dsDNA System (Promega, Madison, WI). DNA (12.5 ng) was input into Illumina’s 16S metagenomics Library preparation workflow. The V3-V4 region of bacterial 16S rRNA gene sequences were amplified using primer pairs containing gene-specific sequences and Illumina adapter overhang nucleotide sequences (Table 3).

Table 3.

16S sequencing reaction system

Primers Forward 5’-TCGTCGGCAGC GTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG
Reverse 5’-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGG TATCTAATCC
Reaction System DNA 12.5 ng sample DNA per input
ReadyMix 12.5 μL 2x KAPA HiFi HotStart ReadyMix (Kapa Biosystems, Wilmington, Massachusetts)
Primer 5 μL 1 μM of each primer
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The PCR products were enriched from the reaction mix with Mag-Bind RxnPure Plus magnetic beads (Omega Bio-tek, Norcross, GA). A second index PCR amplification, used to incorporate barcodes and sequencing adapters into the final PCR product, was performed in 25 μl reactions, using the same conditions as described above. Cycling parameters were as follows: 95°C for 3 minutes, followed by 8 cycles of 95°C for 30”, 55°C for 30” and 72°C for 30”. A final, 5 minutes’ elongation step was performed at 72°C.

Libraries were standardized with Mag-Bind® EquiPure Library Normalization Kit (Omega Bio-tek, Norcross, GA), then pooled. The pooled library ~600 bases in size was checked utilizing an Agilent 2200 TapeStation and sequenced (2 × 300 bp paired-end read setting) on MiSeq (Illumina, San Diego, CA). Amplified 16S DNA sequences, separated by sample/treatment and also observed as aggregated, were initially analyzed and visualized using BaseSpace app 16S Metagenomics (Illumina, San Diego, CA). Classification was performed using RDP Naive Bayes taxonomic classification algorithm.

Data analyses

Animal behavior-based dependent variables included ethanol intake (g/kg/2 hrs or ml/g/2 hrs for DID or 2-bottle choice study, respectively), liquid (H2O/ABX/SB/ABX+SB) intake (ml/g/day), food intake (g/g/day), and body weight (g), compared using two-way repeated measures (RM) ANOVA. One-way ANOVA analyses were performed on cecum weights and BEC levels with significance level set at p < 0.5.

For DID, ethanol intake was calculated as g/kg [g of pure ethanol per kg of body weight; 20E intake = (ml of 20E consumed × 0.15786 g/ml)/body weight in kg]. For preference, ethanol intake was compared to total volume consumed during the 2 hrs and presented as percentage (%) [20E consumed in ml/(20E consumed in ml + treatment bottle liquid consumed in ml)/body weight in g]. Two-way ANOVA or RM ANOVA was used to evaluate the main effect of daily ABX administration with or without SB co-treatment on DID and preference (%). Significant differences between treatment groups were assessed using Tukey’s post-hoc multiple comparison test.

Significant differences in microbiome diversity and specific cecal bacterial populations between treatment groups were determined using the non-parametric Kruskal-Wallis test wherein post-hoc multiple comparisons were conducted while controlling the False Discovery Rate (FDR) method of Benjamini and Hochberg correction set at 5% (FDR q-values). Non-parametric Spearman r correlations, two-tailed with 95% CI, between total ethanol consumption, 4 wks DID (20E g/kg/ 40hrs), and microbiota phyla population (p_Firmicutes, p_Actinobacteria, p_Bacteroidetes, p_Verrucomicrobia) were also performed.

All data are presented as Mean ± Standard Error Mean (SEM) for each group with post hoc multiple comparisons displayed as *p < 0.05, **p < 0.01 and ***p < 0.001. Graphs and statistical analyses were produced using GraphPad Prism (GraphPad Software Inc., San Diego, CA).

Results

Sodium butyrate supplementation did not significantly affect weight or food consumption, but increased liquid intake

Adult mice were treated with H2O, ABX, SB or with combination of ABX and SB (ABX+SB) which were provided to the animals within the drinking bottles. All bottles were available continuously throughout the study, i.e. 2 wks prior to and throughout the 4 wks of DID exposure (except for during DID procedures, Table 1). All mice steadily gained weight throughout the study with no differences between groups in average weekly weights (Fig. 1A; two-way RM ANOVA: F(3,36) = 0.85, p = 0.47) and food consumption (Fig. 1B; two-way RM ANOVA: F(3,36) = 0.5, p = 0.69). No significant differences were detected in the liquid intake among H2O, ABX and ABX+SB treatment groups, while SB-treated mice drank significantly more from their respective bottle compared to the other 3 treatment groups (Fig. 1C; two-way RM ANOVA: F(3,36) = 7.7, p < 0.001).

Figure. 1. Changes in physiological parameters of mice after treatments.

Figure. 1.

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A) Weekly body weights and B) food intake did not significantly differ between any of the 4 groups during the DID study. C) SB mice drank higher levels of their daily treated liquid (SB in water only) throughout the study compared to the other 3 treatment groups. D) (Top images) Post-mortem necropsy images displaying ABX-driven cecum enlargement (black arrows) and the reduced appearance of adipose (white stars). (Bottom figure) Mice in ABX and ABX+SB groups exhibited significantly increased cecum weights compared to both non-ABX treated groups (H2O and HSB). Data are presented as Mean ± SEM, n = 9–11/group, cecum n= 4–5/group; *p < 0.05, **p < 0.01, ***p < 0.001.

Less prominent adipose tissue was observed, and ceca were engorged with significantly heavier weights in mice in ABX and ABX+SB groups compared to mice in H2O and SB groups (Fig. 1D - Top and Bottom; one-way ANOVA: ***p < 0.001, ABX and ABX+SB mice compared to H2O and SB mice).

Sodium butyrate supplementation prevented ABX-induced increases in ethanol intake

DID exposure for the total duration of 4 wks significantly increased ethanol consumption in mice in the H2O group (Fig. 2A; two-way RM ANOVA: F(3,36) = 32, p < 0.01). In the ABX group, mice consistently consumed significantly higher amounts of ethanol compared to H2O mice, with a mean consumption of 7.9 g/kg/2 hrs and 4.7 g/kg/2hrs, respectively. This effect was observed immediately after 2 wks of ABX treatment. SB treatment did not affect ethanol consumption compared to H2O group (p = 0.42, SB vs H2O). Importantly, combined with ABX, SB treatment significantly lowered ethanol intake levels compared to mice with ABX treatment alone, with a mean consumption of 5.8 g/kg/2hrs and 7.9 g/kg/2hrs, respectively for ABX+SB and ABX groups.

Figure. 2. The effects of SB supplementation on both ethanol intake in the DID and ethanol preference in the 2-bottle choice paradigm and in BEC levels.

Figure. 2.

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A) In the voluntary binge-like DID model, ABX-treated mice consumed higher levels of 20E which was damped by ABX and SB co-treatment. B) (Left panel) In the modified 2-bottle choice study, SB supplementation decreased the ethanol preference without and with ABX treatment. There was no significant difference in ethanol preference between H2O and ABX groups. (Right panel, left) Mice consumed slightly higher amounts of liquid from the SB bottle compared to H2O bottle but this difference was not significant. (Right panel, right) In the same modified 2-bottle choice study, the SB preference was 10-fold lower than the preference for 5% sucrose. C) ABX treatment with and without SB supplementation did not significantly affect serum BEC levels in mice 45 mins following intraperitoneal injection of ethanol (3.5 g/kg per mouse weight). Data are presented as Mean ± SEM, n = 9–11/group for DID study, n = 5–6 for preference study and BEC measurements, n = 5–7/group for 2-bottle choice study; *p < 0.05, **p < 0.01.

Sodium butyrate supplementation reduced ethanol preference within the 2-bottle choice model

To preclude potential SB taste preference effects that could account for reduction in ethanol intake in DID with ABX+SB treatment, we tested ethanol preference within a 2-bottle choice model. We used a modified schedule of our previously used DID schedule wherein mice were exposed to 2-bottles for 2 hrs Monday-Friday for the duration of 2 wks. In this paradigm, mice in all treatment groups consumed significantly more ethanol compared to their daily liquid intake from all treatments (Tukey’s post-hoc multiple comparisons: p < 0.01; intake from treatment bottle 1 vs 2; ml/g (mouse weight)/2 hrs). Furthermore, SB treatment lowered the ethanol preference without and with ABX treatment (Fig. 2B, Left panel; two-way ANOVA: F(3, 16) = 13.7; p < 0.001 for SB vs H2O and p < 0.05 for SB+ABX vs ABX). Interestingly, there was no significant difference in ethanol preference between H2O and ABX groups. Lastly, we tested whether the SB effects in the 2-bottle choice were not due to increased taste preference for SB containing bottle. To accomplish this, we tested mice for their preference for SB in the 2-bottle choice study by comparing preference for SB vs. H2O as well preference for SB vs 5% sucrose (Table 2). Mice consumed slightly higher amounts of liquid from the SB bottle compared to H2O bottle, however this difference was not significant for any of the days tested for the duration of 2 weeks (Fig. 2B – Right panel (left); two-way RM ANOVA: F(1,10) = 2.4, p = 0.156). The preference for SB was dramatically lower (10-fold) than the preference for 5% sucrose (Fig. 2B – Right panel (right); two-way RM ANOVA: F(1,10) = 4267: p < 0.001).

Additionally, we investigated whether any of the treatments led to significant changes in BECs. This was accomplished by testing a separate cohort of mice after 2 wks of H2O, ABX, SB and ABX+SB treatments. We found that BECs measured in sera collected 45 mins after intraperitoneal injections with a single dose of ethanol at 3.5 g/kg of mouse weight were not significantly different between treatment groups Fig. 2C; one-way ANOVA: (F(3,25) = 1.02: p = 0.4).

Sodium butyrate supplementation did not significantly affect ABX-induced reductions in cecal microbiome diversity and butyrate-producing bacteria populations

To investigate the effects of SB supplementation on the microbiome, we isolated cecal tissues at the end of 4 wks of DID exposure and analyzed 16S RNA sequences. In this study we weren’t able to determine the effect of ABX treatment on the total abundance of microbiota due to the lack of methods for sample standardization for composition studies; however our previous study showed a ~90% elimination of the cecal microbiota after 2 weeks of ABX treatment (Reyes et al., 2020). The diversity among the bacterial populations was significantly reduced as determined through the α-diversity Shannon Index (Fig. 3A). ABX treatment, with or without SB, independently promoted reductions in diversity, indicating taxa richness and evenness were significantly different between ABX-treated groups and controls (Fig. 3A, Kruskal-Wallis test, Benjamini and Hochberg comparisons, p < 0.01 for ABX vs H2O and for SB+ABX vs ABX). In addition, ABX- and ABX+SB-treated mice had significantly less unique number of species within the microbiome compared to H2O and SB groups (Fig. 3B, Kruskal-Wallis test, Benjamini and Hochberg comparisons, p < 0.01 for ABX vs H2O and p < 0.05 for SB+ABX vs ABX).

Figure. 3. The cecal microbiome diversity (A), unique species (B) and changes in butyrate-producing microbiota populations (C) with different treatments evaluated at the end of the DID paradigm.

Figure. 3.

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ABX treatment, with or without SB, drove reductions in diversity and within genus populations of Firmicutes bacteria. A) SB treatment (SB and ABX+SB groups) did not affect α-diversity compared to non-SB treated mice (H2O and ABX groups) according to the Shannon Index measure. B) Similarly, SB treatment (SB and ABX+SB groups) did not affect the number of unique species found within cecal samples compared to H2O and ABX groups. C) SB treatment did not affect the number of g_Lachnospiraceae, g_Clostridium IV, and g_Clostridium XIVa found within cecal samples compared to H2O and ABX groups, respectively. Data are presented as Mean ± SEM, n = 6/group, *p < 0.05, **p < 0.01.

There were also differences in the genus populations of Firmicutes bacteria between groups treated with ABX and those without ABX treatment. At the genus-level, butyrate-producing bacteria (Lachnospiraceae, Clostridium IV, and Clostridium XIVa) were significantly reduced in ABX and ABX+SB mice compared to H2O and SB groups (Fig. 3C, Kruskal-Wallis test, Benjamini and Hochberg comparisons, p < 0.01 for ABX vs H2O and p < 0.05 for SB+ABX vs ABX for all populations). SB treatment (SB and ABX+SB groups) did not affect butyrate-producing microbiota populations found within cecal samples compared to non-SB treated mice (Fig. 3C, Kruskal-Wallis test, p > 0.5 for all comparisons).

Correlations between total ethanol consumption and bacterial populations

Total ethanol intake data (g/kg/40 hrs) was analyzed per mouse over the 4 wks of DID exposure from H2O, ABX, SB and ABX+SB groups and correlated to the population counts of Firmicutes, Actinobacteria, Bacteroidetes and Verrucomicrobia phyla (Fig. 4A–D; Spearman r correlation, *p < 0.05, **p < 0.01; counts of bacteria per phyla population compared to total ethanol consumption. Ethanol intake was found to be significantly inversely correlated with Firmicutes and Actinobacteria population counts (Fig. 4A–B; Spearman r correlation, r = −0.6, **p < 0.01 for both) and significantly positively correlated with Bacteroidetes and Verrucomicrobia populations (Fig. 4C–D; Spearman r correlation, r = 0.5 *p < 0.05 and r = 0.7 **p<0.01, respectively).

Figure. 4. Correlation between total ethanol intake and microbiome phyla populations.

Figure. 4.

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Accumulated 20E was calculated per mouse and averaged within treatment groups (H2O – white circles, ABX – black circles, SB – white triangles, ABX+SB – black triangles) across the 4 wks of DID exposure. ABX-treated mice (ABX and ABX+SB) clustered similarly and separately from non-ABX mice (H2O and SB) in terms of total 20E intake and counts of bacterial populations. Total 20E intake significantly correlated with reduced bacterial counts of phyla A) Firmicutes B) and Actinobacteria. Conversely, total 20E intake significantly correlated with increased counts of C) Bacteroidetes and D) Verrucomicrobia. Data are presented as Mean ±SEM, Non-parametric Spearman r correlations, n = 6/group; *p < 0.05, **p < 0.01.

Discussion

We recently demonstrated that adult male C57BL/6J mice, orally administered an antibiotic cocktail, drank higher ethanol within the DID paradigm (Reyes et al., 2020), indicating an increase in binge-like drinking behaviors. ABX-treatment also resulted in significant reductions in cecal butyrate-producing bacteria within the Firmicutes phyla. These findings led us to hypothesize that reduction in butyrate levels within the gut was linked to ABX-induced increases in alcohol consumption in mice. In the current study, we tested this hypothesis using an approach of oral SB supplementation in combination with ABX treatment of C57BL/6J mice. We chose the SB concentration of 8 mg/ml of drinking water given ad libitum due to its previously demonstrated behavioral impact in addition to its role as a histone deacetylase inhibitor (Mayer et al., 2019).

Consistent with our previous findings, ABX treatment rapidly induced a significant increase in ethanol intake within the first week of DID. SB supplementation prevented the ABX-induced increase in ethanol intake with the effect occurring immediately during the first week and lasting throughout the duration of the study. Notably, SB supplementation did not alter the basal ethanol intake level, occurring in the H2O group. This finding agrees with results of a previously published study in which intraperitoneal injection of SB did not affect operant intermittent ethanol self-administration in alcohol non-dependent rats (Simon-O’Brien et al., 2015). On the other hand, SB administration had been shown to reduce ethanol consumption in alcohol dependent rats, especially during the drinking escalation phase (Simon-O’Brien et al., 2015). In agreement with this latter finding, we found that SB reduced ethanol intake escalated by ABX treatment. Effects of SB supplementation in the limited access 2-bottle choice model somewhat differed from the findings in the DID. In this paradigm, SB significantly reduced ethanol preference in the presence as well as absence of ABX treatment. Of note, H2O and ABX mice had similar ethanol preference, in contrast to ABX-enhanced ethanol intake during the DID. Differences in ethanol consumption using these two different protocols (DID and 2-bottle choice) have been demonstrated before (Rhodes et al., 2005). These procedures are sensitive to several factors, like number of bottles, access period, food and drink availability before and during the tests (Rhodes et al., 2005). For example, introduction of the second treatment containing bottle during the 2-bottle choice procedure may have caused differences in the drinking behavior. Previous studies have demonstrated that increasing the number of sipper tubes increased the level of ethanol consumption in the continuous access paradigm (Tordoff & Bachmanov, 2003a, 2003b). However, it has also been recognized that the overall ethanol consumption is reduced when introducing a second water bottle during the 2 hr access period in a limited access 2-bottle choice approach (Rhodes et al., 2005). We have used a similar limited access approach and our findings agree with previous observation (Rhodes et al., 2005). At the baseline, mice drank significantly less ethanol in the 2-bottle choice procedure as compared to ethanol intake in the DID (1.67 ± 0.27 vs 4.68 ± 0.15 g/kg 20E). These considerations may explain the observed differences in the behavioral outcomes in the two ethanol intake procedures with different pharmacological treatments in our study.

During the DID study, mice demonstrated an increased intake of the liquid solution containing SB compared to groups provided with either water or ABX suggesting a possibility for higher preference for SB serving as the source for the reduced ethanol intake/preference. We tested this possibility in the limited access 2-bottle choice study and found slightly increased preference of mice for SB compared to water, however this difference was not significant and was dramatically different from the preference towards sucrose (10-fold). These data suggest that the increase in SB taste preference is negligible and is unlikely to have an impact on SB-induced reduction of ethanol intake and preference observed in respectively DID and 2-bottle choice studies.

The reducing effects of SB supplementation on ethanol intake in the DID and ethanol preference in the 2-bottle choice study were also not due to treatment-induced effects on BECs. As such, we found similar serum ethanol concentrations at 45 min following a single bolus intraperitoneal injection of ethanol (3.5 g/kg of mouse weight) within an ethanol-naïve cohort of H2O-, ABX-, SB-, and ABX+SB-treated mice. We would like to acknowledge the limitation of this study, wherein we tested BECs at one time point after administration. A future, more rigorous study testing time-dependent changes in BEC levels is required to drive conclusions on the effects of SB on ethanol metabolism.

The ability of SB to reduce ethanol preference and dampen ABX-related increase in ethanol intake supported the integral role of butyrate-producing microbiota in these interactions. We therefore compared cecal microbiome 16S rRNA sequences between treatment groups to further elucidate potential microbiota-drinking behavior relationships. In agreement with our previous work (Reyes et al., 2020), we found that ABX treatment resulted in significant reductions in microbiome diversity and butyrate-producing bacteria. The total ethanol intake correlated with specific changes in phyla population abundances, with negative correlation with Firmicutes and positive correlation with Bacteroidetes and Verrucomicrobia phyla. In addition, we identified a significant negative correlation between total ethanol consumption and the titers of Actinobacteria with ABX treated groups (ABX and ABX+SB). This phylum has been shown to be enhanced in rodents consuming high-fat diet as well as in obese twins in a human study (P. J. Turnbaugh, Bäckhed, Fulton, & Gordon, 2008; Peter J. Turnbaugh et al., 2009). The reduction of Actinobacteria in our ABX and ABX+SB mice may account for the observed reductions in the adipose tissue. Therefore, further studies will investigate the role of changes in adiposity, insulin sensitivity, and glucose as they relate to ethanol intake behaviors.

Although some rodent models of disease studies have shown the ability of SB treatment to modify gut microbiota composition (Fang, Xue, Chen, Chen, & Ling, 2019; Ma et al., 2020), we did not see an effect of SB on microbiome diversity or composition over the ABX-induced changes within our study, while drinking behavior in SB groups showed significant changes. This is most probably related to continuous presence of ABX cocktail during the course of the study that suppressed bacterial growth and overrode any potential influences of SB on different microbiota populations and/or their interactions. These findings also indicate that orally-administered butyrate is able to affect ethanol intake behaviors through direct or indirect signaling pathways independent of microbiome composition, emphasizing the important role of metabolites within the gut-brain communication.

Dysregulation of the gut microbiota (dysbiosis) has been linked to abnormal neural development, neuroinflammation, and brain dysfunction (Dinan & Cryan, 2017). Gut-produced butyrate can function as the main energy source for colonic enterocytes and plays an important role in the maintenance of gut-barrier integrity in order to block the translocation of microbial-produced endotoxins such as LPS (Ørgaard, Jepsen, & Holst, 2019; Silva, Ferguson, Avila, & Faciola, 2018). Once leaked into circulation, LPS is able to induce systemic immune activation which has also been shown to increase ethanol intake behaviors in rodent models (Y. A. Blednov et al., 2011; de la Cuesta-Zuluaga et al., 2018). Butyrate is also a known histone deacetylase inhibitor and may exert its action directly on the brain by crossing the blood-brain barrier (Mitchell, On, Del Bigio, Miller, & Hatch, 2011). The multifaceted abilities of butyrate to affect host activities opens up several potential mechanisms to explore in regards to its beneficial effects on ABX-induced ethanol intake. Investigation of these mechanisms were not within the scope of the current study but are part of ongoing and future investigations. Our ongoing studies have already demonstrated a potential in the neuroinflammatory pathways underlying the effects of SB; these findings which will be the focus of the next paper.

Conclusion

In conclusion, our current study demonstrated that butyrate produced in the intestines by beneficial microbiota species can counter-affect ABX-induced increases in voluntary ethanol consumption in a binge-like drinking paradigm. This is supported by the findings demonstrating the ability of SB to reduce both ethanol intake (DID) and preference (2-bottle choice) in mice. Ongoing and future studies will focus on specific mechanisms of SB action responsible for the observed reduction in ethanol intake and support its potential as a target candidate towards the mitigation of alcohol abuse. Work in this direction will further strengthen the importance of the gut microbiome-brain axis as it relates to alcohol consumption behaviors and therapeutic management of AUD.

HIGHLIGHTS.

  • Sodium butyrate supplementation protected from antibiotic-induced increased ethanol intake without altering basal ethanol consumption

  • Sodium butyrate supplementation lowered ethanol preference in 2-bottle choice study

  • Sodium butyrate did not affect reduction in bacterial diversity or composition caused by antibiotic treatment

Acknowledgements

We want to thank undergraduate students Catalina Vu and Rachel Paik for assistance with cecal DNA isolations; MS student Maryam Alsaeed for help with tissue collections.

Funding

This work was funded by Rose Hills Foundation Innovator Award (USC; to LA), NIAAA R01AA022448 (NIAAA, to DLD), USC School of Pharmacy and USC Good Neighbors.

List of abbreviations:

ABX

antibiotic cocktail

AUD

alcohol use disorder

BEC

blood ethanol concentration

DID

drinking in the dark

SB

sodium butyrate

20E

20% ethanol

LPS

lipopolysaccharide

Footnotes

Declaration of competing interest

The authors declare no conflict of interest.

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