A nutritional memory effect counteracts benefits of dietary restriction in old mice

Preservation of body weight associates with late onset DR outcome

The strong effect of a history of AL feeding on subsequent mortality under DR could indicate the presence of a physiological memory that may impede the molecular changes mediating the benefits of DR. Interestingly, there was a significant inverse association between the animal-specific rate of weight change and age at death for the ALDR but not DRAL cohort (Fig. 2a). There was, however, no association between the absolute weight pre-switch and lifespan in either of the switch groups (Fig. 2b). In agreement with previous findings on chronic DR regimens ^20,21, preservation of weight and, specifically, fat may thus increase the responsiveness of survival to late-onset DR, implicating a role of lipid metabolism.

Open in a separate window

Fig. 2

Post-switch weight change correlate with survival outcome under late-onset DR

a, Scatterplot representation of mouse-specific rates of weight change versus mouse-specific age at death for both ALDR and DRAL switch cohorts. Linear regression analysis found a significant association for weight change and age at death for the ALDR switch cohort. Animals with a weight loss higher than 0.2 g/d are marked. b, Scatterplot representation of mouse-specific weights at switch date versus mouse-specific age at death for both ALDR (green) and DRAL (orange) switch cohorts. Linear regression found no significant association. Animals with a weight loss higher than 0.2 g/d are marked. n=45 biologically independent animals per diet.

Brown and white adipose tissue but not liver show a transcriptional memory of AL feeding

In the light of a possible memory effect of AL feeding on lipid metabolism, we next investigated the molecular memory of AL feeding in liver, brown and gonadal white adipose tissue (BAT and WAT), which fulfill key functions in lipid turnover and storage. Tissues were sampled two months post-switch, when the effects on mortality were the most disparate between the two switch diets (compare Fig. 1e-g). In contrast, body (Fig. 1b) and adipose weights (Fig. 3b,c) indicated that the two diet switches had already caused comparable changes in fat tissue mass at this time point (Fig. 3a). RNA-seq profiling revealed high transcriptional similarity between DRAL mice and chronic AL controls in all three tissues, indicating that late-onset AL feeding induced a transcriptional profile similar to chronic AL feeding. Similarly, hepatic transcriptional profiles from ALDR mice clustered with the DR controls. In strong contrast, in BAT and even more in WAT, ALDR profiles clustered with those of the AL diet (Fig. 3c,d), and were thus resistant to the diet switch. Chronic DR caused significant expression changes in 3569 genes in liver, 2412 in BAT and 3296 in WAT, when compared to AL controls. Of these, only 62 genes (~2%) in the liver, but 866 genes (~35%) and a total of 1609 genes (~50%) in the WAT, were still differentially expressed between DR and ALDR mice two months post-switch (Fig. 3e). These “switch-resistant” genes in the adipose tissues are candidates for a transcriptional memory of AL feeding. In DRAL switch mice, we detected only 22 (0.8%) switch-resistant genes in the liver, 19 (0.08%) in BAT and 423 (~13%) switch-resistant genes in the WAT (Fig. 3f).

Open in a separate window

Fig. 3

Detection of an age- and tissue-specific transcriptional memory of prior AL feeding

a, Organ weight of gonadal fat pads at dissection time point (805 days). One-way ANOVA followed by two-sided post-hoc Tukey test; n=10 biologically independent animals per diet. Means ± SEM. b-d, Principal component analysis plot of RNA-seq data in b liver, c brown adipose tissue (BAT) and d gonadal white adipose tissue (WAT). e-f, Venn diagrams depicting the overlap of differentially expressed genes in liver, BAT and WAT e under DR feeding, relative to the AL or ALDR group; and f under AL feeding, relative to the DR or DRAL groups. Switch-resistant genes are highlighted in red. g, Boxplot representation of scaled expression levels of differentially up- and down-regulated genes under chronic DR as opposed to chronic AL controls in all three tissues. Expression levels of ALDR switch-resistant genes are indicated Boxplots. Scatterplots depicting the expression change for each gene under DR or ALDR feeding (relative to AL). Whiskers represent 1^st and 5^th quartile, box edges represent 2^nd and 4^th quartile and centre line represents 3^rd quartile/median. h, Heatmap of unsupervised clustering of expression changes for ALDR switch-resistant genes in WAT; color bar represents z-score range. i, Schematic representation of the DR switch experiment in young mice. j, Boxplot representation of scaled expression levels of differentially up- and down-regulated genes under chronic DR as opposed to chronic AL controls in BAT and WAT of ALDR switch mice at young age. Boxplots are defined as in g, k Representative Gene ontology (GO) enrichment of ALDR switch-resistant genes in BAT (left) and WAT (right). Lengths of bars represent negative ln-transformed p-values using two-sided Fisher’s exact test. Cells indicate gene-wise log2-foldchanges (log2FC) between DR and ALDR fed mice. The complete list of enriched GO terms can be found in Supplementary Tables 2 and 3. Biologically independent animals used for RNA-seq: Liver: n=3 (AL, DR, ALDR, DRAL, ALDR 5m, AL 5m); BAT: n=3 (AL, DR, DRAL, AL 5m, ALDR 5m) n=5 (ALDR); WAT: n=3 (AL, DR, ALDR, AL 5m) n=5 (ALDR, DRAL). *** p<0.0001.

To analyze the transcriptional similarity between chronic and newly DR animals, we focused on genes that were differentially up- or down-regulated under chronic DR (Fig. 3g). Plotting for each gene the scaled expression in response to chronic or late-onset DR confirmed an almost complete transcriptional adaptation to the ALDR switch in the liver, while both adipose tissues remained largely refractory (Fig. 3g). Corresponding to the acute rise in mortality, DRAL mice broadly adopted the expression profile of chronic AL fed animals across tissues, as did ALDR mice in the liver. (Fig. 3g). In addition, unsupervised hierarchical clustering revealed that ALDR switch-resistant genes in adipose tissues were not resistant in general, because their expression adopted an AL-like pattern under the reciprocal DRAL switch (Fig. 3h and Extended Data Fig. 3a). Thus, the liver transcriptome remained acutely responsive to either diet change, whilst the adipose tissue was specifically unresponsive to late-onset DR.

The incomplete reprogramming of RNA expression in response to late-onset DR could simply indicate that the adipose tissues respond slowly to DR, which would argue against the presence of a specific memory of AL feeding. To investigate this possibility, we repeated the experiment in young mice by switching AL fed animals to DR (young ALDR) starting at 12 weeks of age (Fig. 3i). Identical to the experiment in old mice, tissues were collected two months post-switch for expression profiling. Strikingly, there was a complete transcriptional reprogramming in all three tissues (Fig. 3j and Extended Data Fig. 3c,d). Genes that were resistant to the switch in old ALDR mice showed full sensitivity in young ALDR mice. Thus, the adipose tissue transcriptome was highly responsive to newly applied DR switches in young mice, but this transcriptional flexibility markedly declined with age, in particular in the WAT.

Taken together, switching mice from DR back to full feeding caused a rapid loss of DR-related RNA expression patterns, implying no or only a weak memory of prior DR regimen. In contrast, chronic AL feeding caused the formation of an adipose-tissue-specific gene expression memory over time. This reduced transcriptional flexibility of BAT and WAT in response to DR mirrored the resistance of age-specific mortality to late-onset DR.

Chronic AL feeding causes a proinflammatory expression pattern in preadipocytes

Age-related changes have been previously identified to impair the differentiation of WAT preadipocytes into brite adipocytes under cold-exposure in aged mice ^25–27, and we hypothesized that a similar process could contribute to the reduced transcriptional flexibility of the WAT in old AL mice. To test this hypothesis, we obtained publicly available single cell (sc) RNA-seq data of the gonadal stromal-vascular-fraction (SVF) from the Tabula muris consortium ²⁸, comprising cells isolated from 3 and 24 months old AL fed mice. Indeed, preadipocytes from old mice exhibited profound transcriptional shifts, including decreased expression of growth/differentiation factors and elevated expression of inflammatory response genes and secretable cytokines such as Ccl2 and Ccl7 (Extended Data Fig. 4a-d). We further found a significant, inverse association between the gene expression changes in aged preadipocytes and those observed under DR with bulk RNA-seq. This association was still significant when limited to ALDR switch-resistant genes, which were functionally enriched for inflammatory response genes and chemokines (Extended Data Fig. 4e,f, Supplementary Table 1). Many of these genes were found to be particularly, or even exclusively, expressed in preadipocytes (Extended Data Fig. 4g,h). Thus, expression patterns in bulk RNA-seq may indeed reflect those found by scRNA-seq. Our analysis thus strongly suggests that preadipocytes lower the expression of growth/differentiation factors associated with WAT plasticity ^29,30, while acquiring an inflammatory phenotype during ageing on AL diet. This phenotype appears to be strongly prevented by DR, but not by late-onset DR. The transcriptional memory of AL diet may thus be, in part, rooted in preadipocytes.

Mitochondrial biogenesis in WAT is impaired under late onset DR

We next investigated the molecular pathways affected by the transcriptional memory of AL feeding, since these could point to mechanisms that improve health under chronic DR. Whereas switch-resistant genes in the liver showed no major gene ontology enrichment (Extended Data Fig. 5a), the corresponding analysis in BAT revealed increased lipid transporter activity and largely down-regulated mitochondrial function, including markers of uncoupling-dependent thermogenesis (Fig. 3k, Extended Data Fig. 6a-d; Supplementary Table 2). Switch-resistant genes in WAT showed the strongest functional enrichment for various mitochondria-related pathways, fatty acid metabolism and phospholipid biosynthesis, all of which were up-regulated under chronic and young ALDR but not old ALDR feeding (Fig. 3k; Supplementary Table 3). In addition, we identified a switch-resistant reduction of inflammatory response and interferon gamma-related genes. The increase in mitochondrial gene expression in WAT contrasts with the down-regulation of the same gene set in BAT, indicating that the declining transcriptional flexibility acts in a tissue-specific manner.

To test whether the shifts in gene expression could be correlated with functional consequences, we characterized the predicted switch-resistant up-regulation of mitochondrial function in the WAT, since this tissue exhibited the most switch-resistant genes and strongest functional enrichment. Indeed, genes associated with mitochondria in WAT (both nuclear and mitochondrially encoded genes) showed globally increased expression under chronic DR compared with either switch diet (Fig. 4a). Furthermore, expression of the transcription factor Pgc1α, a key driver of mitochondrial biogenesis, was significantly increased in chronic DR and in young ALDR mice but was resistant to the ALDR switch (Fig. 4b). Consistent with this observation, chronic DR induced increased abundance of mitochondrial DNA (mtDNA), protein levels of mitochondrial complex I and IV subunits (mtCO1; NDUFA9) and key mitochondrial metabolites (Propionyl- and Succinyl-CoA), all of which were resistant to the ALDR switch (Fig. 4c-e, Extended Data Fig. 6e). Of note, expression levels of various thermogenic processes such as UCP1 (uncoupling ³¹), Ckmt1 (Creatine cycling ³²) and Serca2b (Ca²⁺ cycling ³³) were unaffected by diet (Fig. 4f,g, Extended Data Fig. 6 f-h). Remarkably, all parameters of mitochondrial activity reverted back to the level of AL controls in the DRAL switch (Fig. 4b-e), in line with only weak mRNA expression differences between chronic and late-onset AL and only a small fraction of the DR–related transcriptional program still being active (Fig. 3f-h, Extended Data Fig. 6i, Supplementary Table 4). Long-term AL feeding thus interfered with elevated, uncoupling-independent mitochondrial biogenesis and activity in the WAT in response to late-onset DR.

Open in a separate window

Fig. 4

White adipose tissue-specific impairment of mitochondrial biogenesis under late-onset DR.

a, Distribution of gene-wise expression changes in WAT under chronic DR and switch diets relative to chronic AL feeding for genes associated with the GO term ‘Mitochondrion’ (n = 1299 genes). Whiskers represent 1^st and 5^th quartile, box edges represent 2^nd and 4^th quartile and centre line represents 3^rd quartile/median. Two-sided Wilcoxon rank-sum test, adjusted for multiple testing. b, Pgc1α (Ppargc1a) mRNA expression (RNA-seq) in WAT. Two-sided Wald test, adjusted for multiple testing. c, mtDNA copy number in WAT. One-way ANOVA, two-sided Tukey post hoc test. d, Western blot analysis of mtCO1 and NDUFA9 in WAT with α-tubulin as loading control. One-way ANOVA, two-sided Tukey post hoc test. e, Propionyl- and Succinyl-CoA levels in WAT. One-way ANOVA, two-sided Tukey post hoc test. f, mRNA expression (RNA-seq) of thermogenic marker genes in WAT. Two-sided Wald test, adjusted for multiple testing. g, mRNA expression (qRT-PCR) of Ucp1, Srebf1, Acaca (ACC), Fasn and Elovl6 in the WAT relative to Pol2ri. One-way ANOVA, two-sided Tukey post hoc test. Biologically independent animals used: RNA-seq: n=3 (AL, DR, ALDR, AL 5m) n=5 (ALDR, DRAL); Western blot, qPCR and lipidomics: n=4 per diet. Means ± SEM, *** q<0.0001.

De novo lipogenesis in WAT exhibits the strongest memory of prior AL feeding

In order to find putatively causal mediators of the transcriptional memory in the WAT, we plotted for each gene the log2-fold expression change in response to chronic or late-onset DR relative to chronic AL and ranked each gene’s influence on the resulting linear fit. The 50 switch-resistant genes with the highest influence (Cook’s distance; Fig. 5a,b) were enriched primarily for lipid metabolism, including fatty acid (FA) biosynthesis, triglyceride (TG) metabolism and phospholipid biosynthesis (Fig. 5c). They included the key lipogenic transcription factor Srebf1 ³⁴ and several of its corresponding downstream targets (Fig. 5d,e), Acly, ACC, Fasn, Scd1 and Elovl6, which code for key enzymes in FA synthesis, desaturation and elongation ³⁴. Their expression was strongly up-regulated (some more than ten-fold, (Fig. 5e and ​and4g)4g) in chronic DR and in young ALDR mice, but they remained largely refractory to the ALDR switch at old age.

Open in a separate window

Fig. 5

White adipose tissue-specific transcriptional memory predicts impaired activation of de-novo lipogenesis under late-onset DR.

a, Scatterplots depicting the expression change for each gene under DR or ALDR feeding (relative to AL) in WAT. The top 50 switch-resistant genes are highlighted in red. b, Gene-wise Cook’s distances from weighted linear regression analysis of expression changes under DR or ALDR feeding (relative to AL) in WAT. c, GO enrichment of the top 50 ALDR switch-resistant genes. Cells indicate gene-wise log2-foldchanges (log2FC) between DR and ALDR fed mice. Lengths of bars represent negative ln-transformed p-values using two-sided Fisher’s exact test. d, Srebf1 mRNA expression (RNA-seq). e, mRNA expression (RNA-seq) of key de-novo fatty acid synthesis genes in WAT. Two-sided Wald test, adjusted for multiple testing. f-h, Abundance of f the total pool of free fatty acids, g, Palmitate and h, Palmitoleate in WAT. One-way ANOVA followed by two-sided post-hoc Tukey test. i, Heatmap of unsupervised clustering of abundance changes for measured lipid classes in WAT; color bar represents z-score range. Black box highlights lipids with induction specifically in young ALDR mice. Biologically independent animals used for RNA-seq: n=3 (AL, DR, ALDR, AL 5m) n=5 (ALDR, DRAL). The RNA-seq experiment has been done once. Biologically independent animals used for Lipidomics: n=4 per diet. Means ± SEM, *** q<0.0001.

We next explored the metabolic consequences of these changes in gene expression, by full liquid chromatography–tandem mass spectrometry (LC-MS/MS) profiling of the WAT lipidome. This allowed quantification of 516 lipid species and 32 different classes with a dynamic range of ~5x10⁷, including the major neutral lipid, (lyso-)phospholipid and sphingolipid classes ³⁵. Our dataset thus permitted a global and unbiased analysis of cellular lipid dynamics (Supplementary Table 5). Consistent with transcriptional up-regulation of de-novo lipogenesis, we detected elevated levels of free FAs in chronic DR and young ALDR mice, including intermediate metabolites of FA synthesis, palmitate and palmitoelate (Fig. 5f-h). In contrast, late-onset DR did not cause a similar increase in FAs, while DRAL mice had FA levels lowered to those of chronic AL controls. These results suggest imperfect activation of lipogenesis as a direct consequence of the transcriptional memory in the WAT of old ALDR mice.

Chronic DR causes white adipose tissue autonomous reprogramming of phospholipid synthesis

To better understand the possible functions of newly synthesized FAs in the WAT of DR fed mice, we conducted unsupervised hierarchical clustering of all measured lipid classes. Free FAs clustered closely with several phospholipid classes (Fig. 5i), such as phosphatidic acid (PA), phosphatidylethanolamine (PE) and phosphatidylcholine (PC), which were all elevated in chronic DR fed mice (Fig.s 5i and ​and6a).6a). Levels of neutral TGs, however, were markedly reduced (Fig.s 5i and ​and6a).6a). In addition, fatty acyl-CoA and diglycerides, which mark the transition between FA synthesis and biogenesis of complex lipid structures ³⁶, showed a strong peak in young ALDR mice only (Fig.s 5i and ​and6a).6a). Consistent with the major transcriptional reprogramming, DR thus instigated broad-spectrum changes in the WAT lipidome. In agreement with previous studies ²³, DR fed mice appeared to use newly synthesized FAs to build various types of membrane lipids, and this process remained refractory to the ALDR switch at old age.

Open in a separate window

Fig. 6

DR remodels lipid flux in adipose tissue

a, Abundance of lipid classes involved in synthesis of complex lipid molecules as measured in WAT; n=4 biologically independent animals per diet. One-way ANOVA followed by two-sided post-hoc Tukey test. Triglycerides were extracted separately and absolute lipidome values cannot directly be compared to other lipid classes. b, Schematic representation of the WAT explant culture experiment. The experiment was done once with n=3 biologically independent, 24 months old animals per diet. Each isolated WAT depot was equally distributed across three wells and incubated with NBD-PG and transfectant (n=2 technical replicates) or transfectant only (n=1 negative control) c, Representative cellular uptake profiles of exogenously supplied NBD-PG explant-cultured adipocytes. Lipids were separated by TLC and analyzed by fluorescence scanning. Lipid species with low polarity run on top. Standard phospholipids allowed the identification of lipid spots representing triglyceride (TG), diglyceride (DG) and PG levels (the asterisks indicate unidentified lipid species). Full TLC scan and analysis details are presented in Extended Data Fig. 8. d, Relative fluorescence intensity profile in AL- and DR-derived explant cultures (biological replicates n=3; technical replicates n=2; the asterisks indicate unidentified lipid species). e, Relative fluorescent signal in the TG band. n=3 biologically independent, 24 months old animals per diet; technical replicates were averaged prior analysis. Two-sided t test.

Means ± SEM, *** q<0.0001.

Consistent with the shift towards phospholipid synthesis, levels of neutral TGs were markedly reduced (Fig.s 5i and ​and6a).6a). Notwithstanding, we assumed that at least a fraction of newly synthesized FAs would also be incorporated into TGs. Given that rapid de-novo synthesis results in generally shorter and less saturated FAs, we thus analyzed the relative TG profile for global shifts in elongation and desaturation. Indeed, chronic and short-term DR at young age decreased the chain length of TG-associated fatty acids, a shift not observed under late-onset DR (Extended Data Fig. 7a,b). Similarly, the relative fraction of saturated and mono-unsaturated TGs was elevated in young ALDR and chronic DR fed animals, but not in old ALDR mice (Extended Data Fig. 7c,d). The molecular composition of TGs in the WAT thus further supported highly active lipogenesis under chronic but not late-onset DR.

To experimentally verify that the observed phospholipid profiles were a consequence of tissue-specific changes in lipid utilization and not confounded by lipid import from other tissues (such as the liver), we conducted an ex-vivo pulse-chase experiment (Fig. 6b). To this end, we employed highly water-soluble phosphatidylglycerol (PG) with fluorophor-labeled fatty acyl groups (NBD-PG), which, in contrast to most lipid classes, is readily taken up and used by mammalian cells in culture ³⁷. Adipose tissue explants from freshly isolated WAT of chronically DR and AL fed 24-month-old mice from an independent cohort were seeded for 24 hours, before incubating the tissue cultures with NBD-PG and a transfection agent for 48 hours. Subsequent lipid extraction and thin-layer chromatography (TLC) visualized the distribution of fluorescent fatty acyl groups among several lipid classes, representing new lipid molecules that were synthesized by turnover of NBD-PG (Fig. 6b,c and Extended Data Fig. 8a,b).

Lipid extracts of both DR- and AL-derived adipocytes showed a clear and equally strong fluorescent PG band, indicating potent cellular uptake of NBD-PG, and the specificity of the fluorescence was confirmed by tissue cultures incubated without PG (Fig. 6c,d and Extended Data Fig. 8a-c). In agreement with the steady-state lipid levels measured by lipidomics, the fluorescent signal was differentially distributed among the individual lipid classes in a diet-dependent manner, suggesting a global shift in use of lipid mass (Fig. 6d). Indeed, DR-derived adipocytes showed markedly weaker signal in the TG band, indicating reduced breakdown of phospholipids to fuel neutral storage fat synthesis (Fig. 6d,e). Instead, DR-derived samples exhibited significantly increased fluorescence intensity in fractions with higher polarity, especially diglycerides, which run below the TG band (Fig. 6d,e and Extended Data Fig. 3a,b). Unidentified lipid fractions with even higher polarity (which can include fatty acids and membrane lipids), were also significantly increased under DR. Thus, DR-fed mice rewired the lipid flux in WAT autonomously so that TG synthesis was reduced while diglycerides and membrane lipids were increasingly built up.

Post switch lifespan and mortality analysis

Animals that died before the diet switch were eliminated from the mortality analysis. Cox regression of post-switch survival curves was performed using custom RStudio (https://www.rstudio.com/) scripts and the following packages: survival, survminer and flexsurv (Reporting Summary). Survival data were modeled with two factors, diet and breeding cohort (~ diet + cohort; diet factor levels: AL, DR, ALDR, DRAL; cohort factor levels: F1, F2, F3). Schoenfeld residuals were analyzed to confirm that the data underlying the Cox regression in Fig. 1C met the proportionality assumption. Contrasts were used to compare the hazard ratios between ALDR relative to AL and between DRAL relative to DR (ALDR_AL and DRAL_DR; ‘Switch vs past diet’). We repeated the analysis with altered contrasts to analyze the hazard ratio difference between ALDR relative to DR and the DRAL relative to AL (ALDR_DR and DRAL_AL; ‘Switch vs new diet’). In order to directly compare the effects of each diet switch relative to their previous diet, we further introduced a contrast to subtract the hazard ratios of ALDR_AL from DRAL_DR (‘Hazard ratio difference’; Fig. 1G).

Analyses were repeated for each cohort separately, for which the ‘cohort’ factor was omitted. To determine if the results of the cohort-wise Cox regression could be accounted for by the larger cohorts of animals under DR and DRAL feeding, which could alone have produced more significant differences for comparisons involving those groups, we repeated each analysis 1000 times while randomly down-sampling the DR and DRAL groups to match the number of AL- and ADLR-fed mice. The resulting distributions of p-values for each analysis were plotted as boxplots (Extended Data Fig. 1C).

To test for acute effects of either switch diet, we performed Cox regression for the first two months post-switch, censoring all mice that were still alive at the end of that period. We repeated the Cox regression and iteratively extended the analyzed time interval by one month. P-values and hazard ratios for ALDR_AL, DRAL_DR and the hazard ratio difference were recorded for each iteration (Fig. 1d,g).

Visualizing age specific mortality rate

Events of death were summarized in bins of 10 days. Mortality (μx) was estimated as μ_x = -ln (p_x), where p_x is the probability of an individual alive at age x-1 surviving to age x. Data for Fig. 1e,f were smoothed by averaging μ_x over 3 10-day bins. Mortality trajectories were truncated when n < 40 AL fed mice (equivalent to 25% survival).

Calculating rate of weight change

We monitored the weight of 15 mice per diet group and breeding cohort. We used generalized additive modeling to determine the inflection point of the weight gain/loss curve, which we found at day 889 (equivalent to ~ 145 days on new diet). Rate of weight change for each animal was estimated by linear regression over the weight trajectory for this interval. We thereby limited the analysis to the interval before the trajectories plateaued.

In order to compare the weight increase between chronic and late-onset AL mice, we performed linear modeling of the weight change in young chronic AL fed mice, starting at the same average body weight as the DRAL cohort. To allow comparability, we analysed only the period until the AL fed mice reached the same weight as DRAL animals at the inflection point.

Analysis of food intake quantification

Average food intake of chronic or late-onset AL fed mice was monitored through food consumption per cage. In order to normalize food consumption to body weight, we estimated the average body weight from the weighing group.

RNA sequencing and analysis

We isolated RNA from liver, BAT and epididymal white adipose tissue (WAT) of AL, DR, ALDR and DRAL female mice at old age (27 months), as well as AL and ALDR female mice at young age (5 months). For liver tissue, we profiled the transcriptome of three biological replicates per treatment/age group. For BAT, we profiled three biological replicates per treatment/age group, with two extra replicates for old ALDR mice. In case of WAT, we profiled three biological replicates per treatment/age group, with two extra replicates for old ALDR and DRAL mice from the same cohort (three to five). RNA was isolated using Trizol Reagent (Thermo Fisher Scientific, Germany) according to the manufacturer’s protocol before treating samples with DNAse using the TURBO DNA-free Kit (Thermo Fisher Scientific, Germany). RNA quality was measured using the Agilent TapeStation System (Agilent Technologies, Germany). RNA-seq library preparation and sequencing were performed by the Max Planck-Genome-Centre Cologne, Germany (http://mpgc.mpipz.mpg.de/home/). According to the facility’s procedure, stranded TruSeq RNA-seq libraries were prepared as described in ⁶⁸, using 3 μg of rRNA-depleted RNA as input for liver and WAT, and 1 μg polyA-enriched RNA for BAT. Multiplexed libraries were sequenced with 2x40 mio, 100 bp paired-end reads on an Illumina HiSeq2500 (Illumina, San Diego, California, USA) liver and WAT, and 1x25 mio, 150bp reads for BAT. Liver RNA-seq data for young ALDR, young AL, old DR and old AL-fed mice were previously published ²² and are publicly available under the Gene expression omnibus (GEO) ID {"type":"entrez-geo","attrs":{"text":"GSE92486","term_id":"92486"}}GSE92486. Liver RNA-seq data for old ALDR and DRAL-fed mice and RNA-seq data from BAT and WAT are available under GEO ID {"type":"entrez-geo","attrs":{"text":"GSE124772","term_id":"124772"}}GSE124772.

Raw sequence reads were trimmed to remove adaptor contamination and poor-quality reads using Trim Galore! (v0.3.7, parameters: --paired --length 25). Trimmed sequences were aligned using Tophat2 ⁶⁹ (v2.0.14, parameters: --no-mixed --library-type=fr-firststrand -g 2 -p 15 -r 500 --mate-std-dev 525). Multi-mapped reads were filtered. Data visualization and analysis were performed using SeqMonk (http://www.bioinformatics.babraham.ac.uk/projects/seqmonk/), custom RStudio (https://www.rstudio.com/) scripts and the following Bioconductor packages: Deseq2 ⁷⁰, topGO ⁷¹ and org.Mm.eg.db (Reporting Summary). To account for tissue-specific expression, we defined all genes with an FPKM of > 2 in at least half of all samples as ‘expressed’. Unless stated otherwise, the set of expressed genes was used as background for all functional enrichment analyses involving expression data. P-values were adjusted for multiple testing.

To identify global expression changes for genes associated with mitochondria, we retrieved the list of genes associated with the gene ontology term ‘mitochondrion’ (GO:0005739) and plotted the log2 fold changes for each diet group as opposed to the chronic, old, AL diet group.

Single cell RNA sequencing analysis

Pre-processed and annotated scRNA-seq data (FACS followed by Smart-seq2 protocol) from gonadal adipose tissue (3 and 24 months old) were obtained from the Tabula Muris Senis consortium ²⁸. Given the lack of data from aged female mice, we focused our analyses on samples derived from male mice. Additionally, cells with less than 200 or more than 6,500 genes were excluded. This yielded 1,962 high quality cell transcriptomes derived from four young and four old biological replicates. Data visualization and analysis were performed using custom Rstudio scripts and the following Bioconductor packages: Seurat (version 3) ⁷² and topGO (Reporting Summary). Data normalization, scaling and identification of variable genes was performed using Seurat’s built-in vst method with 2000 features to select. A shared-nearest-neighbors graph was constructed using the first 10 PC dimension before clustering cells using Seurat’s built-in FindClusters function with a resolution of 0.4 and default parameters. Annotations for preadipocytes were adopted from the Tabula Muris Senis consortium (referred by the consortium as ‘mesenchymal stem cell of adipose’). Differentially expressed genes between young and old preadipocytes were identified using Seurat’s FindMarkers function (parameters: only.pos=F min.pct=0.01 thresh.use=0.01, test=’MAST’). In order to compare the expression changes observed on the single cell level with mRNA patterns on the tissue level, functional enrichment analysis was run with the set of WAT bulk RNA-seq expressed genes as background.

To test for a potential association between gene expression changes measured in aged preadipocytes and in WAT of DR fed mice, we considered only genes changing significantly in both datasets. We plotted log2-fold expression changes during pre-adipocyte ageing versus expression changes on the tissue level under DR, and the distribution of genes among the four resulting quadrants was tested for directionality using Fisher’s exact test. ALDR switch-resistant genes that showed a significant inverse association between both datasets were further analyzed for functional enrichment using topGO.

Comparing transcriptional shifts between diet groups

To compare the global transcriptional shifts induced by chronic and late-onset DR, we defined significantly up- or down-regulated genes between chronic DR and AL as a reference set. For each of these genes, expression values were scaled by the root-mean-square (using R’s scale function) across all samples. The resulting distribution for all genes was visualized as boxplots. The expression patterns for ALDR switch-resistant genes in ALDR switch mice were additionally highlighted.

To identify top switch-resisting genes, we performed weighted linear regression by correlating the expression changes across all genes. For each gene, we retrieved and correlated the log2 fold change values from Deseq2 for the comparisons of chronic DR versus chronic AL and late-onset DR versus chronic AL. In addition, we provided the log10-transformed average expression (‘base mean’) for each gene as weight for the linear fit, to compensate for lowly expressed genes having larger log2 fold changes. Genes were ranked according to their Cook’s distance and the top 50 genes that were also classified as ‘switch-resistant’, were selected. Given that both x- and y-axis represent data normalized to the same reference group (i.e. are not independent), the resulting correlation may be estimated incorrectly. Expression levels of selected candidates were therefore verified via Quantitative-Real-Time PCR.

Quantification of RNA Transcripts by Quantitative Real Time PCR

Quantitative Real Time PCR (qRT-PCR) was conducted on tissues that were derived from the same tissue collection group (but not identical mice) as the ones used for RNA-seq. In order to isolate total RNA from WAT for q-RT-PCR analysis, samples were homogenized in Trizol (#15596018, ThermoFisher Scientific), incubated 5min at RT and then centrifuged at full-speed for 10 min at 4°C in a tabletop centrifuge. To avoid carry-over of the resultant fat layer, the Trizol subnatant was carefully transferred to a fresh tube, mixed with 200 μl chloroform (366927-100 ml, Sigma) and incubated for 10min at RT prior to centrifugation at 12.000g for 15min at 4°C. The aqueous RNA-containing phase was transferred to a fresh tube, mixed with 500 μl isopropanol, 50 μl 3 M Sodium acetate and 15 μl GlycoBlue™ Coprecipitant (AM9515, ThermoFisher Scientific) and incubated for 10min at RT followed by centrifugation at 12.000g for 10 min at 4°C. The supernatant was removed and the pellet was washed twice with 500 μl ice-cold 70% ethanol and centrifuged at 7.500g for 5 min at 4°C. Pellets were air-dried for 15min at RT and re-suspended in 50 μl DEPC-treated, autoclaved ddH₂O followed by Dnase treatment to remove genomic DNA contaminations using the DNA-free™ DNA Removal Kit (AM1906, Invitrogen). RNA concentrations were measured using the Qubit™ RNA BR Assay Kit ({"type":"entrez-protein","attrs":{"text":"Q10210","term_id":"1723278","term_text":"Q10210"}}Q10210, ThermoFisher Scientific). 1.5 μg of RNA was used for first-strand cDNA synthesis using SuperScript VILO Master Mix (#11755-500, ThermoFisher Scientific) with 120 min incubation at 42°C to increase cDNA yield. Q-RT-PCR analysis was conducted using the Taqman Gene Expression Master mix (#4369106, Applied Biosciences) and the following Taqman probes (ThermoFisher Scientific): Srebf1 (Mm01138344_m1), Acaca (Mm01304257_m1), Elovl6 (Mm00851223_s1), Fasn (Mm00662319_m1), Pol2ri (Mm01176661_g1), UCP1 (Mm01244861_m1). Pipetting was carried out using a Janus Automated Workstation (PerkinElmer). qRT-PCR was done on a QuantStudio 6 Flex Real-Time PCR System (ThermoFisher Scientific) and gene expression was calculated using the 2-ΔΔCT method with Pol2ri expression as internal control and normalized to the respective gene expression level of the AL control group.

Protein purification and western blotting

Protein purification and western blotting were conducted on tissues that were derived from the same tissue collection group (but not identical mice) as the ones used for RNA-seq. For western blot analysis, WAT samples were homogenized in Pierce™ RIPA Lysis and Extraction Buffer buffer (#89900, ThermoFisher Scientific) supplemented with PhosSTOP™ phosphatase inhibitor cocktail (#4906837001, Roche) and cOmplete™, Mini, EDTA-free Protease Inhibitor Cocktail (#11836170001, Roche). Homogenates were incubated for 10 min on ice and then sonicated for 5 min. After centrifugation for 15 min at 4°C full-speed in a table top centrifuge, protein extracts were transferred to fresh tubes and protein concentrations were quantified using the Pierce™ BCA assay (#23225, ThermoFisher Scientific). 25 μg of protein extract per sample was separated on 12% acrylamide gels (#5678044, Criterion™ TGX Stain-Free™ Protein Gel, Biorad) and blotted on PVDF membranes (Immobilon-FL IPFL00010, Merck) for 1 hr at 100V on ice. Membranes were blocked for 1 hr at RT in Odyssey® Blocking Buffer (TBS) (927-50000 LI-COR Biosciences) followed by overnight incubation in the following primary antibodies diluted in Odyssey® Blocking Buffer: NDUFA9 (1:1000, AB_301431, Abcam), mtCO1 (1:1000, AB_2084810, Abcam), α-Tubulin 11H10 (1:1000, AB_2619646, Cell Signaling Technology). Blots were washed four times with TBS 0.2% Tween (TBS-T), incubated with fluorescently labeled secondary antibodies (1:15000, IRDye 680RD, (AB_10956166, LI-COR Biosciences), IRDye 800CW (1:15000, AB_621842, LI-COR Biosciences)) diluted in Odyssey® Blocking Buffer for 1h at RT followed by four washing steps with TBS-T at RT. Image acquisition was done using an Odyssey Infrared Imaging System (LI-COR Biosciences). For the western blot analysis of UCP1, samples were blotted on Amersham™ Hybond® PVDF membranes (GE10600023, Merck), blocked for 1h at RT in 5% non-fat dry milk powder (A0830, 1000 PanReac AppliChem) and washed three times in TBS-T. Membranes were incubated OVN with the following primary antibodies diluted in sterile filtered 5% fatty-acid free bovine serum albumin (BP9704-100, Fisher Scientific) in TBS-T: UCP1 (1:1000, AB_2687530) or α-Tubulin 11H10, (1:1000, AB_2619646) both obtained from Cell Signaling Technology. Blots were washed three times in TBS-T, incubated with anti-rabbit HRP- coupled secondary antibodies (1:10000, AB_2536530, ThermoFisher Scientific) diluted in 5% milk in TBS-T for 1h at RT, followed by three washing steps in TBS-T before incubation in ECL solution (Pierce™ ECL Plus Western Blotting Substrate, 32132, ThermoFisher Scientific) and image acquisition on a ChemiDoc™ XRS+ System (Biorad). After UCP1 image acquisition, blots were stripped in 0.5M sodium hydroxide (S8045-500G, Sigma) for 1h at RT, washed three times in TBS-T before ECL incubation and image acquisition to control for residual UCP1 signal. Blots were washed three times in TBS-T before blocking and incubation in primary α-Tubulin antibody as described before.

Protein bands were quantified using the Fiji software package ⁷³ with α-Tubulin as loading control. Samples were normalized against the respective AL control.

Analysis of mtDNA copy number

mtDNA copy number quantification was conducted on tissues that were derived from the same tissue collection group (but not identical mice) as the ones used for RNA-seq. To analyze mtDNA copy number, total DNA of WAT samples was isolated using the DNA Blood and Tissue kit (69506, Qiagen) with an additional centrifugation step at 200g for 5 min after lysis in ATL buffer. DNA concentrations were quantified using the Qubit dsDNA BR Assay kit ({"type":"entrez-protein","attrs":{"text":"Q32853","term_id":"75280860","term_text":"Q32853"}}Q32853, ThermoFisher Scientific). QPCR was carried out in a QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems) using the Taqman Universal PCR Master Mix (Applied Biosystems). Reactions were run in quadruplicates on 384-well plates using 5 ng of total DNA per reaction. Specific Taqman probes were used to quantify the nuclear 18S gene, (Hs99999901_s1) and the Rnr2 (Mm04260181_s1), Atp6 (Mm03649417_g1) and Cox1 (Mm04225243_g1) genes for mtDNA (ThermoFisher Scientific). Data were analyzed using a standard curve method and relative mtDNA content was calculated by the ratio of mtDNA probes relative to genomic DNA (mtDNA/18S). Results were normalized to the relative mtDNA content of the AL control group.

Lipidome measurement and analysis

Extraction, measurement and quantification of lipids were performed following published protocol ⁷⁴. Therefore, 50 mg of WAT from the same mice used for RNA-seq measurement were used for lipid extraction (four replicates per age and diet). Tissue pieces were homogenized before lipids were extracted using the Folch method. The lower phase was recovered and resuspended in 150 μl chloroform, while the upper aqueous phase was isolated, dried and resuspended in 100 μl in chloroform/methanol/water (2:5:1, by vol.). Isolated lipids were analyzed by LC/MS/MS using an Orbitrap Elite mass spectrometer (Thermo Fisher Scientific, USA) with both positive and negative electrospray ionization.

The naturally occurring lipids originating from the WAT samples were identified by reference to 80 μl of a standard mixture of synthetic lipids containing C17-acyl groups rather than the even numbers of carbon atoms. This sample of lipid standard contained 17:0-cholesterol ester (CE), 17:1/17:1/17:1-triacylglycerol (TG), 17:1/17:1/17:1-1-alkyltriacylglcerol (aTG), 17:0/18:1-diacylglycerol (DG), 17:0/18:1-alkyldiacylglycerol (aDG), 17:0-monoacylglycerol (MG), 17:0-free fatty acid (FA), 17:0-fatty-acyl coenzyme A (FaCoA), 13:0-fatty-acyl coenzyme A (short-chain FaCoA), 17:0-fatty-acyl carnitine (FaCN), 17:0/18:1-phosphatidic acid (PA), 17:0/18:1-phosphatidylcholine (PC), 17:0/18:1-alkylphosphatidylcholine (aPC), 17:0/18:1-phosphatidylethanolamine (PE), 17:0/18:1-alkylphosphatidylethanolamine (aPE), 17:0/18:1-phosphatidylglycerol (PG), 17:0/20:4-phosphatidylinositol (PI), 17:0/18:1-phosphatidylserine (PS), 14:0/14:0/14:0/14:0-cardiolipin (CL), C17-platelet-activating factor (PAF; 50 ng), C17-2-lysoplatelet-activating factor, 17:0-2-lysophosphatidic acid (LPA), 17:0-2-lysophosphatidylcholine (LPC), 17:0-2-alkyllysophosphatidylcholine (aLPC), 17:1-2-lysophosphatidylethanolamine (LPE), 17:1-2-alkyllysophosphatidylethanolamine (aLPE), 17:12-lysophosphatidylglycerol (LPG), 17:1-2-lysophosphatidylinositol (LPI), 17:1-2-lysophosphatidylserine (LPS), C17-ceramide (Cer), C17-sphingosine-1-phosphate (S1P), C17-sphingomyelin (SM).

Lipid species were normalized to synthetic standards to quantify their absolute abundance. Lipid species of the same class were summarized to quantify abundance levels of the entire lipid class. Abundance differences in individual lipid species or lipid classes were tested by one-way ANOVA followed by a post-hoc Tukey test for pairwise comparisons across all treatment and age groups. For elongation and saturation analysis, TG species with the same number of carbons/double bonds were calculated as percentage of the entire TG lipidome. We conducted one-way ANOVA with post-hoc Tukey HSD for each chain length/saturation to test for significant differences.

White adipose tissue explant culture and lipid transfection

WAT explant cultures were generated from 24 months old female AL and DR mice of an independent cohort. Mice were sacrificed using cervical dislocation and epididymal WAT pads were dissected and placed in explant culture growth medium (Dulbecco’s MEM/Ham’s F12 cell culture medium (FG4815, Merck) containing 1% Penicillin-Streptomycin (5,000 U/mL) (#15070063, ThermoFisher Scientific), 10% Fetal Bovine Serum (#10270106, Gibco), 17 μM D-Pantothenic acid (Sigma, P5155) and 33 μM Biotin (B4639, Sigma). Fat pads were washed once in sterile DPBS (14190-094, ThermoFisher Scientific) and cut into small tissue pieces using a razor blade. Tissue pieces were transferred to a 70 μm MACS SmartStrainer (130-098-462, Miltenyi) and washed with 35 ml DBPS to remove free-floating fat. Excess liquid was removed and tissue explants were transferred to 6-well plates (657160, Greiner Bio-One). Explant cultures were allowed to adhere at RT for 10 min, supplied with pre-warmed 2 ml growth medium and incubated at 37°C 5% CO2 for 24 hrs prior to lipid transfection. Lipid transfections with 18:1-12:0 NBD-PG (1-oleoyl-2-12-[(7-nitro-2-1,3-benzoxadiazol-4-yl)amino]dodecanoyl)-sn-glycero-3-[phospho-rac-(1-glycerol)] (ammonium salt) (#810166C, Avanti Polar Lipids Inc.) were conducted according to manufacturer’s recommendations. Per well, 30 nM NBD-PG in chloroform was evaporated and dissolved in 195μl Opti-MEM™ I Reduced Serum Medium (31985070, Gibco) and sonicated for 15 min in a chilled sonicator water bath to improve solubility. Lipofectamine 3000 (L3000-001, ThermoFisher Scientific) mixtures with 3.75μl Lipofectamine per well were prepared according to manufacturer’s recommendations in Opti-MEM medium. Lipofectamine and NBD-PG were mixed in a 1:1 ratio and incubated for 15 min at RT in the dark. Lipofectamine control or NBD-PG were added drop-wise to the respective well, mixed. Explant cultures were incubated for 48h before samples were snap frozen and stored at -20°C.

Thin layer chromatography of lipids

Extraction of lipids from adipose tissue was performed according to ⁷⁶ with modifications. WAT explant cultures were homogenized in 500 μl of 255 mM ammonium carbonate by Precellys 24 beads beater (2 x 20 sec 6500 rpm with ceramic beads). 120 μl homogenate was mixed with 900 μl of chloroform/methanol (1:2 (v/v)). After mixing for 30 min, H₂O (0.12 ml) was added followed by vortexing. After the addition of 0.3 ml chloroform and 0.3 ml H₂O, the sample was mixed again for 10 min, and phase separation was induced by centrifugation (800g, 2 min). The lower chloroform phase was carefully transferred to a clean glass vial. The upper water phase was mixed with 10 μl 1N HCl and 300 μl chloroform and extraction was repeated. After phase separation, the lower chloroform phase was carefully transferred to the glass vial with the chloroform phase from the first extraction. The solvent was evaporated by a gentle stream of argon at 37°C. Lipids were dissolved in 100 μl of chloroform/methanol (1:1 (v/v)). 4 μl of the lipid samples were spotted on a HPTLC plate (Merck Silica gel 60 F₂₅₄) and developed with chloroform/methanol/water/triethylamine 30:35:7:35 (v/v/v/v). Analysis of fluorescent signals was performed using the Typhoon Trio laser scanner (λem=526nm, λex= 488 nm) and the ImageQuant software (GE Healthcare). TLC plates were stained with 470 mM CuSO4 in 8.5% o-phosphoric acid and subsequently incubated for 10 min at 180°C.

Thin layer chromatography fluorescent signal analysis

Distribution of fluorescent signal was analysed with Fiji ⁷³. First, the distribution and width of each fluorescent band was quantified by vertical paths run through the centre of each lane using ‘integrated density’ as measurement. Since there was no fluorescent signal from cells incubated without NBD-PG, it can be assumed that all the fluorescence in the NBD-transfected cells was originally PG. Thus, the sample-wise relative distribution of fluorescent signal (i.e. relative conversion rate of PG into other lipid species) can be obtained by normalising against the total sum of the integrated signal, thereby removing potential differences in cell number or PG uptake. Data from technical replicates were averaged after quantification. Fluorescent and non-fluorescent lipid standards were run in parallel to identify individual lipid classes and to estimate the influence of the fluorescent label on the retention behaviour of the TLC. TG band was identified as the top most running band after CuSO₄ staining and scanning.

Next, the fluorescent signal of each band was measured using the ‘regions of interest’ option in Fiji. For each band (TG, CL, PG, etc.) the selected area was of equal size across all samples. Measured values were normalised against the sample-wise, total fluorescent signal quantified by a bin spanning all bands together. Data from technical replicates were averaged after quantification.

We thank Richard Weindruch, James Nelson, Rich Miller, Colin Selman, Dominic Withers and Friedemann Kiefer for their advice on the mouse dietary restriction protocol and Dietmar Vestweber and the Max-Planck-Institute for Molecular Biomedicine, Münster, for kindly allowing us to conduct our studies at their facilities. We further thank Ina Gravemeier, Ulrich Hill, Jennifer Matutat, Andrea Mesaros and Brit Neuhaus for assistance with mouse work. We thank Tony Wyss-Coray and the Tabula Muris consortium for kindly granting access to their single-cell transcriptome atlas, and for advice and supervision during the corresponding analysis.

We acknowledge funding from the Max Planck Society, Bundesministerium für Bildung und Forschung Grant SyBACol 0315893A-B (to AB and LP) and the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement number 268739 to LP. MJOW, AN and QZ thank the BBSRC (BB/P013384/1) and the MRC (MR/M004821/1) for financial support.