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Determine the differential miRNA levels in plasma of methamphetamine addicts [array]

ABSTRACT: To determine the differential miRNA levels in methamphetamine addicts, we comparatively profiled plasma miRNA expression of methamphetamine abusers and healthy controls using Agilent Human miRNA Array.

ORGANISM(S): Homo sapiens

PROVIDER: GSE224658 | GEO | 2023/04/20

REPOSITORIES: GEO

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Project description:BACKGROUND: Drug addiction can seriously damage human physical and mental health, while detoxification is a long and difficult process. Although studies have reported changes in the oral microbiome of methamphetamine (METH) addicts, the role of the microbiome plays in this process is still unknown. This study aims to explore the function of the microbiome based on analysis of the variations in the oral microbiome and metabolome of METH addicts. We performed the 16S rRNA sequencing analysis based on the oral saliva samples collected from 278 METH addicts and 105 healthy controls (CTL) undergoing detoxification at the detoxification center in Shandong, China. In addition, the untargeted metabolomic profiling was conducted based on 220 samples (170 METH addicts and 50 CTL) to identify the biomarkers and build classifiers for both oral microbiota and metabolites.RESULTS: Compared to the CTL group, alpha diversity was reduced in the group of METH addicts, with significant differences in the microbiota and changes in oral metabolic pathways, including enhanced tryptophan metabolism, lysine biosynthesis, purine metabolism and steroid biosynthesis. Conversely, the metabolic pathways of porphyrin metabolism, glutathione metabolism and pentose phosphate were significantly reduced. It was speculated that four key microbial taxa, i.e., Peptostreptococcus, Gemella, Campylobacter and Aggregatibacter, could be involved in the toxicity and addiction mechanisms of METH by affecting the above metabolic pathways. And, it was found that with the increase of drug use years, the content of tryptamine associated with neuropsychiatric disorders gradually increased. In addition, microbial prediction models were more effective than metabolite-based prediction models in identifying METH addiction.CONCLUSIONS: Our study identified the potential functional connections between the oral microbiome and metabolic profile of METH addicts, providing novel insights into exploring the toxic damage and addiction mechanisms underlying the METH addiction.

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Determine the differential miRNA levels in plasma of methamphetamine addicts [array]

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