Dataset Information

Alterations in the oral microbiome and metabolome of methamphetamine addicts

ABSTRACT:

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.

INSTRUMENT(S): Liquid Chromatography MS - negative - reverse phase, Liquid Chromatography MS - positive - reverse phase

SUBMITTER: Yu Feng

PROVIDER: MTBLS7807 | MetaboLights | 2023-07-24

REPOSITORIES: MetaboLights

ACCESS DATA

Similar Datasets

Project description:Patients with oral preneoplastic lesion (OPL) have high risk of developing oral cancer. Although certain risk factors such as smoking status and histology are known, our ability to predict oral cancer risk remains poor. The study objective was to determine the value of gene expression profiling in predicting oral cancer development. Gene expression profile was measured in 86 of 162 OPL patients who were enrolled in a clinical chemoprevention trial that used the incidence of oral cancer development as a prespecified endpoint. The median follow-up time was 6.08 years and 35 of the 86 patients developed oral cancer over the course. Gene expression profiles were associated with oral cancer-free survival and used to develope multivariate predictive models for oral cancer prediction. We developed a 29-transcript predictive model which showed marked improvement in terms of prediction accuracy (with 8% predicting error rate) over the models using previously known clinico-pathological risk factors. Based on the gene expression profile data, we also identified 2182 transcripts significantly associated with oral cancer risk associated genes (P-value<0.01, single variate Cox proportional hazards model). Functional pathway analysis revealed proteasome machinery, MYC, and ribosomes components as the top gene sets associated with oral cancer risk. In multiple independent datasets, the expression profiles of the genes can differentiate head and neck cancer from normal mucosa. Our results show that gene expression profiles may improve the prediction of oral cancer risk in OPL patients and the significant genes identified may serve as potential targets for oral cancer chemoprevention. Gene expression profile was measured in 86 of 162 OPL patients who were enrolled in a clinical chemoprevention trial that used the incidence of oral cancer development as a prespecified endpoint. The median follow-up time was 6.08 years and 35 of the 86 patients developed oral cancer over the course. Gene expression profiles were associated with oral cancer-free survival and used to develope multivariate predictive models for oral cancer prediction.

Project description:Karsten Krug, Eric J. Jaehnig, Shankha Satpathy, Lili Blumenberg, Alla Karpova, Meenakshi Anurag et al., (2020) <a href="https://www.cell.com/cell/fulltext/S0092-8674(20)31400-8" target="_blank">Cell, 183, 1436-1456</a> DOI: https://doi.org/10.1016/j.cell.2020.10.036 The integration of mass spectrometry-based proteomics with next-generation DNA and RNA sequencing profiles tumors more comprehensively. Herein this "proteogenomic" approach was applied to 122 treatment-naive primary breast cancers purposely accrued to preserve post-translational modifications, including protein phosphorylation and acetylation. Proteogenomics challenged standard breast cancer diagnoses, provided detailed analysis of the ERBB2 amplicon, defined tumor subsets that could benefit from immune checkpoint therapy and allowed more accurate assessment of Rb status for the prediction of CDK4/6 inhibitor responsiveness. Phosphoproteomic profiles uncovered novel associations between tumor suppressor loss and targetable kinases. Acetylproteome analysis highlighted acetylation on key nuclear proteins involved in the DNA damage response and revealed cross-talk between cytoplasmic and mitochondrial acetylation and metabolism. Our results underscore the potential of proteogenomics for the clinical investigation of breast cancer through more accurate annotation of targetable pathways and biological features of this remarkably heterogeneous malignancy.Genomic Data for Breast Cancer tumors is available from the NCI Genomic Data Commons (GDC), <a href="https://portal.gdc.cancer.gov/projects/CPTAC-2" target="_blank">here</a> Gene Expression data in GCT file format is available under the metadata section below Proteomic Data Analysis for Breast Cancer tumors (PSMs and Summary Reports) is available from the CPTAC Common Data Analysis Pipeline (Study S039 Early Data Release), <a href="/study-summary/S039" target="_blank">here</a> <ul><li>Dataset imported into MassIVE from <a href="https://cptac-data-portal.georgetown.edu/study-summary/S060">https://cptac-data-portal.georgetown.edu/study-summary/S060</a> on 06/05/21</li></ul>

Dataset Information

Alterations in the oral microbiome and metabolome of methamphetamine addicts

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure