A metabolic signature for long life in the Caenorhabditis elegans Mit mutants
Version of Record online: 17 JAN 2013
© 2012 The Authors Aging Cell © 2012 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland
Volume 12, Issue 1, pages 130–138, February 2013
How to Cite
Butler, J. A., Mishur, R. J., Bhaskaran, S. and Rea, S. L. (2013), A metabolic signature for long life in the Caenorhabditis elegans Mit mutants. Aging Cell, 12: 130–138. doi: 10.1111/acel.12029
- Issue online: 17 JAN 2013
- Version of Record online: 17 JAN 2013
- Accepted manuscript online: 22 NOV 2012 09:23AM EST
- Manuscript Accepted: 2 NOV 2012
Fig. S1. Analysis of metabolite differences between wild-type (N2), isp-1(qm150), and mev-1(kn1) worms. The exometabolome data used to generate Fig. 1A were analyzed using a General Linear Model (GLM) to capture the major changes in concentration over time between each worm strain. Briefly, normalized MS peak area data for each metabolite were rank ordered across the 18 collection times and then grouped by strain. Significance testing, using strain as the predictor variable and rank as the dependent variable, was then performed (P < 0.05). This test was only sensitive to metabolite changes that were monotonic across time (up or down, see Fig. 1A). Nonetheless, even at this level of resolution, both isp-1 and mev-1 mutants are seen to produce several compounds in significantly altered amounts relative to wild-type worms (left two columns). Also, long-lived isp-1 Mit mutants markedly, and significantly, overproduce a variety of α-ketoacids and α-hydroxyacids relative to both N2 and mev-1 mutants (right column).
Figs. S2–S4. Cluster analysis of exometabolite data shown in Fig. 1A. A self-organizing map (SOM) algorithm (described under Material and Methods) was used to cluster metabolites that correlated temporally within the exometabolome data of N2, isp-1(qm150), and mev-1(kn1) animals; data are presented in Supplementary Figures 2, 3, and 4, respectively. In each figure, individual metabolite clusters are shown in (A). Metabolites that contributed to each cluster are listed in (B). In (A), individual metabolites are shown in gray. Black lines represent cluster averages. Metabolite levels were Z-score transformed before clustering.
Fig. S5. Exometabolome analysis of C. elegans Age mutants. A GLM analysis of the 47 exometabolome datasets collected from the strains listed in the lower Table (number of independent replicates is also shown) was undertaken to assess the differences and similarities between a variety of Age mutants and Mit mutants. Metabolites were collected from populations of 120 000 worms (per replicate) after an 18-h incubation period either in the presence or absence of oxygen. For each metabolite, normalized peak areas were rank-ordered across the 47 datasets, then binned into percentiles of 5%. A GLM was then employed for significance testing using strain as the predictor variable and rank as the dependent variable. No effort was made to test additional terms controlling for changes in collection date or experimenter. Contrasts were performed using SPSS (17.0). Significance was set at P < 0.0042 for each metabolite to account for the Type I error following the 12 tested comparisons. Notable is the almost indistinguishable nature of isp-1 and nuo-6 mutants, and of mev-1 and ucr-2.3 mutants. Only volatile metabolites (2-methylbutyric acid and all metabolites listed below it) were collected for clk-1(qm30). Nonvolatiles were collected using a different MS instrument and are analyzed separately in Supplementary Figures 6 and 7 below.
Figs. S6 and S7. Exometabolome analysis of additional C. elegans Age mutants. The +18-h exometabolomes of the following strains were collected and analyzed by GC-MS: wild-type worms (N2), wild-type worms exposed to anoxia during the collection period, isp-1(qm150) and clk-1(qm30) Mit mutants, short-lived mev-1(kn1) and ucr-2.3(pk732) ETC mutants, long-lived clk-2(qm37), and finally short-lived rad-5(mn159) mutants. The latter two mutants are allelic variants of the same genetic locus. We have segregated this data from that shown in Supplementary Figure 5 because it was collected using a different GC-MS instrument (refer to Materials and Methods for details). Columns represent independent experimental replicates (120 000 worms/replicate). Metabolite data were analyzed as described in Supplementary Figure 5, except binning was not used. In Supplementary Figure 6, data has been presented using hierarchical clustering – left panel, metabolite variation across row; right panel, metabolite variation relative to the entire array. Statistical analyses are provided in Supplementary Figures 7. Significance was set at P < 0.0062 for each metabolite to account for the expected increase in Type I error following the 10 tested comparisons.
Figs. S8 and S9. Mit mutants do not subsist on a metabolism normally reserved for growth under anaerobic conditions. Exometabolites were collected from the following strains after an 18-h incubation period either in the presence or absence of oxygen: N2, isp-1(qm150), clk-1(qm30), nuo-6(qm200), mev-1(kn1), and ucr-2.3(pk729). Metabolite levels were quantified using GC-MS and the effect of oxygen interrogated using a GLM. To monitor highly volatile compounds, a modified collection procedure was employed (refer to Materials and Methods). For the GLM, normalized peak areas were rank ordered across the 44 datasets then binned into percentiles of 5%. Oxygen served as the predictor variable and rank as the dependent variable. Contrasts were undertaken using SPSS (17.0). In Supplementary Figure 8, data has been segregated into (A) highly volatile (evaporative) compounds, and (B) standard assay compounds. Data are presented using hierarchical clustering – blue-yellow, metabolite variation across row; blue-red, metabolite variation relative to the entire array. Scale bars apply to all panels. Wild-type worms produce a distinct set of volatile compounds when exposed to anoxia – including butyric acid, isobutyric acid, 2-methylbutyric acid, isovaleric acid and tiglic acid (most pronounced in the lower display panel of (A)). There is an overt absence of these compounds from all normoxically cultured ETC mutants; suggesting that Mit mutants do not ectopically activate a metabolism normally reserved for survival under anoxia. Statistical analyses are presented in Supplementary Figure 9. The number of independent strain replicates is listed on the right. Significance testing was set at P < 0.017 and P < 0.0125 for the standard and volatile compounds, respectively. Although the data for isp-1, mev-1, and tpk-1 are clearly underpowered, even with this number of samples, the same compounds altered in the N2, nuo-6, ucr-2.3, and clk-1 following anaerobia are also trending toward significance in these animals. (−) indicates metabolite was either not detected or, for clk-1, samples were not collected.
Figs. S10–S15. Cluster analysis of exometabolite data shown in Fig. 2D. SOM clustering was used to identify groups of metabolites that correlated temporally across the exometabolome samples of each feeding RNAi condition in Fig. 2D. All metabolite levels were Z-score transformed before clustering. Treatment conditions included empty vector (pL4440), and DLD RNAi to empty vector ratios of 1:200, 1:20, 1:5, and 1:0. These five conditions are presented sequentially in Supplementary Figures 10 to 14. For each figure, the individual panels of (A) show unique metabolite clusters. Individual metabolites are shown in gray, and black line represents cluster averages. Only metabolites that contributed to each cluster are listed in (B). In Supplementary Figure 15, metabolite peak area data were analyzed similar to how the data for Supplementary Figure 1 were analyzed. That is, a GLM was used to capture the major changes in concentration over time between vector-treated worms and each of the DLD RNAi treatments. Briefly, normalized MS peak area data for each metabolite were rank ordered across the 30 collection times and then grouped by RNAi dilution. Significance testing, using RNAi concentration as the predictor variable and rank as the dependent variable, was then performed (P < 0.05). Contrast coding was performed with SPSS (17.0). As in Supplementary Figure 1, this test was only sensitive to metabolite changes that were monotonic across time (up or down, see Fig. 2D). Nonetheless, there is a significant and progressive change in the exometabolome profile of N2 worms as they suffer increasingly more severe loss of DLD. As predicted, the α-ketoacid levels are among the first metabolites affected.
Fig. S16. Correlation matrix showing metabolic similarity between long-lived Mit mutants, long-lived dld-1 disrupted animals, and long-lived tpk-1(qm162) mutants. The exometabolome profile of each of the strain replicates listed on the ordinate axis was used to calculate a distance measure between it and all other profiles. Distances were calculated using the CORRELATION algorithm of SPSS (17.0). In this procedure, degree of similarity was calculated as CORRELATION (x, y) = Σi(ZxiZyi)/N, where Zxi is the Z-score value of x for the ith metabolite, and N is the number of metabolites. Metabolites with missing peak area data were excluded from the calculation. No effort was made to adjust raw metabolite data for differences in collection date or experimenter. Within-strain correlations should approach 1.0; our data averaged 0.904 ± 0.089 (± 1 SD) (khaki). We set 0.726 as the lower (95%) boundary for the mean of same-strain comparisons (light blue). Red box highlights the similarity of tpk-1 mutants with nuo-6 and isp-1 Mit mutants, and with neat DLD RNAi-treated animals. Box in pure blue (bottom row) highlights similarity of neat DLD RNAi-treated animals also with nuo-6 and isp-1 Mit mutants.
Fig. S17. Summary statistics for survival curves shown in Fig. 2F. Increasing doses of RNAi targeting DLD (RNAi to empty vector ratio – 0:1, 1:1000, 1:500, 1:200, 1:100, 1:50, 1:20, 1:10, 1:5, and 1:0) were fed to wild-type C. elegans from the time of hatching and the effects on lifespan measured. Lifespan data for four independent replicates are shown. red indicates significantly different from vector, p < 0.005 (Bonferroni corrected).
Fig. S18. Individual disruption of the E1 subunits of α-ketoacid dehydrogenases does not increase wild-type lifespan. (A) RNAi targeting the E1α subunits of BCKADH and PDH or the E1 subunit of αKGDH was fed to worms from the time of hatching. All three RNAi treatments reduced adult size. (EV – empty vector (pL4440) control; all worms are of the same chronological age; scale bar: 200 μm). (B, C) Increasing doses of RNAi targeting individual E1 subunits (target gene to empty vector ratio – 0:1, 1:100, 1:50, 1:20, 1:10, 1:5, 1:2, and 1:0) were fed to wild-type C. elegans from the time of hatching and their effect on lifespan measured. Lifespan data represent a single dataset, except for vector control which was collected in triplicate (n = 60 worms/condition/replicate). In (B), asterisks indicates significantly different from vector average (pink line). Significance was set at P < 0.0024 (Bonferroni corrected).
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