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. 2019 Sep 5;178(6):1299-1312.e29.
doi: 10.1016/j.cell.2019.08.003. Epub 2019 Aug 29.

Host-Microbe-Drug-Nutrient Screen Identifies Bacterial Effectors of Metformin Therapy

Rosina Pryor  1 , Povilas Norvaisas  2 , Georgios Marinos  3 , Lena Best  3 , Louise B Thingholm  4 , Leonor M Quintaneiro  1 , Wouter De Haes  5 , Daniela Esser  3 , Silvio Waschina  3 , Celia Lujan  6 , Reuben L Smith  7 , Timothy A Scott  2 , Daniel Martinez-Martinez  8 , Orla Woodward  2 , Kevin Bryson  9 , Matthias Laudes  10 , Wolfgang Lieb  11 , Riekelt H Houtkooper  7 , Andre Franke  4 , Liesbet Temmerman  5 , Ivana Bjedov  6 , Helena M Cochemé  8 , Christoph Kaleta  12 , Filipe Cabreiro  13
Affiliations

Host-Microbe-Drug-Nutrient Screen Identifies Bacterial Effectors of Metformin Therapy

Rosina Pryor et al. Cell. .

Abstract

Metformin 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 VSports手机版. 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 the diet through the phosphotransferase signaling pathway that converges on the transcriptional regulator Crp. A detailed experimental characterization of metformin effects downstream of Crp in combination with metabolic modeling of the microbiota in metformin-treated type 2 diabetic patients predicts the production of microbial agmatine, a regulator of metformin effects on host lipid metabolism and lifespan. Our high-throughput screening platform paves the way for identifying exploitable drug-nutrient-microbiome interactions to improve host health and longevity through targeted microbiome therapies. VIDEO ABSTRACT. .

Keywords: C V体育安卓版. elegans; CRP signaling; Drosophila; aging; diet; humans; metabolic modeling; metformin; microbiome; type-2 diabetes. .

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Conflict of interest statement

All authors declare no competing interests.

"V体育ios版" Figures

None
Graphical abstract
Figure 1
Figure 1
Four-Way Host-Microbe-Drug-Nutrient Screens Identify a Signaling Hub for the Integration of Drug and Nutrient Signals (A–C) The effects of metformin on bacterial growth (A), wild-type N2 worm lifespan (B), and metabolism (C) are dependent on drug dose, nutrients, and bacteria. OP50-MR is an E. coli OP50 strain that developed metformin resistance. As observed previously (Cabreiro et al., 2013), metformin does not extend the lifespan when worms are grown on OP50-MR. In (B), each data point corresponds to the mean lifespan of 80–154 worms. See also Table S1. In (C), each panel shows 8 individual worms. (D) Diagram of the four-way host-microbe-drug-nutrient interaction screen. (E) Nutrient effects on bacterial phenotype (growth, x axis) and on wild-type N2 worm phenotype rescue (Pacs-2::GFP expression, y axis) in response to metformin. The red fit line shows the correlation between metformin and nutrient effects in bacteria and worms. Antagonistic or synergistic refers to the type of interaction determined by linear modeling observed between metformin and nutrient effects, leading to an overall effect that is significantly greater than the sum of the effects of the two components alone either in C. elegans Pacs-2::GFP levels or E. coli growth. Positive fold changes indicate nutrient suppression of the effect of metformin in bacterial growth or C. elegans Pacs-2::GFP expression. Error bars represent SE. FDR < 0.05 for significance. All colored circles are statistically significant. Gray circles are non-significant. Effects of highlighted nutrients are provided in detail in Figures S1 and S2. (F and G) EcoCyc metabolite class (F) and KEGG pathway (G) enrichment for the effects of nutrients on E. coli OP50 growth and worm Pacs-2::GFP expression in the context of metformin treatment. Data are represented as mean ± SEM. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. See also Table S1 for lifespan statistics and Table S2 for screen statistics.
Figure S1
Figure S1
Four-Way Host-Microbe-Drug-Nutrient Screens Identify a Signaling Hub for the Integration of Drug and Nutrient Signals, Related to Figure 1 (A) Bacterial growth of E. coli OP50 on different types of media with increasing concentrations of metformin. Shaded area shows mean growth OD ± SD. (B) Bacterial growth of E. coli OP50-MR (metformin resistant) on different types of media with increasing concentrations of metformin. Shaded area shows mean growth OD ± SD. (C) Pacs-2::GFP expression of worms grown on E. coli OP50 with different types of media and increasing concentrations of metformin. Significance stars represent comparison with 0 mM metformin for each media type. (D) Comparison of nutrient effects on E. coli OP50 growth and worm Pacs-2::GFP expression in the context of metformin treatment. (E and F) Correlation between nutrient rescue of worm Pacs-2::GFP fluorescence and nutrient effect on E. coli OP50 growth in control (E), and metformin treatment conditions (F). Nutrient supplementation without metformin (r2 = 0.057, p = 8.8 × 10−6) (E) nor nutrient supplementation with metformin (r2 = 0.097, p = 4.9 × 10−9) (F) does strongly predict the effects of metformin on host physiology. (G) Strong correlation (r2 = 0.76, p = 6.0 × 10−6) between effects of nutrient supplementation on E. coli OP50 growth in control (x axis) versus metformin treatment conditions (y axis). (H) Venn diagram of nutrients with significant effects on E. coli and/or worms in the context of metformin treatment. (I) Top panel: Bacterial growth curves on base NGM media and with nutrient supplementation. Shaded area represents mean growth OD ± SD. Here and in following panels, red corresponds to control and purple to metformin treatment conditions. Middle panel: Examples of worm Pacs-2::GFP expression with the corresponding nutrient supplementation and the type of drug-nutrient interaction in worm response. Bottom panel: Histograms of worm Pacs-2::GFP expression in log2 scale, with distribution density shown on y axis. Shaded area shows worm brightness distribution SD for individual worms. Vertical lines indicate Q90 worm Pacs-2::GFP expression values. Red- Control and Blue – 50 mM metformin. Full lines- NGM control and dotted lines – NGM plus indicated nutrient supplementation. Full lines are represented in all conditions as a reference for direct comparison. (J) Bacterial growth estimates based on log2 transformed AUC values (top) and worm Pacs-2::GFP expression estimates based on log2 transformed fluorescence brightness Q90 values (bottom). Dashed lines indicate bacterial growth on NGM and a worm Pacs-2::GFP expression level used as a reference. Arrows indicate metformin treatment and significant interaction effects (FDR < 0.05).
Figure S2
Figure S2
E. coli Integrates Drug and Nutritional Cues to Regulate Host Physiology, Related to Figure 1 (A and B) Supplementation with L-serine (A) or adenosine (B) does not suppress worm lifespan extension by metformin. (C) Supplementation with glycerol rescues inhibition of bacterial growth by metformin in control E. coli OP50 but not in OP50 ΔglpK mutants unable to catabolize glycerol. (D–G) Glycerol supplementation suppresses metformin-induced upregulation of Pacs-2::GFP expression (D-E) and abolishes lifespan extension (F-G) in worms in a bacteria-dependent manner. Nutrient effects are rescued by an E. coli OP50 ΔglpK mutant unable to catabolize glycerol. In (E), each panel shows 5 individual worms. (H) Supplementation with D-ribose rescues inhibition of bacterial growth by metformin in control E. coli OP50 but not in OP50 ΔrbsK mutants unable to catabolize D-ribose. (I–L) D-ribose supplementation suppresses metformin-induced upregulation of Pacs-2::GFP expression (I-J) and abolishes lifespan extension (K-L) in worms in a bacteria-dependent manner. Nutrient effects are rescued by an E. coli OP50 ΔrbsK mutant unable to catabolize D-ribose. In (I), each panel shows 5 individual worms. Data are represented as mean ± SEM unless otherwise stated. ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001. For C, D, H, and J, significance stars represent metformin effect (purple) and metformin-nutrient interaction (green). See also Table S1 for lifespan statistics and table S2 for screen statistics.
Figure 2
Figure 2
Bacterial Proteomics Identify Transcriptional Networks Underlying Metformin Effects in E. coli (A) Volcano plot showing E. coli proteins that are differentially regulated in response to metformin. Highlighted proteins belong to significantly enriched KEGG pathways. (B) Diagram displaying connectivity between KEGG pathway enrichment and RegulonDB transcription factor (TF) enrichment from proteomics data of E. coli OP50 treated with metformin. (C) Bacterial growth summary of E. coli OP50 TF mutants with metformin. Significance stars represent comparison with OP50 for each metformin concentration. (D) Metformin regulates worm Pacs-2::GFP expression in a bacterial TF-dependent manner. Significance stars represent comparison with OP50 at 0 mM (red) or 50 mM (purple) and metformin-genotype interaction (green). (E and F) Metformin extends worm lifespan in a bacterial TF-dependent manner. Data are represented as mean ± SEM. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. See also Table S1 for lifespan statistics and Table S3 for proteomics statistics.
Figure S3
Figure S3
Bacterial Proteomics Identify Transcriptional Networks Underlying Metformin Effects in E. coli, Related to Figure 2 (A) Bacterial growth curves of E. coli OP50 transcription factor (TF) mutants with increasing concentrations of metformin. Shaded area shows mean growth OD ± SD. (B) Bacterial growth summaries of E. coli OP50 deletion mutants for TFs associated with proteomic changes in response to metformin treatment. Significance stars represent comparison with OP50 for each metformin concentration. Opposite to the effects of metformin on the resistant OP50-MR strain compared to OP50, Δcra and ΔarcA mutants exhibited increased sensitivity to bacterial growth inhibition by metformin. (C and D) Metformin regulates worm Pacs-2::GFP expression in a E. coli OP50 TF-dependent manner. Worms grown on Δcra (A) and ΔarcA mutants (B) showed an increased activation of host Pacs-2::GFP expression in an additive manner to metformin. For C, significance stars represent comparison with OP50 at 0 mM (red) or 50 mM (purple) and metformin-genotype interaction (green). In (D), each panel shows 5 individual worms. (E and F) Worm lifespan extension by metformin is enhanced with a Δcra E. coli OP50 mutant at low (6. 25 mM) (E) but not high (50 mM) (F) drug concentrations. As previously reported (Cabreiro et al., 2013), these data suggest a shift in the window of action of metformin on host longevity depending on the sensitivity of the bacterial strain to growth inhibition by metformin. (G-N) Survival curves of E. coli OP50 TF mutants that do not affect worm lifespan extension by metformin. Data are represented as mean ± SEM unless otherwise stated. ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001. See also Table S1 for lifespan statistics and Table S3 for proteomics statistics.
Figure 3
Figure 3
Bacterial PTS-Crp Signaling Regulates Metformin Effects on Host Metabolism and Lifespan (A) Diagram of the PTS-Crp signaling pathway in E. coli. (B–D) Glucose supplementation (B); deletion of E. coli OP50 pts H, I, and crr (C); and cyaA (D) abolishes worm lifespan extension by metformin. (E) Metformin upregulates Crp expression in control E. coli OP50 but not in OP50 Δcrp or ΔcyaA mutants or with glucose supplementation. Significance stars represent metformin effect (purple) and metformin-genotype or nutrient interaction (green). (F) Dose-dependent upregulation of Crp in E. coli OP50 extends the worm lifespan. (G) Overexpression of Crp in E. coli OP50 upregulates Pacs-2::GFP expression in worms. Each panel shows 5 individual worms. (H) Effect of overexpression of E. coli Crp on the worm lifespan is dependent on bacterial cyaA. (I and J) Metformin extends the lifespan in flies grown on chemically defined medium with E. coli OP50 (I) but not with an OP50 Δcrp mutant (J). (K) E. coli OP50 overexpressing Crp extends the fly lifespan on chemically defined medium. Data are represented as mean ± SEM. n.s., non-significant; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001. See also Table S1 for lifespan statistics.
Figure S4
Figure S4
Bacterial PTS-Crp Signaling Regulates Metformin Effects on Organismal Metabolism and Lifespan, Related to Figure 3 (A and B) Glucose supplementation suppresses upregulation of worm Pacs-2::GFP expression by metformin. For B, significance stars represent metformin effect (purple) and metformin-nutrient interaction (green). In (A), each panel shows 5 individual worms. (C) Deletion of E. coli OP50 crr abolishes worm lifespan extension by metformin. (D) Bacterial growth curves of E. coli OP50 PTS-Crp signaling mutants with increasing concentrations of metformin. Shaded area shows mean growth OD ± SD. (E) Bacterial growth summaries of E. coli OP50 PTS-Crp signaling mutants with increasing concentrations of metformin. Significance stars represent comparison with OP50 for each metformin concentration. (F) Glycerol supplementation suppresses upregulation of Crp in metformin-treated E. coli OP50. (G) Metformin significantly increases the ratio of PEP/Pyruvate, the glycolytic flux sensor, in E. coli but the effect is abolished by glucose supplementation. (H) An E. coli OP50 Δcrp pCrp strain exhibits augmented Crp expression in response to increasing concentrations of IPTG. (I) Induction of PCrp overexpression is required to extend C. elegans lifespan. IPTG supplementation at 50 μM does not extend worm lifespan. (J) Overexpression of functionally diverse E. coli proteins in distinct sub-cellular compartments does not extend C. elegans lifespan implying that overexpression alone by a protein-inducible plasmid in bacteria does not affect C elegans lifespan. (K) Induction of E. coli pCrp overexpression is required to increase Pacs-2::GFP expression in worms. (L) Worms grown on ΔcyaA pCrp E. coli OP50 are longer lived compared to worms grown on ΔcyaA E. coli OP50 when supplemented with cAMP (1 mM) and 25 μM IPTG. (M) Growth summaries of OP50 and Δcrp E. coli OP50 strains overexpressing Crp in response to increasing concentrations of IPTG. Significance stars represent comparison with 0 μM IPTG for each strain. (N) Growth summary of OP50 and ΔcyaA E. coli OP50 strains overexpressing Crp in response to increasing concentrations of IPTG. Significance stars represent interaction between Crp overexpression and IPTG versus untreated control. (O–Q) An E. coli OP50 ΔcpdA mutant unable to degrade cAMP extends worm lifespan (K) but not in the absence of cyaA (L) and crp (M). (R) Metformin does not extend lifespan of germ-free flies in chemically-defined media. Data are represented as mean ± SEM unless otherwise stated. n.s. non-significant, ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001. See also Table S1 for lifespan statistics.
Figure 4
Figure 4
Bacterial Agmatine Regulates Host Metabolism and Lifespan (A) Volcano plots of metabolomics data showing effect of metformin in control E. coli OP50 or an OP50 Δcrp mutant and the effect of Crp overexpression in OP50. (B) Subset of differentially and significantly expressed metabolites that are unique to Crp regulation and metformin treatment. (C) Bacterial arginine-related metabolic pathways with an overlay of metformin-induced changes in the E. coli proteome and metabolome. Ast, arginine N-succinyltransferase pathway. (D and E) Deletion of genes from E. coli arginine catabolism alters worm Pacs-2::GFP expression (D) and lifespan (E). (F and G) Agmatine supplementation upregulates worm Pacs-2::GFP expression (F) and extends the lifespan (G) in a bacterium-dependent manner. (H) Agmatine supplementation extends the fly lifespan in sugar-yeast-agar (SYA) medium. (I) Metformin does not extend the lifespan in the agmatine-deficient OP50 mutant ΔadiAΔspeA. (J) Comparison of in silico predicted agmatine production capacity and measured worm Pacs-2::GFP expression with nutrient supplementation in the context of metformin. The p values indicate the significance of association between predicted agmatine production capacity and Pacs-2::GFP fluorescence (linear model fit). See Figure S5J for predicted agmatine production capacity and measured growth-rescue of metformin-treated E. coli OP50. Data are represented as mean ± SEM. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. See also Table S1 for lifespan statistics, Table S3 for proteomics statistics, and Table S4 for metabolomics statistics.
Figure S5
Figure S5
Bacterial Agmatine Regulates Host Metabolism and Lifespan, Related to Figure 4 (A) PCA plot of E. coli metabolomics data showing effect of metformin treatment on control E. coli OP50 and a OP50 Δcrp mutant and the effect of Crp overexpression. (B) Worm Pacs-2::GFP expression is increased by a ΔspeB E. coli OP50 mutant. (C) Bacterial growth curves of E. coli arginine catabolism mutants. Shaded area show mean growth OD ± SD. (D) Agmatine supplementation delays worm development and reproduction in a bacteria-dependent manner. (E) Agmatine supplementation extends lifespan in worms grown on a ΔadiAΔastAΔspeAΔspeB E. coli OP50 mutant unable to metabolize agmatine. (F–H) Agmatine supplementation reduces Drosophila fecundity (F) and body weight (G) and extends Drosophila lifespan (H) in a concentration-dependent manner on SYA media. (I) Measurements of macromolecular content (proteins, sugars and lipids) of control E. coli OP50 and a ΔadiAΔspeA OP50 mutant show no significant differences between the strains. Significance stars represent metformin effect (purple) and metformin-genotype interaction (green). (J) Metformin does not extend lifespan further when worms are grown on a ΔastAΔspeB E. coli OP50 mutant. (K) Predicted relative increase in agmatine production by E. coli OP50 following supplementation of 5 mmol of different nutrients to NGM medium. Nutrients are grouped according to their class. (L) Top 15 metabolites according to predicted increase of agmatine production by E. coli OP50 on NGM medium following supplementation of 5 mmol of each compound. Only compounds present in the diet of the Kiel cohort are shown. (M) Comparison of predicted increases in agmatine production following nutrient supplementation to NGM medium and experimentally measured E. coli OP50 growth phenotype rescue by nutrients on Biolog plates in response to metformin. A significant association between predicted agmatine production capacity and measured growth-rescue of metformin-treated E. coli OP50 (linear model p = 2.0 × 10−6, Table S5D). (N) Predicted increases in agmatine production capacity of the microbiota of metformin-treated patients following supplementation 1 mmol of each compound to the reported diet of the participant available per gram of microbiota. Only compounds present in the diet of the Kiel cohort are shown. Abbreviations: FC, fold-change. Data are represented as mean ± SEM unless otherwise stated. n.s. non-significant, ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001. Abbreviations: FC, fold-change. See also Table S1 for lifespan statistics and Table S4 for metabolomics statistics.
Figure 5
Figure 5
Metabolic Modeling of Human Gut Microbiota Reveals Signatures of Agmatine Overproduction in Metformin-Treated Type 2 Diabetic Patients (A) Predicted agmatine production by the gut microbiota in the 3 independent cohorts. Shown are FDR-corrected p values from Wilcoxon rank-sum tests between the indicated groups. (B) Longitudinal changes in predicted agmatine production following initiation of metformin treatment in newly diagnosed type 2 diabetic patients. The p values indicate the significance of the treatment effect (i.e., time) on agmatine production (linear model fit). (C) Predicted top 5 microbial producers of agmatine within the gut microbiome of metformin-treated patients across cohorts. (D) Side products of predicted agmatine production in the Kiel cohort. Values correspond to moles of side product produced per mole of agmatine produced. Data are represented as absolute values. For details regarding statistical tests, see STAR Methods and Table S5. mmol/gM/day, predicted production fluxes in millimoles per gram of gut microbiota per day.
Figure 6
Figure 6
Metformin and Bacterium-Dependent Transcriptional and Metabolic Signatures in C. elegans (A) Multi-dimensional scaling plot of worm RNA-seq data showing distinct and bacterium-dependent transcriptional signatures associated with metformin treatment. (B) KEGG pathway enrichment for worm RNA-seq data. (C) Metformin increases expression of worm lipid-related genes in a bacterium-dependent manner as effects are suppressed in OP50 Δcrp. Similar effects were observed for worms grown on OP50-MR (Figures S6B and S6C). (D and E) Confocal visualization of worm lipid droplets (D) and peroxisomes (E), showing effects of metformin in worms in a bacterial Crp-dependent manner. Similar effects were observed for worms grown on OP50-MR (Figures S6E and S6F). Scale bars, 10 μm. No changes in gene expression for dhs-3 or vha-6 were observed (Table S6). (F) Metabolomics in worms show an interaction between metformin and bacteria on host fatty acid profiles. Data are represented as mean ± SEM. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. In (E) and (F), significance stars represent metformin effect (black) and metformin-bacterium interaction (green or blue). See also Table S6 for RNA-seq statistics and Table S7 for fatty acid metabolomics statistics.
Figure S6
Figure S6
Metformin and Bacterium-Dependent Transcriptional and Metabolic Signatures in C. elegans, Related to Figure 6 (A) Venn diagram showing an overlap of metformin-induced significant (FDR < 0.05) transcriptional changes in worms on E. coli OP50 and OP50-MR strains, and the subset responsible for the longevity phenotype. (B) Metformin increases the expression of worm genes involved in multiple processes in a bacteria-dependent manner. Significance stars represent metformin effect (purple) and metformin-bacteria interaction (green). (C) Metformin-induced increases in worm gene expression revealed by RNaseq are recapitulated using fluorescent transgenic reporter lines. (D) Diagram of genes and metabolites involved in fatty acid metabolism that were studied in order to evaluate their contribution to metformin effects on host metabolism and lifespan. Transgenic reporter strains (green) were used to quantify the expression of the following genes: atgl-1, required to mobilize fatty acids from triglyceride stores; acs-2, required for fatty acid activation; cpt-5 and cpt-2, required for transport of fatty acids across the mitochondrial membrane; acad-10, a mitochondrial β−oxidation enzyme and dhs-23, a peroxisomal short chain dehydrogenase involved in steroid and lipid metabolism. Genetic mutants or RNAi knockdown (orange) were used to investigate the role of the following genes: nhr-49, a global regulator of β−oxidation; acs-1, a mitochondrial β−oxidation enzyme; acox-1.1/5, peroxisomal β−oxidation enzymes; fzo-1 and eat-3, required for mitochondrial fusion; drp-1, required for mitochondrial fission; nuo-1, gas-1, isp-1 and cco-1, required for electron transport chain function and prx-5, required for peroxisomal biogenesis. Lipid droplets and peroxisomes were visualized using transgenic strains that report the dhs-3 lipid droplet marker protein and a RFP-PTS1 peroxisome-targeting sequence fusion, respectively (blue). Worms were also treated with perhexiline, an inhibitor of β−oxidation and acetoacetate, a product of fatty acid β−oxidation. (E and F) Confocal visualization of worm lipid droplets (E) and peroxisomes (F) show effects of metformin in worms in a bacterial OP50-MR-dependent manner. 10 μm scale bar. No changes in gene expression for dhs-3 or vha-6 were observed (Table S6). (G) Metformin increases worm peroxisomal abundance in a bacterial OP50-MR-dependent manner. Significance stars represent metformin effect (purple) and metformin-bacteria interaction (green). Data are represented as mean ± SEM n.s.- non-significant, ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001. See also Table S1 for lifespan statistics and Table S6 for RNA-seq statistics.
Figure 7
Figure 7
Metformin Increases Fatty Acid Oxidation to Regulate Host Metabolism and Lifespan (A) PCA plot of fatty acid metabolomics data, showing distinct signatures of metformin in worms in a bacterium- and worm nhr-49-dependent manner. (B) Fatty acid metabolomics in worms, showing an interaction between metformin and worm nhr-49. (C–F) Host nhr-49 regulates metformin effects on worm Pacs-2::GFP expression (C) and the effects of metformin (D), agmatine supplementation (E), and E. coli OP50 Crp overexpression (F) on the worm lifespan. (G and H) Worm lifespan extension by metformin is abolished by RNAi knockdown of the mitochondrial FAO gene acs-1 (G) and in acox-1.1 and acox-1.5 peroxisomal FAO mutants (H). (I) Proposed model of host-microbe-drug-nutrient interactions that regulate metformin effects on host metabolism and lifespan. Data are represented as mean ± SEM. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. In (B) and (C), significance stars represent metformin effect (black) and metformin-genotype interaction (green). See also Table S1 for lifespan statistics and Table S7 for fatty acid metabolomics statistics.
Figure S7
Figure S7
Metformin Increases Fatty Acid Oxidation to Regulate Host Metabolism and Lifespan, Related to Figure 7 (A and B) Quality of representation (measured as squared cosine) of the variables (Samples in (A) and metabolites in (B)) in the first three Principle Components. Value ranges between 0 and 1, where 1 corresponds to the maximum quality of representation. (C–E) Worm lifespan extension by metformin is suppressed in fzo-1 (C) and eat-3 (D) mitochondrial fusion mutants and a drp-1 mitochondrial fission mutant (E) involved in mitochondrial homeostasis. (F–I) Worm lifespan extension by metformin is suppressed in gas-1 (F) and nuo-1 (G) mitochondrial respiration complex I mutants, an isp-1 mitochondrial complex III mutant (H) and with RNAi knockdown of cco-1 encoding a mitochondrial complex IV subunit (I). (J) Metformin does not further extend lifespan of worms treated with the FAO-inhibitor perhexiline (control plates supplemented with 0.25% DMSO). (K) Worm lifespan extension by metformin is suppressed in a prx-5 peroxisomal biogenesis mutant. (L) Worm lifespan extension by metformin is abolished by acetoacetate supplementation. (M) Acetoacetate synergizes with metformin to inhibit E. coli OP50 growth. Significance stars represent metformin effect (purple) and metformin-acetoacetate interaction (green). (N) Acetoacetate supplementation suppresses metformin-induced upregulation of worm Pacs-2::GFP expression in a concentration-dependent manner. Significance stars represent metformin effect (purple) and metformin-acetoacetate interaction (green). (O) Acetoacetate supplementation suppresses metformin-induced upregulation of multiple worm lipid metabolism and FAO-related genes. Significance stars represent metformin effect (purple) and metformin-acetoacetate interaction (green). (P) Suppression of metformin-induced upregulation of worm Pacs-2::GFP expression by acetoacetate is partially rescued by RNAi knockdown of Succinyl-CoA:3-Ketoacid-CoA Transferase OXCT-1/C05C10.3, a gene involved in the catabolism of ketone bodies including acetoacetate. This suggests that effect of acetoacetate partly depends on its utilization as metabolic fuel. Significance stars represent metformin effect (purple) and metformin-OXCT-1 interaction (green). Data are represented as mean ± SEM. ∗∗p < 0.01; ∗∗∗p < 0.001. See also Table S1 for lifespan statistics and Table S7 for fatty acid metabolomics statistics.
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