Metformin induced gut dysbiosis
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB24497 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR2662900.fastq.gz | Fastqsanger.gz | |||
| ERR2662910.fastq.gz | Fastqsanger.gz | |||
| ERR2662920.fastq.gz | Fastqsanger.gz | |||
| ERR2662930.fastq.gz | Fastqsanger.gz | |||
| ERR2662940.fastq.gz | Fastqsanger.gz |
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Project description:Metformin, a biguanide agent, is the first-line treatment for type 2 diabetes mellitus due to its glucose-lowering effect. Despite its wide application in the treatment of multiple health conditions, the glycemic response to metformin is highly variable, emphasizing the need for reliable biomarkers. We chose the RNA-Seq-based comparative transcriptomics approach to evaluate the systemic effect of metformin and highlight potential predictive biomarkers of metformin response in drug-naïve type 2 diabetes patient volunteers in vivo. Longitudinal blood-derived transcriptome analysis revealed metformin-induced differential expression of novel and previously described genes involved in cholesterol homeostasis (SLC46A1 and LRP1), cancer development (CYP1B1, STAB1, CCR2, TMEM176B) and immune responses (CD14, CD163) after administration of metformin for three months. We demonstrate for the first time transcriptome-based molecular discrimination between metformin responders (delta HbA1c ≥ 1% or 12.6 mmol/mol) and non-responders (delta HbA1c < 1% or 12.6 mmol/mol) determined by expression levels of 56 genes, explaining 13.9% of the variance in the therapeutic efficacy of the drug. Moreover, we found a significant upregulation of IRS2 gene (log2FC 0.89) in responders compared to non-responders before the use of metformin. Finally, we provide evidence for the mitochondrial respiratory complex I as one of the factors related to the high variability of the therapeutic response to metforminin patients with type 2 diabetes mellitus.
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Project description:To test the effects of metformin on the human gut micorbiome, we fist collected human stool samples. We processed the samples in vitro culturing under anaerobic condition for 24 hours using the rapidAIM assay and either and cultured them with metformin, or DMSO as a control. We know that metformin can alter the human gut microbiome and were interested in better analyzing which functional proceses were altered.
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Project description:Metformin, the most widely administered diabetes drug, has been proposed as a candidate for host directed therapy for tuberculosis although very little is known about its effects on human host responses to Mycobacterium tuberculosis. When added in vitro to PBMCs isolated from healthy non-diabetic volunteers, metformin increased glycolysis, inhibited the mTOR targets, strongly reduced M. tuberculosis induced production of TNF-α (-58%), IFN-gamma (-47%) and IL-1β (-20%), while increasing phagocytosis. In healthy subjects, in vivo metformin intake induced significant transcriptional changes in whole blood and isolated PBMCs, with substantial down-regulation of genes related to inflammation and the type 1 interferon response. Metformin intake also increased monocyte phagocytosis (by 1.5 to 2 fold) and ROS production (+20%). These results show that metformin in humans has a range of potentially beneficial effects on cellular metabolism, immune function and gene-transcriptional level, that affect innate host responses to M. tuberculosis. This underlines the importance of cellular metabolism for host immunity and supports a role for metformin as host-directed therapy for tuberculosis.
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Project description:Aims: Metformin is a widely used, primary drug of choice to treat individuals with type 2 diabetes (T2D). Clinically, inter-individual variability of drug response is of significant concern. The targets and precise mechanisms of action for metformin is still under interrogation. In the present study, a whole transcriptome analysis was performed with an intent to identify predictive biomarkers of metformin response in T2D individuals. Methods: Twenty drug naïve individuals with T2D and ten healthy controls were enrolled. T2D individuals were administrated with metformin monotherapy and were defined as responders and non-responders based on the changes in their glycated haemoglobin (HbA1c) over a period of three months. Poly(A) RNA Seq was performed using Ion Torrent Proton platform and differentially expressed genes were identified from the transcriptome profiles. The gene prioritization, enrichment analysis, drug-gene interactions, disease gene association analysis were performed using various tools and databases. A correlation analysis was also performed between gene expression and change in HbA1c levels after 3 months of metformin therapy. Results: The expression analysis highlighted differentially expressed genes in metformin responders, non-responders and healthy individuals. The gene prioritization and enrichment analysis highlighted potential targets for predicting metformin response (n=35) as well as T2D disease (n=14). The drug-gene interactions did not show any direct association with diabetes drugs. However, the expression of GDF15, TWISTNB and RPL36A genes showed maximum correlation with change in HbA1c levels after 3 months of metformin therapy. The disease gene association analysis highlighted rs113805659 in MAGI2 gene to be linked with T2D in African Americans. Conclusion: The results of the present study provide evidence for the perturbed transcriptome and pathways associated with metformin drug response in T2D individuals.
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Project description:Background: Metformin, one of the first-line medication for the treatment of type 2 diabetes and gestational diabetes, has recently be suggested for targeting cardiovascular disease, cancer and aging. Therefore, current understanding of the mechanism of this drug is incompletely understood, and the function of multiple tissues, other than liver metabolism alone, may be influenced. Methods: The wildtype healthy mice treated with metformin were compared with controls (treated with double distilled water). The transcriptome changes with/without metformin treatment were probed by using high-throughput RNA-seq techniques Results: A comprehensive mouse transcriptome map with metformin treatment across ten tissues including aorta, eyeball, brain, adipose tissue, heart, kidney, liver, skeletal muscle, stomach and testis, was provided. Function enrichment, network characteristics and disease association of the differentially expressed genes were analyzed. We also compared our expression profiles with related microarray data in order to find conditions that share similar expression profiles with metformin treatment. Conclusions: This dataset could serve as a baseline resource for investigating the potential beneficial or adverse effects of metformin across different tissues.
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