Alterations in the Gut Microbiota of Rats Chronically Exposed to Volatilized Cocaine and Its Active Adulterants Caffeine and Phenacetin
A role of the gut microbiota in influencing brain function and emotional disorders has been suggested. However, only a few studies have investigated the gut microbiota in the context of drug addiction.
Cocaine can be smoked (i.e., crack or coca paste) and its consumption is associated with a very high abuse liability and toxicity. We have recently reported that cocaine base seized samples contained caffeine and phenacetin as main active adulterants, which may potentiate its motivational, reinforcing, and toxic effects. However, the effect of volatilized cocaine and adulterants on the gut microbiota remained unknown. In the present study, we evaluated the effect of volatilized cocaine and two adulterants on the structure, diversity, and functionality of the gut microbiota in rats. Animals were chronically exposed to the fume of cocaine, caffeine, and phenacetin during 14 days. At the end of the treatment, feces were collected and the structure, composition, and functional predictions of the gut microbiota were analyzed. Cocaine significantly decreased the community richness and diversity of the gut microbiota while both cocaine and phenacetin drastically changed its composition. Phenacetin significantly increased the Firmicutes-Bacteroidetes ratio compared to the control group. When the predicted metagenome functional content of the bacterial communities was analyzed, all the treatments induced a dramatic decrease of the aromatic amino acid decarboxylase gene. Our findings suggest that repeated exposure to volatilized cocaine, as well as to the adulterants caffeine and phenacetin, leads to changes in the gut microbiota. Future studies are needed to understand the mechanisms underlying these changes and how this information may support the development of novel treatments in drug addiction.
KeywordsGut-brain axis Cocaine Adulterants Addiction Microbiota
This study was partially supported by the Programa de Desarrollo de Ciencias Básicas (PEDECIBA, Uruguay). MMB has a postgraduate fellowship from the Agencia Nacional de Investigación e Innovación (ANII, Uruguay). We are grateful to Dr. Lorenzo Leggio and Dr. Kuei Y. Tseng for their critical reading of this manuscript.
Conflict of Interest
The authors declare that they have no conflicts of interest.
Compliance with Ethical Standards
The study was carried out in accordance with the Instituto de Investigaciones Biológicas Clemente Estable (IIBCE) Bioethics Committee’s requirements, consistent with the National Institutes of Health guide for the care and use of laboratory animals (NIH Publication No. 8023, revised 1978), and under the current ethical regulations of the national law on animal experimentation no. 18.611.
- Abin-Carriquiry JA, Martínez Busi M, Galvalisi M, Minteguiaga M, Prieto JP, Scorza C (2018) Identification and quantification of cocaine and active adulterants in coca-paste seized samples: useful scientific support to health care. Neurotox Res. https://doi.org/10.1007/s12640-018-9887-1
- Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335–336CrossRefGoogle Scholar
- Galvalisi G, Prieto JP, Martínez M, Abin-Carriquiry JA, Scorza C (2015) Smoked cocaine: chemical analysis of seized samples and the role of caffeine in its central actions. IBRO 9th World Congress, Rio de Janeiro, Brazil. http://ibro.info/events/meetings/
- Hammer Ø, Haper DAT, Ryan PD (2001) PAST: Paleontological Statistics software package for education and data analysis. Paleontol Electron 4:4–9Google Scholar
- Kuczynski J, Stombaugh J, Walters WA, González A, Caporaso JG, Knight R (2011) Using QIIME to analyze 16S rRNA gene sequences from microbial communities. Curr Protoc Bioinformatics Chapter 10:Unit 10.7. https://doi.org/10.1002/0471250953.bi1007s36
- Muñiz JA, Prieto JP, González B, Sosa MH, Cadet JL, Scorza C, Urbano FJ, Bisagno V (2017) Cocaine and caffeine effects on the conditioned place preference test: concomitant changes on early genes within the mouse prefrontal cortex and nucleus accumbens. Front Behav Neurosci 11:200CrossRefGoogle Scholar
- Rognes T, Flouri T, Nichols B, Quince C, Mahé F (2016) VSEARCH: a versatile open source tool for metagenomics. PeerJ. 4:e2584Google Scholar
- Roselli M, Devirgiliis C, Zinno P, Guantario B, Finamore A, Rami R, Perozzi G (2017) Impact of supplementation with a food-derived microbial community on obesity-associated inflammation and gut microbiota composition. Genes Nutr 4:12–25Google Scholar