2019
DOI: 10.1016/j.cell.2019.08.003
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Abstract: SummaryMetformin is the first-line therapy for treating type 2 diabetes and a promising anti-aging drug. We set out to address the fundamental question of how gut microbes and nutrition, key regulators of host physiology, affect the effects of metformin. Combining two tractable genetic models, the bacterium E. coli and the nematode C. elegans, we developed a high-throughput four-way screen to define the underlying host-microbe-drug-nutrient interactions. We show that microbes integrate cues from metformin and … Show more

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Cited by 189 publications
(155 citation statements)
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References 57 publications
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“…In agreement with previous results (13, 14), metformin intake (MET) significantly affected T2D classification (+MET T2D AUC = 0.87 vs. -MET T2D AUC = 0.60; Supplementary Fig. 4 A ), but a risk signature based on +/-MET intake was not able to classify T2D in the KORA cohort (AUC = 0.60; Supplementary Fig.…”
Section: Resultssupporting
confidence: 92%
See 1 more Smart Citation
“…In agreement with previous results (13, 14), metformin intake (MET) significantly affected T2D classification (+MET T2D AUC = 0.87 vs. -MET T2D AUC = 0.60; Supplementary Fig. 4 A ), but a risk signature based on +/-MET intake was not able to classify T2D in the KORA cohort (AUC = 0.60; Supplementary Fig.…”
Section: Resultssupporting
confidence: 92%
“…Increasing evidence links the human gut microbiome to metabolic health (1), and altered microbial profiles are associated with obesity, insulin resistance, and Type-2-Diabetes (T2D) (29). Population-based studies highlighted a significant degree of variability in inter-individual microbiome differences (10, 11), regional effects (12), and drug-associated changes in the gut microbiome (13, 14), which complicates the identification of disease-related microbial risk factors. Despite extensive investigations of the role of the gut microbiome in metabolic diseases, especially T2D, there is still no consensus on disease-related bacterial taxa with diagnostic relevance.…”
mentioning
confidence: 99%
“…To address the outcomes of metformin treatment at different age, we treated young adult (3 days old, day 1 of adulthood), adult at the age of reproduction decline (day 4 of adulthood), middle aged (day 8 of adulthood) and old (day 10 of adulthood) wild type C. elegans worms with different doses of metformin – 10mM, 25mM and 50mM. 50mM metformin is the common dose used to induce lifespan extension in C. elegans while 10mM is the lowest dose linked to reproducible life extension in this model in previous reports (Cabreiro et al, 2013; Onken and Driscoll, 2010; Pryor et al, 2019). We found that metformin treatment started at young age (days 1 and 4 of adulthood) extended lifespan of nematodes at all doses used (Figure 1A and Figure S1A).…”
Section: Resultsmentioning
confidence: 78%
“…Metabolic models not only have demonstrated their ability to predict phenotypes on the level of cellular growth and gene knockouts, but also provide potential molecular mechanisms in form of gene and reaction activities, which can be validated experimentally [87]. Due to this predictive potential, genome-scale metabolic models have been applied to identify metabolic interactions between different organisms [1,32,44,80,96], to study host-microbiome interactions [33,64,95], to predict novel drug targets to fight microbial pathogens [55,85], and for the rational design of microbial genotypes and growth-media conditions for the industrial production or degradation of biochemicals [59,66]. Furthermore, recent advances in DNA-sequencing technologies have led to a vast increase in available genomic-and metagenomic sequences in databases [48], which further expands the applicability of genome-scale metabolic network reconstructions.…”
Section: Doug Mcilroymentioning
confidence: 99%