Gene Expression Data Explorer
Info Gene counts are sourced from ARCHS4, which provides uniform alignment of GEO samples. You can learn more about ARCHS4 and its pipeline here.
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GROUP CONDITION SAMPLES
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GSM2882966 GSM2882967 GSM2882970 GSM2882971 GSM2882972 GSM2882973 GSM2882974 GSM2882975 GSM2882976 GSM2882977 GSM2882986 GSM2882987
GSM2883022 GSM2883024 GSM2883025 GSM2883026 GSM2883027 GSM2883028 GSM2883029 GSM2883031 GSM2883032 GSM2883036
GSM2882988 GSM2882989 GSM2882990 GSM2882993 GSM2883002 GSM2883007 GSM2883008 GSM2883009 GSM2883010 GSM2883011 GSM2883012 GSM2883013 GSM2883014 GSM2883015 GSM2883016 GSM2883017 GSM2883018
GSM2883037 GSM2883038 GSM2883042 GSM2883043 GSM2883050 GSM2883051 GSM2883055 GSM2883056 GSM2883057 GSM2883058 GSM2883059
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GSM2882963 GSM2882964 GSM2882965 GSM2882968 GSM2882969 GSM2882978 GSM2882979 GSM2882980 GSM2882981 GSM2882982 GSM2882983 GSM2882984 GSM2882985
GSM2883020 GSM2883021 GSM2883023 GSM2883030 GSM2883033 GSM2883034 GSM2883035
GSM2882991 GSM2882992 GSM2882994 GSM2882995 GSM2882996 GSM2882997 GSM2882998 GSM2882999 GSM2883000 GSM2883001 GSM2883003 GSM2883004 GSM2883005 GSM2883006 GSM2883019
GSM2883039 GSM2883040 GSM2883041 GSM2883044 GSM2883045 GSM2883046 GSM2883047 GSM2883048 GSM2883049 GSM2883052 GSM2883053 GSM2883054 GSM2883060 GSM2883061 GSM2883062
Description

Submission Date: Dec 11, 2017

Summary: Administration of metformin increases healthspan and lifespan in model systems and evidence from clinical trials and observational studies suggests that metformin delays a variety of age-related morbidities. Although metformin has been shown to modulate multiple biological pathways at the cellular level, these pleiotropic effects of metformin on the biology of human aging have not been studied. We studied ~70-year-old participants (n=14), in a randomized, double-blind, placebo-controlled, crossover trial in which they were treated with 6 weeks each of metformin and placebo. Following each treatment period, skeletal muscle and subcutaneous adipose tissue biopsies were obtained, and a mixed-meal challenge test was performed. As expected, metformin therapy lowered 2-hour glucose, insulin AUC, and insulin secretion compared to placebo. Using FDR<0.05, 647 genes were differentially expressed in muscle and 146 genes were differentially expressed in adipose tissue. Both metabolic and non-metabolic pathways were significantly influenced, including pyruvate metabolism and DNA repair in muscle and PPAR & SREBP signaling, mitochondrial fatty acid oxidation and collagen trimerization in adipose. While each tissue had, a signature reflecting its own function, we identified a cascade of predictive upstream transcriptional regulators, including mTORC1, MYC, TNF, TGFß1 and miRNA-29b, that may explain tissue-specific transcriptomic changes in response to metformin treatment. This study provides the first evidence that, in older adults, metformin has metabolic and non-metabolic effects linked to aging. These data can inform the development of biomarkers for the effects of metformin, and potentially other drugs, on key aging pathways.

Key words: Aging, biguanides, gene expression, metabolism, upstream regulators

GEO Accession ID: GSE107894

PMID: 29383869

Description

Submission Date: Dec 11, 2017

Summary: Administration of metformin increases healthspan and lifespan in model systems and evidence from clinical trials and observational studies suggests that metformin delays a variety of age-related morbidities. Although metformin has been shown to modulate multiple biological pathways at the cellular level, these pleiotropic effects of metformin on the biology of human aging have not been studied. We studied ~70-year-old participants (n=14), in a randomized, double-blind, placebo-controlled, crossover trial in which they were treated with 6 weeks each of metformin and placebo. Following each treatment period, skeletal muscle and subcutaneous adipose tissue biopsies were obtained, and a mixed-meal challenge test was performed. As expected, metformin therapy lowered 2-hour glucose, insulin AUC, and insulin secretion compared to placebo. Using FDR<0.05, 647 genes were differentially expressed in muscle and 146 genes were differentially expressed in adipose tissue. Both metabolic and non-metabolic pathways were significantly influenced, including pyruvate metabolism and DNA repair in muscle and PPAR & SREBP signaling, mitochondrial fatty acid oxidation and collagen trimerization in adipose. While each tissue had, a signature reflecting its own function, we identified a cascade of predictive upstream transcriptional regulators, including mTORC1, MYC, TNF, TGFß1 and miRNA-29b, that may explain tissue-specific transcriptomic changes in response to metformin treatment. This study provides the first evidence that, in older adults, metformin has metabolic and non-metabolic effects linked to aging. These data can inform the development of biomarkers for the effects of metformin, and potentially other drugs, on key aging pathways.

Key words: Aging, biguanides, gene expression, metabolism, upstream regulators

GEO Accession ID: GSE107894

PMID: 29383869

Visualize Samples

Info Visualizations are precomputed using the Python package scanpy on the top 5000 most variable genes.

Precomputed Differential Gene Expression

Info Differential expression signatures are automatically computed using the limma R package. More options for differential expression are available to compute below.

Signatures:

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Control Condition

Perturbation Condition

Only conditions with at least 1 replicate are available to select

Differential Gene Expression Analysis
Info Differential expression signatures can be computed using DESeq2 or characteristic direction.
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Bulk RNA-seq Appyter

This pipeline enables you to analyze and visualize your bulk RNA sequencing datasets with an array of downstream analysis and visualization tools. The pipeline includes: PCA analysis, Clustergrammer interactive heatmap, library size analysis, differential gene expression analysis, enrichment analysis, and L1000 small molecule search.