Microbial glutamate metabolism predicts intravenous cocaine self-administration in diversity outbred mice

Abstract

The gut microbiome is thought to play a critical role in the onset and development of psychiatric disorders, including depression and substance use disorder (SUD). To test the hypothesis that the microbiome affects addiction predisposing behaviors and cocaine intravenous self-administration (IVSA) and to identify specific microbes involved in the relationship, we performed 16S rRNA gene sequencing on feces from 228 diversity outbred mice. Twelve open field measures, two light-dark assay measures, one hole board and novelty place preference measure significantly differed between mice that acquired cocaine IVSA (ACQ) and those that failed to acquire IVSA (FACQ). We found that ACQ mice are more active and exploratory and display decreased fear than FACQ mice. The microbial abundances that differentiated ACQ from FACQ mice were an increased abundance of Barnesiella, Ruminococcus, and Robinsoniella and decreased Clostridium IV in ACQ mice. There was a sex-specific correlation between ACQ and microbial abundance, a reduced Lactobacillus abundance in ACQ male mice, and a decreased Blautia abundance in female ACQ mice. The abundance of Robinsoniella was correlated, and Clostridium IV inversely correlated with the number of doses of cocaine self-administered during acquisition. Functional analysis of the microbiome composition of a subset of mice suggested that gut-brain modules encoding glutamate metabolism genes are associated with the propensity to self-administer cocaine. These findings establish associations between the microbiome composition and glutamate metabolic potential and the ability to acquire cocaine IVSA thus indicating the potential translational impact of targeting the gut microbiome or microbial metabolites for treatment of SUD. This article is part of the Special Issue on "Microbiome & the Brain: Mechanisms & Maladies".

Keywords: Behavior; Cocaine; Diversity outbred; IVSA; Microbiome; Novelty; Sex differences.

Fig. 1.

Novelty behaviors are associated with the acquisition of IVSA. A) The twelve open field measures that differed between ACQ and FACQ. From top left to lower right. Total distance traveled, total time in corner, total time in perimeter, total time in center, total resting time in corner, total resting time, percent resting time in center, total distance traveled in corner, toral distance traveled in the perimeter, total distance traveled in the center, the total distance traveled in the last 5 min and the total ambulatory time. B) The two light-dark metrics that differed between ACQ and FACQ, the percent resting time in the light and the percent time in the light C) The hole board total entries and the preference for a novel arena differed between ACQ and FACQ mice. D) Sex specificity of novelty behaviors associated with the acquisition of IVSA. Three open field measures were significantly different in females, the distance time (as a percent) in the center, and, the percent distance and time in the perimeter. ACQ is in blue and FACQ is in orange. Outliers have been removed from the phenotype data, and the values rank z transformed. *p < 0.05, **p < 0.01.

Fig. 2.

Microbial composition of the ACQ vs. FACQ groups of DO mice. A) The percent abundance of the 25 most abundant genera in female and male mice ACQ and FACQ mice. B) Principal components analysis of bacterial beta diversity at OTU level using Bray-Curtis for 16 S microbiome data showing the ACQ, FACQ, and male and female groupings.

Fig. 3.

Differential gut microbiome composition of ACQ and FACQ mice. A) Barnesiella, B) Clostridium IV, C) Ruminococcus, D) Robinsoniella, E) Sex-dependent associations of Blautia and F) Lactobacillus. G) Correlation between Robinsoniella abundance and doses of cocaine administered during the acquisition phase. H) Correlation between Clostridium IV abundance and doses of cocaine administered during the acquisition phase.

Fig. 4.

Novelty behaviors associated with microbial abundance in the DO mice A. Generalized linear model results for specific effects of the microbiome on behavior in DO mice. The GLM fit was Behavior_i ~ Age + Sex + Genus_j B) Lasso regression analysis of the microbiomes as predictors of behavior.

Fig. 5.

Network analysis of ACQ and FACQ at OTU level: interactions between microbe-microbe and microbe-behavior. The network was built on filtered 50% OTU prevalence of DO mice cohort using SPIEC-EASI. Each OTU and each novelty behavior was considered as a node in the network. Abbreviations in network: novelty place preference (NPP1), percent distance in the center (OFA13), percent distance in the perimeter (OFA15), percent time in the corner (OFA17), percent resting time in the center (OFA19), percent resting time in the corner (OFA20), percent resting time in the perimeter (OFA21), percent resting time in the light (LD3), Number of infusions at 1.0 mg/kg (IVSA).

Fig. 6.

Volcano plots of the functional categories encoded by the microbiome. A. A scatterplot showing the statistical significance and magnitude of change of the KO clusters as determined by PICRUSt2 of 16S data. B. The KO categories (p < 0.05) from PICRUSt2 of 16S data. C. A scatterplot of the statistical significance and magnitude of change of the KO clusters determined by mWGS. D. The KO categories (p < 0.05) from mWGS data. E. A scatterplot of the statistical significance and magnitude of change of the mWGS Pathways F. The mWGS Pathways (p < 0.05) blue = significant KO/pathway (p value < 0.05), red = significant KOs that are GBM (p value < 0.05). G. H.