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Does brown fat increase with age?

Brown adipose tissue function declines during aging and may contribute to the onset of metabolic disorders such as diabetes and obesity. Only limited understanding of the mechanisms leading to the metabolic impairment of brown adipocytes during aging exists.

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Identification of potential metabolic biomarkers of BAT-aging

To identify novel biomarkers and mechanisms of defective BAT function during aging, a comparative metabolomic analysis by mass spectrometry was used. Data analysis of male mice of the C57BL/6-J strain resulted in a total of 368 identified metabolites in BAT samples of five different age groups, ranging from 2.5 to 25 months of age (Fig. 1a). A broad variety of metabolites was detected, including 227 non-polar and 141 polar metabolites which contained intermediates belonging to different metabolic pathways. When analyzing changes of metabolites in BAT from each age group, we identified a total of 48 lipids and 20 polar metabolites with significant age-dependent correlations (Fig. 1a). The subset of non-polar, lipid metabolites were further analyzed in a previous study which identified several lipid species as functional biomarkers of BAT-aging, including a sphingolipid, Sphingosine-1-phopshate, and an isoprenoid-derived lipid class, the dolichols, which exerted regulatory effects on brown adipocyte formation and function14. Polar metabolites showing age-dependent correlations in BAT were further investigated here, revealing negative or positive correlations for 11 and 9 metabolites, respectively (Fig. 1b). Despite this limited number, pathway analysis of these 20 age-regulated candidates was performed using the software MetaboAnalyst to broadly annotate the dataset for metabolic processes involving these metabolites. This allowed us to assign the metabolites to three pathway clusters, i.e. being related to either nutrient/energy metabolism, or nucleotide metabolism, or vitamin metabolism (Fig. 1c,d). The pathways driving the cluster of nutrient and energy metabolism were annotated as pyruvate metabolism, glycolysis/gluconeogenesis, and the tricarboxylic acid cycle (TCA). Altogether, this observation confirms that BAT, like many other tissues, experiences mitochondrial dysfunction and defective energy metabolism during aging. The cluster of nucleotide metabolism was mainly represented by enrichment of metabolites within the purine metabolism pathway with concomitant identification of pyrimidine metabolism. An additional third cluster was mostly represented by metabolic processes related to vitamins, in particular to those belonging to the group of B-vitamins (riboflavin, thiamine, pyridoxine and nicotinamide). Figure 1 Changes of polar metabolites during BAT-aging. (a) Venn diagram representing metabolites from BAT samples that were collected from animals aged 2.5, 5, 10, 15, 21, and 25 months (n = 3/ age-group). All metabolites were detected by LC–MS and GC–MS metabolomic analysis. 368 compounds were detected (light grey) and grouped into non-polar lipid species (227 candidates; light blue) and polar metabolites (141 candidates; light red). For each group, clusters of metabolites with significant correlation to aging across the age-groups were identified, corresponding to 48 non-polar lipid species (dark blue) and 20 polar metabolites (dark red). (b) Heatmap representing concentrations of the 20 individual polar metabolites that were significantly correlated to BAT-aging in the individual animals from each age group. (c) Pathway enrichment analysis for the 20 polar metabolites with significant age-related correlation using MetaboAnalyst software. Grey line indicates formal threshold of significant enrichment. (d) Summary of all pathways identified from the enriched polar metabolites, including pathways with significant enrichment (bold; p < 0.05), after grouping into three functionally related sub-clusters, i.e. (1) nutrient/ energy metabolism, (2) nucleotide metabolism, and (3) vitamin metabolism. Full size image To correlate systemic metabolomic changes in relation to aging in BAT, a metabolomic assessment of plasma samples collected from young and aged mice was performed. Among the detected compounds, 34 metabolites were age-depleted and 10 metabolites were significantly up-regulated in aged mice (Fig. 2a). To annotate each metabolite to metabolic processes, a pathway analysis similar to the analysis of BAT was performed, revealing only limited overlap with metabolic clusters identified in aging BAT (Fig. 2b, Supplementary Table S1). While individual metabolites were not overlapping between analyses of BAT and plasma, pathways particularly linked to nucleotide, i.e. purine and pyrimidine, metabolism and also energy metabolism and B-vitamin metabolism were identified. In summary, age-dependent regulation of pathways involved in energy, nucleotide and B-vitamin metabolism in BAT were reflected partially by age-related alterations in circulating metabolite levels. Figure 2 Age-related changes in plasma metabolites reflect effects on purine metabolism in aged BAT. (a) Heatmap depicting plasma metabolite concentrations comparing plasma samples from young (2 months) to aged (15 months) mice that are significantly (p < 0.05) different between both age groups (n = 5 / group). All metabolites were detected by LC–MS and GC–MS metabolomic analysis. (b) Pathway identification analysis for polar and non-polar plasma metabolites with age-related changes using MetaboAnalyst software. Metabolites were selected based on significant (p < 0.05) differences between age groups. Tabular summary of all pathways identified from plasma metabolites, including pathways with significant enrichment (bold; p < 0.05), which were subsequently grouped into functionally related sub-clusters, i.e. (1) nutrient/ energy metabolism, (2) nucleotide metabolism, (3) vitamin metabolism, (4) lipid metabolism and (5) xenobiotic metabolism. Full size image

Histamine indicates negative effects of BAT resident mast cells on energy metabolism

To specifically investigate the age-dependent effects on identified metabolites, time-dependent correlation analyses for single metabolites were performed, revealing that energy metabolism intermediates related to pyruvate metabolism, including acetylphosphate and phosphoenolpyruvic acid, were negatively correlated with BAT-aging, as well as amino acid-metabolites 5-hydroxy lysine and glutathione. Other polar intermediates, including a glucose-derived metabolite, D-glucuronic acid, showed a positive correlation with aging in BAT (Supplementary Fig. S1, Supplementary Table S2). While nutrient/energy metabolism intermediates could reflect metabolic or mitochondrial dysfunction, which is well-documented in many tissues during aging, the positive correlation of histamine with BAT-aging caught our attention. This metabolite was significantly enriched in aged BAT and the amino acid histidine, the precursor of histamine synthesis, was also significantly increased in aged plasma samples. We therefore decided to more closely investigate histamine as a candidate for a potential functional biomarker of aging in BAT. Mast cells, the main source of histamine, have previously been linked to inhibition of thermogenesis in brown adipocytes, although aside from limited data, published studies predominantly address browning of WAT rather than the effect of Mast cells on classical depots of BAT24,25. We therefore hypothesized that enrichment of mast cells in aged BAT may contribute to its age-related dysfunction. Indeed, a histological assessment revealed that mast cells were enriched in BAT of aged mice (Fig. 3a and b). Moreover, expression of mast cell marker genes carboxypeptidase A3, Cpa3, and mast cell protease 4, Mcpt4, was measured in BAT and inguinal white adipose tissue (iWAT) of young, 2.5 months-, and aged, 25 months-old mice before and after a 7-day cold exposure. These analyses showed that aging alone tended to increase expression of mast cell markers in iWAT but not BAT, while this effect of increased expression of both genes was significant between both tissues after cold exposure (Fig. 3c). To functionally link mast cell activity to brown and white adipocyte differentiation, we next performed co-culture assays where primary pre-adipocytes isolated from brown and white adipose tissue by flow cytometry were differentiated in the presence of increasing numbers cells of the mast cell line p815 ranging from 0.05 – 0.5% of mast cells as proportion of total cells in each culture well26. While expression of mast cell marker Cpa3 correlated with the increasing percentages of mast cells in the co-culture, expression of Pparg was reduced in brown adipogenic co-cultures at 0.1 and 0.5% and white adipogenic co-cultures at the highest percentage of p815 mast cells. A strong inverse correlation between the percentage of mast cells in the culture and expression of brown adipogenic marker Ucp1 was found with a virtually absent expression in co-cultures with the highest mast cell proportion (Fig. 3d). To rule out dilution effects due to proliferation of mast cells, we also cultivated differentiated brown adipocytes in cell culture media previously conditioned by p815 mast cells. Recapitulating the findings from the co-cultures, treatment with such media resulted in a rapid reduction of expression of brown adipogenic markers and induction of serpin genes, Serpina3n and Serpinb6, which are inhibitors of mast cell proteases27, suggesting that a paracrine crosstalk between the two cell types, through factors released from mast cells, may affect brown adipocyte function (Fig. 3e). In summary, these observations suggest that the age-related increase in histamine may act as a biomarker of increased mast cell accumulation in BAT, which in turn could exert inhibitory effects on brown adipocyte function. Figure 3 Aging results in mast cell accumulation which impairs brown adipogenesis. (a) Toluidine blue staining of BAT-sections of young (2.5 months, left panel; n = 7) and aged mice (25 months; middle panel, n = 7) and subsequent cell count-based quantification (right panel) of stained mast cells (red arrows) normalized to total section area using sections of BAT. For each animal, 1–2 independent tissue sections per animal were assessed. Images were collected at 600-fold magnification (scale bar: 10 µm). (b) Immunofluorescence analysis of cells expressing mast cell marker carboxypeptidase A3 (CPA3) in BAT-sections of young (2.5 months, left panel; n = 8) and aged mice (25 months; middle panel, n = 8) and subsequent cell count-based quantification (right panel) of stained mast cells (green signal, white arrows) normalized to total section area using sections of BAT. For each animal, 1–2 independent tissue sections per animal were assessed. Images were collected at 600-fold magnification (scale bar: 10 µm). (c) mRNA levels of mast cell markers Cpa3 and Mcpt4 in BAT (left) and iWAT (right) of young mice maintained at room temperature (RT; white bars) or of young mice after 7 days of cold exposure (light blue) were compared to mice aged 25 months at RT (grey bars) or 25-month old mice after 7-day cold exposure (dark blue). Data are depicted as mean ± standard deviation (SD; n = 4 for 2.5 months BAT and iWAT; n = 8 for all cold exposed tissues and aged BAT)-8 mice; n = 7 for aged iWAT). (d) mRNA expression of Cpa3, Pparg and Ucp1 of primary brown (left, orange) and white (right, grey) adipose tissue-derived progenitors co-seeded with 0.05, 0.1 or 0.5% of p815-mast cells compared to control cultures without masts cells (control; C) after 10 days of differentiation (n = 15, from 3 independent repeat-experiments for BAT; n = 9 from 2 independent repeat experiments for iWAT). (e) mRNA expression of brown adipocyte marker genes Ucp1, Cidea, Ppargc1a, Pparg, Cebpa and of mast cell protease inhibitors Serpina3n and Serpin6b in in vitro differentiated brown adipocytes exposed to control conditioned media (white) or mast cell-conditioned media (light grey) for 24 h (n = 12 for Ppargc1a from 2 independent experiments; n = 15 for all other genes, from 3 independent repeat-experiments). All data (see Suppl. Table S5 for full gene names) are depicted as mean ± standard deviation (SD); *p < 0.05, **p < 0.01, ***p < 0.001 using a nonparametric Kruskal–Wallis test for multiple comparisons of panels with multiple BAT- or iWAT-samples, respectively, and non-parametric Mann–Whitney test for panels with pairwise comparisons. Full size image

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Altered nucleotide metabolism as biomarker of brown adipose tissue-aging

The second cluster of aging-sensitive correlations in BAT, nucleotide metabolism, was driven by several purine- and pyrimidine-linked metabolites, i.e. guanosine monophosphate (GMP), ribose-5´-phosphate, adenosine diphosphate-ribose (ADP-ribose), uridine monophosphate (UMP) and UDP-N-acetyl glucosamine, which correlated inversely with age. Moreover, one purine nucleotide, deoxyguanosine, and one pyrimidine nucleoside, cytidine, were found to be positively correlated with BAT-aging (Fig. 4, Supplementary Table S3). Age-dependent regulation of additional purine intermediates was observed when directly comparing metabolite concentrations for each age group to the youngest, 2.5-month old reference group, showing guanosine, inosine, and the purinogenic amino acid L-glutamine to be significantly elevated in the old groups (Supplementary Fig. S2, Supplementary Table S3). In addition, four intermediates related to B-vitamin metabolism were detected using this analysis, thiamine pyrophosphate (TPP), which was negatively correlated to BAT-aging, and riboflavin, pyridoxamine and nicotinamide riboside, which showed an induction in the aged groups compared to 2.5-month old BAT (Supplementary Fig. S2, Supplementary Table S4). Figure 4 Polar metabolites involved in nucleotide metabolism correlate with BAT aging. Spearman correlations of individual polar metabolites linked to nucleotide metabolism that are significantly associated with aging (from left to right, top to bottom): Guanosine monophosphate (GMP), ribose-5´-phosphate, adenosine diphosphate-ribose (ADP-ribose), uridine monophosphate (UMP), uridine diphosphate N-acetylglucosamine (UDP-GlcNAc), deoxyguanosine, and cytidine. Correlation coefficients (r) for individual metabolites were calculated to visualize age-dependent correlations of metabolic species. Individual peak intensities of all age groups were normalized to intensities measured in the 2.5 months control group. All data are depicted as mean ± SD (n = 3 for all groups); * p < 0.05 indicating significant correlation across all age groups; a, b, c, d p < 0.05 comparing relative intensity of the indicated age group compared to (a) 2.5 months, (b) 5 months, (c) 10 months or (d) 15 months, respectively, using two-tailed unpaired t-test. Full size image To investigate whether the metabolic pathways identified through the metabolite analysis were also altered on the level of gene expression linked to these substances, we screened a proteomic dataset of aging BAT that we had previously generated by 18O-labeling mass spectrometry for candidates involved in enzymatic reactions of the three identified metabolic clusters14. Proteins were annotated depending on their pathway-association within the metabolic process and, for each protein, the relative heavy/light (H/L) ratio of the 18O-labeling analysis was calculated from the intensity-weighted average of all detected peptide ratios, with values of < 1.0 indicating age-dependent down-regulation and H/L ratios > 2.5 depicting age-enriched proteins14. This analysis revealed a prominent age-dependent down-regulation of most detected proteins within all three pathway clusters, where the highest number of age-depleted candidates was related to purine metabolism (Table 1). Of note, only two proteins were enriched in BAT of aged mice, the glycolysis enzymes hexokinase 1 (HK1) and phosphoglycerate mutase 2 (PGAM2). To verify these observations, we also tested mRNA expression of these genes linked to energy or nucleotide metabolism in BAT and, additionally, in iWAT, comparing 2.5- and 15-months old animals. However, a significant age-dependent up-regulation was only detected for Hk1, whereas Pgam2 was found to be reduced on the mRNA level, and the remaining energy metabolism genes were not differentially expressed (Fig. 5a). In genes linked to nucleotide metabolism, a significant down-regulation of mRNA expression of several enzymes involved in purine metabolism was detected (Fig. 5b), further corroborating the previous findings of age-dependent decrease of those candidates on the proteomic and metabolite level. We also investigated expression of B-vitamin metabolism-related genes, but no changes of transcript levels of these candidates were found in aged BAT (Supplementary Fig. S3). To further assess whether comparable alterations may also occur in aged white fat, a similar set of genes was measured in iWAT, but only sparse and less consistent regulations of genes throughout the different metabolic clusters were detected in this adipose tissue depot (Supplementary Fig. S4). In summary, aging resulted in consistent changes in the expression of enzymes related to nucleotide, and in particular, purine metabolism in aging BAT. Given the heterogenous composition of brown and white adipose tissue, we also evaluated expression of the same set of genes in isolated brown and white adipocytes in comparison to the respective SVF of BAT and iWAT. These analyses revealed that the majority of genes involved in either energy or nucleotide metabolism pathways were significantly enriched in mature brown and white adipocytes compared to the respective SVF. As the stromal vascular fraction will contain all blood cells, but also all non-adipocytes permanently resident within the adipose tissue, these findings suggest that the metabolite changes observed in aged BAT were indeed a consequence of altered metabolic processes in adipocytes rather than stemming from residual blood or SVF cells within the tissue (Fig. 6a, b). Expression of B-vitamin genes was not similarly enriched in brown adipocytes (Supplementary Fig. S5), and mRNA levels of the same set of genes were also less consistently enriched in mature white adipocytes compared to iWAT-derived SVF (Supplementary Fig. S6). To illustrate the concerted age-related regulation of metabolic flux in brown adipose tissue, we graphically integrated metabolomic, proteomic and transcriptional changes in a metabolic network representing the nucleotide metabolism cluster (Fig. 7) and the energy metabolism pathways (Supplementary Fig. S7). Taken together, these data suggest that aging results in changes of BAT metabolism on several levels which could serve to better define biomarkers depicting age-related dysfunction of energy metabolism and thermogenesis in brown adipocytes. Table 1 Age-dependent proteomic regulation of nucleotide metabolism enzymes. Table representing enzymes involved in (1) nutrient/ energy metabolism, (2) nucleotide metabolism and (3) B-vitamin metabolism identified by comparative 18O-labeled proteomic analysis of young and aged BAT (n = 5). Proteins were annotated based on their Uniprot accession number53. Identified proteins were clustered depending on their function in the metabolic pathway: (1) pyruvate metabolism, TCA cycle, glycolysis, amino acid metabolism; (2) purine and pyrimidine metabolism; (3) vitamin transport, vitamin B6 metabolism and nicotinamide metabolism. Proteins that were reduced in aged mice were identified based on heavy/light ratios (H/L ratio) < 1, while H/L ratios > 2.5 depicted age-enriched enzymes. Full size table

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Figure 5 mRNA expression of biosynthesis and degradation genes linked to metabolites identified in aged BAT. (a) mRNA levels of nutrient/ energy metabolism genes (see Suppl. Table S5 for full gene names) comparing BAT samples of young (2.5 months; n = 8; white bars) and aged (15 months; n = 7; grey bars) mice. (b) mRNA levels of nucleotide metabolism genes comparing BAT samples of young (2.5 months; n = 8; white bars) and old (15 months; n = 8; grey bars) mice. All genes are organized according to pathway enrichment in the proteome analysis of the same age groups (Table 1). Data are expressed as percentage of young (2.5 months) control. Data are shown as mean ± SD; *p < 0.05; **p < 0.01; ***p < 0.001 using non-parametric Mann–Whitney test. Full size image Figure 6 Expression of marker genes is enriched in mature adipocyte fraction. (a) mRNA levels of nutrient/ energy metabolism genes and of nucleotide metabolism genes (b) assessed in SVF (white bars) and mature adipocytes (grey bars) isolated from BAT of 2.5-months old mice. Genes (see Suppl. Table S5 for full gene names) are organized according to pathway enrichment in the proteome analysis of the same age groups (Table 1). For each gene, data are expressed as percentage of BAT-derived SVF. Data are shown as mean ± SD (n = 4 for all groups); *p < 0.05; assessed using non-parametric Mann–Whitney test. Full size image

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