Abstract
Catecholamine-induced lipolysis, the first step in the generation of energy substrates by the hydrolysis of triglycerides1, declines with age2,3. The defect in the mobilization of free fatty acids in the elderly is accompanied by increased visceral adiposity, lower exercise capacity, failure to maintain core body temperature during cold stress, and reduced ability to survive starvation. Although catecholamine signalling in adipocytes is normal in the elderly, how lipolysis is impaired in ageing remains unknown2,4. Here we show that adipose tissue macrophages regulate the age-related reduction in adipocyte lipolysis in mice by lowering the bioavailability of noradrenaline. Unexpectedly, unbiased whole-transcriptome analyses of adipose macrophages revealed that ageing upregulates genes that control catecholamine degradation in an NLRP3 inflammasome-dependent manner. Deletion of NLRP3 in ageing restored catecholamine-induced lipolysis by downregulating growth differentiation factor-3 (GDF3) and monoamine oxidase A (MAOA) that is known to degrade noradrenaline. Consistent with this, deletion of GDF3 in inflammasome-activated macrophages improved lipolysis by decreasing levels of MAOA and caspase-1. Furthermore, inhibition of MAOA reversed the age-related reduction in noradrenaline concentration in adipose tissue, and restored lipolysis with increased levels of the key lipolytic enzymes adipose triglyceride lipase (ATGL) and hormone sensitive lipase (HSL). Our study reveals that targeting neuro-immunometabolic signalling between the sympathetic nervous system and macrophages may offer new approaches to mitigate chronic inflammation-induced metabolic impairment and functional decline.
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References
Young, S. G. & Zechner, R. Biochemistry and pathophysiology of intravascular and intracellular lipolysis. Genes Dev. 27, 459–484 (2013)
Lönnqvist, F., Nyberg, B., Wahrenberg, H. & Arner, P. Catecholamine-induced lipolysis in adipose tissue of the elderly. J. Clin. Invest. 85, 1614–1621 (1990)
James, R. C., Burns, T. W. & Chase, G. R. Lipolysis of human adipose tissue cells: influence of donor factors. J. Lab. Clin. Med. 77, 254–266 (1971)
Gerber, J. G., Detmar-Hanna, D. & Zahniser, N. R. Lack of an effect of age on beta-adrenoceptor-mediated lipolysis in isolated human adipocytes. J. Gerontol. A Biol. Sci. Med. Sci. 54, B71–B77 (1999)
Feingold, K. R., Doerrler, W., Dinarello, C. A., Fiers, W. & Grunfeld, C. Stimulation of lipolysis in cultured fat cells by tumor necrosis factor, interleukin-1, and the interferons is blocked by inhibition of prostaglandin synthesis. Endocrinology 130, 10–16 (1992)
Mowers, J. et al. Inflammation produces catecholamine resistance in obesity via activation of PDE3B by the protein kinases IKKε and TBK1. eLife 2, e01119 (2013)
Bertrand, H. A., Anderson, W. R., Masoro, E. J. & Yu, B. P. Action of food restriction on age-related changes in adipocyte lipolysis. J. Gerontol. 42, 666–673 (1987)
Schweiger, M. et al. Adipose triglyceride lipase and hormone-sensitive lipase are the major enzymes in adipose tissue triacylglycerol catabolism. J. Biol. Chem. 281, 40236–40241 (2006)
Xu, X. et al. Obesity activates a program of lysosomal-dependent lipid metabolism in adipose tissue macrophages independently of classic activation. Cell Metab. 18, 816–830 (2013)
Hotamisligil, G. S. Inflammation and metabolic disorders. Nature 444, 860–867 (2006)
Ferrante, A. W., Jr. The immune cells in adipose tissue. Diabetes Obes. Metab. 15 (Suppl 3), 34–38 (2013)
Lumeng, C. N. & Saltiel, A. R. Inflammatory links between obesity and metabolic disease. J. Clin. Invest. 121, 2111–2117 (2011)
Shoelson, S. E., Lee, J. & Goldfine, A. B. Inflammation and insulin resistance. J. Clin. Invest. 116, 1793–1801 (2006)
Vega, A. et al. A new role for monoamine oxidases in the modulation of macrophage-inducible nitric oxide synthase gene expression. J. Leukoc. Biol. 75, 1093–1101 (2004)
Rogers, N. H., Landa, A., Park, S. & Smith, R. G. Aging leads to a programmed loss of brown adipocytes in murine subcutaneous white adipose tissue. Aging Cell 11, 1074–1083 (2012)
Lavin, Y. et al. Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment. Cell 159, 1312–1326 (2014)
Spadaro, O. et al. Growth Hormone Receptor Deficiency Protects against Age-Related NLRP3 Inflammasome Activation and Immune Senescence. Cell Reports 14, 1571–1580 (2016)
Youm, Y. H. et al. Canonical Nlrp3 inflammasome links systemic low-grade inflammation to functional decline in aging. Cell Metab. 18, 519–532 (2013)
Campisi, J. & d’Adda di Fagagna, F. Cellular senescence: when bad things happen to good cells. Nat. Rev. Mol. Cell Biol. 8, 729–740 (2007)
Amano, S. U. et al. Local proliferation of macrophages contributes to obesity-associated adipose tissue inflammation. Cell Metab. 19, 162–171 (2014)
Shen, J. J. et al. Deficiency of growth differentiation factor 3 protects against diet-induced obesity by selectively acting on white adipose. Mol. Endocrinol. 23, 113–123 (2009)
Andersson, O., Korach-Andre, M., Reissmann, E., Ibáñez, C. F. & Bertolino, P. Growth/differentiation factor 3 signals through ALK7 and regulates accumulation of adipose tissue and diet-induced obesity. Proc. Natl Acad. Sci. USA 105, 7252–7256 (2008)
Guo, T. et al. Adipocyte ALK7 links nutrient overload to catecholamine resistance in obesity. eLife 3, e03245 (2014)
Yogosawa, S., Mizutani, S., Ogawa, Y. & Izumi, T. Activin receptor-like kinase 7 suppresses lipolysis to accumulate fat in obesity through downregulation of peroxisome proliferator-activated receptor γ and C/EBPα. Diabetes 62, 115–123 (2013)
Shih, J. C., Chen, K. & Ridd, M. J. Monoamine oxidase: from genes to behavior. Annu. Rev. Neurosci. 22, 197–217 (1999)
Czimmerer, Z. et al. Identification of novel markers of alternative activation and potential endogenous PPARγ ligand production mechanisms in human IL-4 stimulated differentiating macrophages. Immunobiology 217, 1301–1314 (2012)
Balter, N. J. & Schwartz, S. L. Accumulation of norepinephrine by macrophages and relationships to known uptake processes. J. Pharmacol. Exp. Ther. 201, 636–643 (1977)
Youdim, M. B., Edmondson, D. & Tipton, K. F. The therapeutic potential of monoamine oxidase inhibitors. Nat. Rev. Neurosci. 7, 295–309 (2006)
Mariathasan, S. et al. Cryopyrin activates the inflammasome in response to toxins and ATP. Nature 440, 228–232 (2006)
Berry, R. & Rodeheffer, M. S. Characterization of the adipocyte cellular lineage in vivo. Nat. Cell Biol. 15, 302–308 (2013)
Rodeheffer, M. S., Birsoy, K. & Friedman, J. M. Identification of white adipocyte progenitor cells in vivo. Cell 135, 240–249 (2008)
Elsworth, J. D. et al. Symptomatic and asymptomatic 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-treated primates: biochemical changes in striatal regions. Neuroscience 33, 323–331 (1989)
Berry, R. et al. Imaging of adipose tissue. Methods Enzymol. 537, 47–73 (2014)
Nolan, T., Hands, R. E. & Bustin, S. A. Quantification of mRNA using real-time RT-PCR. Nat. Protocols 1, 1559–1582 (2006)
Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013)
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014)
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995)
The Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res. 43, D1049–D1056 (2015)
Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000)
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015)
Xue, J. et al. Transcriptome-based network analysis reveals a spectrum model of human macrophage activation. Immunity 40, 274–288 (2014)
Theocharidis, A., van Dongen, S., Enright, A. J. & Freeman, T. C. Network visualization and analysis of gene expression data using BioLayout Express(3D). Nat. Protocols 4, 1535–1550 (2009)
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003)
Chen, H., Zheng, X. & Zheng, Y. Age-associated loss of lamin-B leads to systemic inflammation and gut hyperplasia. Cell 159, 829–843 (2014)
Tacutu, R. et al. Human Ageing Genomic Resources: integrated databases and tools for the biology and genetics of ageing. Nucleic Acids Res. 41, D1027–D1033 (2013)
Acknowledgements
We thank R. Medzhitov and T. Horvath for presubmission review of the manuscript, and V. M. Dixit at Genentech Inc for providing the anti-caspase-1 antibody and the Nlrp3-deficient mice. We also thank S. Baindur for graphics, S. Sidorov, S. Valle Torres, P. Günther, K. Klee and T. Ulas for support in bioinformatics analyses, The Yale Center on Genomic Analysis (YCGA) for RNA-seq studies and the P. Cresswell laboratory for confocal microscopy support. J.L.S. was funded by the German Research Foundation (SFB704, SFB645) and by the ImmunoSensation Cluster of Excellence Bonn. C.C. was supported by NIA postdoctoral training fellowship under AG043608. E.L.G was supported by AFAR (American Federation of Aging Research). The Dixit laboratory is supported in part by NIH grants P01AG051459, AI105097, AG051459, AR070811, the Glenn Foundation on Aging Research and Cure Alzheimer’s Fund.
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C.D.C. carried out most experiments. O.S., E.L.G., K.Y.N. and Y.-H.Y. helped with experiments. A.W. and M.S.R. generated adipocytes from sorted progenitors and helped with co-culture experiments. C.W.B. generated the Gdf3 mouse model. A.L. and O.S. performed whole-mount confocal microscopy. J.E. measured noradrenaline using HPLC. J.S. and J.L.S. performed the bioinformatics analysis and interpretation. C.D.C. and V.D.D. conceived the project, analysed data and wrote the manuscript with input from all co-authors.
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Extended data figures and tables
Extended Data Figure 1 Age-related adipose tissue defects in response to fasting.
Data are related to Fig. 1. a, Body weight after 24 h feeding or fasting. n = 13 4-month fed; n = 10 21-month fed; n = 16 21-month fasted; n = 11 21-month fasted pooled from 2 independent experiments. b, Percentage body weight change after 24 h fasting. n = 13 4-month fed; n = 10 21-month fed; n = 16 21-month fasted; n = 11 21-month fasted pooled from 2 independent experiments. c, Blood glucose. n = 6 4-month fed; n = 6 21-month fed; n = 8 21-month fasted; n = 7 21-month fasted. d, Percentage change in blood glucose after feeding or fasting. n = 6 4-month fed; n = 6 21-month fed; n = 8 21-month fasted; n = 7 21-month fasted. e, VAT weight, displayed as a percentage of fed. n = 13 4-month fed; n = 10 21-month fed; n = 16 21-month fast; n = 11 21-month fasted pooled from 2 independent experiments. f, Serum FFA levels in fed and fasted mice. Data are pooled from 4 independent experiments. n = 23 4-month fed; n = 13 21-month fed; n = 26 4-month fasted; n = 13 21-month fasted. Each symbol represents an individual mouse. g, Body-weight, VAT weight, serum FFA levels, glycerol and FFA release from VAT explants in 4-month-old fed, 12-h fasted or 24-h fasted animals. Data are pooled from 2 independent experiments. n = 10 fed; n = 10 12-h fasted; n = 5 24-h fasted. h, Hsl and Atgl gene expression in floating adipocytes isolated from VAT of 4- (black) or 21- (red) month-old mice that were fed or fasted for 24 h. n = 5 4-month fed; n = 6 21-month fed; n = 6 4-month fasted; n = 9 21-month fasted pooled from 2 independent experiments. i, Western blot of lipolytic signalling pathway (pHSL, HSL, ATGL) in VAT from 4- or 21-month-old mice fed or fasted for 12 h. Each lane is an individual animal that received indicated treatment. Actin was probed for as a loading control. Representative of one experiment. j, Western blot of lipolytic signalling pathway (pHSL, HSL, ATGL) in VAT explants from 4- or 21-month-old fed mice that were left unstimulated or stimulated with 1 μM noradrenaline. Each lane represents a biological replicate. Representative of two individual experiments. Actin was probed for as a loading control. In all graphs (a–h), each symbol represents an individual mouse and data are mean ± s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, Tukey’s test (a–h).
Extended Data Figure 2 Age-related changes in ATMs in response to fasting.
Data are related to Fig. 1. a, Representative gating strategy for ATM analyses. b, Representative contour plots of CD206 and CD11c expression within F4/80+CD11b+ ATMs from VAT of fed or fasted mice. Values denote mean for each condition from 2 individual experiments. Exact n values are shown in the Methods. c, Quantification of CD11c+CD206− (top) and CD11c+CD206+ (bottom) populations from b, expressed as a percentage of total F4/80+ CD11b+ cells. Data are mean ± s.e.m. and pooled from two independent experiments. Each symbol represents an independent biological sample pooled from 1–4 mice; exact n values are shown in parentheses and in the Methods. ***P < 0.001, ****P < 0.0001, Tukey’s test, d, FFA release from 3- or 21-month-old fasted VAT explants that were co-cultured with sorted ATMs from 3-month-old adipose. Data are mean ± s.e.m. Exact n values (biological replicates) are shown in parentheses and are combined from three individual experiments. *P < 0.05, Student’s t-test. e, Western blot of MAOA in VAT from 4- or 21-month-old mice that were fasted for 24 h. Each lane represents an individual animal. A short (5 min; top) and long (30 min; middle) exposure are shown for clarity. Actin was probed for as a loading control. Representative of one independent experiment.
Extended Data Figure 3 ATMs are unique tissue-resident macrophage cell type.
a, Workflow, related to Fig. 2. b, PCA based on 13,954 present genes revealing the distribution of samples according to tissue origin and age. c, Heat map of differentially expressed genes between 24- and 3-month-old macrophages in VAT or spleen using a fold change < −1.5 or > 1.5 and an FDR-adjusted P < 0.05. Expression values were z-transformed and scaled to a minimum of −2 and a maximum of 2. Rows and columns were ordered based on hierarchical clustering. d, Heat map of the 1,000 most variable genes. Expression values were z-transformed and scaled to a minimum of −2 and a maximum of 2. Rows and columns were ordered based on hierarchical clustering. e, PCA based on 13,846 present genes of a combined dataset containing the four mouse macrophage populations defined by origin and age and a compendium of mouse tissue-resident macrophages of seven different organs (GEO accession GSE63340).
Extended Data Figure 4 Aged ATMs express distinct transcriptome.
a, Workflow, related to Fig. 2. b, Co-expression networks based on 1,887 variable genes having a correlation of at least 0.98 to at least one other gene. For each condition, the fold change compared to the overall mean was mapped onto the networks, ranging from blue (negative fold change) to red (positive fold change). Circles indicate the subclusters, which were identified for each condition. c, Heat map of lipid metabolism-related genes in VAT and splenic macrophages. Expression values were z-transformed and scaled to a minimum of −1.41 and a maximum of 1.41. Genes were ordered by hierarchical clustering.
Extended Data Figure 5 Myeloid cell changes in aged Nlrp3−/− mice.
Data relate to Fig. 3. a, Representative contour plots showing CD206 and CD11c expression, gated through F4/80+CD11b+ cells, from VAT of fasted wild-type and Nlrp3−/− mice. Values represent mean percentage in that quadrant. n values are shown in the Methods. b, Quantification of CD11c+CD206− (top) and CD11c+CD206+ (bottom) cells gated through F4/80+CD11b+ cells from VAT of fasted wild-type (filled) and Nlrp3−/− (open) mice. Data are mean ± s.e.m. c, B220−MHCII+CD11c+ frequency as percentages of the stromal vascular fraction of wild-type (filled) and Nlrp3−/− (open) mice at 3 or 24 months of age. Data are mean ± s.e.m. n values are shown in parentheses and in the Methods. d, Percentages of F4/80+CD11b+ cells in the spleen of wild-type and Nlrp3−/− mice at 5 or 24 months of age, which were fed (filled) or fasted (fasted) for 24 h. Data are mean ± s.e.m. n values are shown in parentheses and represent individual mice. e, Quantification of CD206 and CD11c expression, based on percentages of F4/80+CD11b+ cells, from spleen of fasted wild-type (filled) or Nlrp3−/− (open) mice. Data are mean ± s.e.m. Exact n values are shown in parentheses the figure. *P < 0.05, **P < 0.01, ****P < 0.0001, Tukey’s test.
Extended Data Figure 6 Nlrp3 regulation of age-induced genes in ATMs.
Data relate to Fig. 3. a, Workflow for RNA-seq data analysis. b, Left, PCA based on 13,129 present genes revealing the distribution of VAT macrophage populations from three different age/genotype groups. Right, heat map of the 1,000 most variable genes in VAT macrophage populations compared between all three groups. Expression values were z-transformed and scaled to a minimum of −2 and a maximum of 2. Rows and columns were ordered based on hierarchical clustering. c, Left, PCA based on 13,169 present genes reveals the distribution of splenic macrophage populations from three different age/genotype groups. Right, heat map of the 1,000 most variable genes in splenic macrophage populations compared between all three groups. Expression values were z-transformed and scaled to a minimum of −2 and a maximum of 2. Rows and columns were ordered based on hierarchical clustering. d, e, Venn diagrams comparing genes being up- (fold change > 2, left) or downregulated (fold change < −2, right) in 24- compared to 3-month wild-type macrophages with genes being down- (fold change < −2, left) or upregulated (fold change > 2, right) in 24-month Nlrp3−/− compared to 24-month wild-type macrophages in VAT (d) or spleen (e). f, Heat maps showing the expression patterns of the top 20 upregulated (left) or downregulated (right) genes (from e) to be rescued by Nlrp3-deficiency in splenic macrophages. Expression values were z-transformed and scaled to a minimum of −1.15 and a maximum of 1.15. Genes were ranked according to expression in 24-month wild-type splenic macrophages.
Extended Data Figure 7 NLRP3 regulation of senescence and lipid-associated genes in ATMs and splenic macrophages.
a, Workflow, related to Fig. 3. b, Bar chart displaying the fold change between 24- and 3-month wild-type VAT macrophages (pink) and 24-month Nlrp3−/− and wild-type VAT macrophages (turquoise) for senescence-associated genes. Horizontal dashed lines indicate fold changes of 1.5 and −1.5, respectively. c, Heat map of lipid metabolism-related genes in VAT macrophages. Expression values were z-transformed and scaled to a minimum of −1.15 and a maximum of 1.15. Genes were ranked according to hierarchical clustering. d, Heat map of present genes of the spleen macrophage dataset linked to Maoa, Comt, Aldh or Akr families in splenic macrophages. Expression values were z-transformed and scaled to a minimum of −1.15 and a maximum of 1.15. Rows are in the same order as in Fig. 3c, and genes that are not present in the spleen dataset are left blank.
Extended Data Figure 8 Mechanism for GDF3 inhibition of NLRP3 inflammasome activation.
Data relate to Fig. 4. a, Left, Pcsk6 mean expression, from RNA-seq analysis, from sorted ATMs in 3-month wild-type, and 24-month wild-type or Nlrp3−/− VAT. Data are mean ± s.e.m. n values are in the Methods. Right, schematic depicting the interaction between macrophage–PCSK6 and GDF3 and action on adipocyte lipolysis. b, Densitometric quantification of pro-caspase-1 (left) and active p20 caspase-1 (right) immunoblots depicted in Fig. 4b, normalized to actin, in wild-type (filled) or Gdf3−/− (open) BMDMs treated with LPS or LPS plus ATP. Data are mean ± s.e.m. and show n = 3 (WT) and n = 4 (Gdf3−/−) biological replicates. c, Casp1 gene expression in LPS-treated BMDMs generated from wild-type (black) or Gdf3−/− (grey) mice. Data are pooled from 2 independent experiments, each with n = 3 and n = 2 biological replicates, and expressed as mean ± s.e.m. d, Il1b gene expression in LPS-treated BMDMs generated from wild-type (black) or Gdf3−/− (grey) mice and displayed as fold change to untreated BMDMs. Data are pooled from 3 independent experiments, each with n = 3, n = 4 and n = 3 biological replicates, and expressed as mean ± s.e.m. e, Immunoblot showing phopho-p65, total p65, and IκB-α expression in LPS-treated wild-type or Gdf3−/− BMDMs. Representative of one independent experiment. f, Maoa gene expression in wild-type or Gdf3−/− BMDMs that have left untreated. Data are mean ± s.e.m. and pooled from 2 individual experiments. Each symbol represents a biological replicate. *P < 0.05, **P < 0.01, ***P < 0.0001, Tukey’s test (a, b) or Student’s t-test (c, d, f).
Extended Data Figure 9 Schematic to show nerve-associated macrophages and role in aged adipose.
Data relate to Fig. 4. a, Schematic of the mT/mG construct before (top) and after (bottom) Cre-mediated recombination in the LysM-cre+ myeloid cells. LysM-cre+ mT/mG+ traces the myeloid cells with membrane green fluorescence protein (mG, green), whereas LysM-cre− mT/mG+ cells that lack Cre recombination indelibly express tomato (mT, red). b, Single-colour images of merged image from Fig. 4e. Use of LysM-cre mT/mG reporter mouse plus antibody staining enabled visualization of all cells (mTomato, red), myeloid cells (mGFP, green), tyrosine hydroxylase (TH)-expressing (blue) and TUBB3-expressing (white) nerves. Representative of 6 independent experiments. c, Schematic representing the model. In young adipose, fasting initiates catecholamine release from sympathetic nerves. Binding of catecholamines on β-adrenergic receptors (β-ARs) causes intracellular cAMP increases, activation of PKA and downstream signalling lipases (ATGL, HSL and MGL), and results in the hydrolysis of triglyceride and release of fatty acid (FA) and glycerol. Lipolysis and FFA release are necessary fuel elements for survival during starvation, and promote cold and exercise tolerance. During ageing, there is an increase in inflammatory danger-associated-molecular patters (DAMPs), leading to the activation of the NLRP3 inflammasome in adipose tissue macrophages. NLRP3-dependent increases in GDF3 and MAOA result in degradation of noradrenaline, prevent noradrenaline activation of lipolytic signalling in adipocytes and cause reduced fatty acid and glycerol release. Inset, the pathway for MAOA-driven degradation of noradrenaline into dihydroxyphenylglycolaldehyde (DOPEGAL), and subsequent breakdown into dihydroxymandelic acid (DHMA) and dihydroxyphenyl glycol (DHPG) by aldehyde dehydrogenases (ALDs) and aldo-keto reductases (AKRs). DG, diglyceride; MG, monoglyceride; TG, triglyceride.
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Camell, C., Sander, J., Spadaro, O. et al. Inflammasome-driven catecholamine catabolism in macrophages blunts lipolysis during ageing. Nature 550, 119–123 (2017). https://doi.org/10.1038/nature24022
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DOI: https://doi.org/10.1038/nature24022
Majid Ali
Inflammasome-driven Catecholamine Catabolism, Oxygen-Insulin Signalling, and Ageing
Hyperinsulinism-to-Type 2 diabetes (T2D) continnum has eclipsed all other chronic disorders as the dominant threats to human health worldwide. Camell et al. (ref. 1) provide valuable information and insights concerning catecholamine catabolism, neuro-immunometabolic signalling between the sympathetic nervous system and macrophages, and lipolysis during ageing. They foresee that this work will bring forth new approaches to mitigating chronic inflammation-induced metabolic impairment and functional decline.
Human life span is put in jeopardy by disruptions of molecular biology of oxygen and insulin dysregulation; health and life expectancy of an individual is compromised by impeded signalling of the former (ref.2,3) and accentuated signalling of the latter (ref.4,5). This writer began his book Oxygen and Aging (2000) with the following words: Oxygen is the organizing principle of human biology and governs the aging process (ref. 2). He presented evidence for respiratory-to-fermentative shift in immune-inflammatory disorders (ref.3). From these and other findings concerning insulin homeostasis (ref. 6,7) he inferred the following: (1) insulin and oxygen signalling are inextricably intertwined and may not be seen as discrete entities; (2) oxygen and insulin are master regulators of corporeal ageing, influencing the rate and range of ageing in most, if not all, body organ systems; (3) disruptions of ?oxygen-insulin signalling? set the stage for persistent low-grade inflammation (6-8); and (4) impeded oxygen-insulin signalling and persistent low-grade inflammation make up the bioenergetics duo of chronic disease (ref. 9).
Camell and colleagues show that in mice: (1) macrophages in fat regulate the age-related reduction in lipolysis in adipocytes by lowering the bioavailability of noradrenaline; (2) ageing upregulates genes that control catecholamine degradation in Nod-like receptor (NLR) pathway; and (3) deletion of NLRP3 (a part of the NLR inflammasome) in ageing restored catecholamine-induced lipolysis by downregulating growth differentiation factor-3 and monoamine oxidase A that is known to degrade noradrenaline. These findings fill important gaps in the current knowledge of ageing-related inflammation, catecholamine catabolism, lipolysis, and other inflammasomal functionalities.
In Darwin, Oxygen Homeostasis, and Oxystatic Therapies, the 10th volume of The Principles and Practice of Integrative Medicine (ref.10), the writer presents the pathophysiology, clinical course, and integrative indigenous therapeutics of diverse clinic-pathologic entities, many of them with major disruptions of the sympathetic nervous system and neurometabolic dimensions (ref. 11). The NLRP3 inflammasome is of special interest in consideration of pathophysiology of ageing because it is activated in response to structurally diverse damage-associated molecular patterns (DAMPs) such as extracellular ATP, excess glucose, urate, cholesterol crystals, amyloids, and ceramides, all of which increase with age. Of note here is that control of NLRP3-mediated inflammation prevents age-related insulin resistance, bone loss, cognitive decline, and frailty.
References
1.	Carnell CD, Sander J, Spadaro O, et al. Inflammasome-driven catecholamine catabolism in macrophages blunts lipolysis during ageing. Nature.2017;550:119-123.
2.	Ali M. Oxygen and Aging. 1st. Edi. New ork, Canary 21 Press.2000
3.	Ali M. Respiratory-to-Fermentative (RTF) Shift in ATP Production in Chronic Energy Deficit States. Townsend Letter for Doctors and Patients. 2004;253:64-65.
4.	Ali M. Oxygen, Insulin Toxicity, Inflammation, And the Clinical Benefits of Chelation. Part I. Townsend Letter-The examiner of Alternative Medicine. 2009;315:105-109. October, 2009.
5.	Ali M. Epidemic of Dysoxygenosis and the Metabolic Syndrome. In: The Principles and Practice of Integrative Medicine. Volume 5. Pp 246-256. Canary 21 Press. New York. 2005.
6.	Ali M, Fayemi AO, Ali O. Dasoju S, et al. Shifting Focus From Glycemic Status to Insulin Homeostasis. Townsend Letter-The Examiner of Alternative Medicine. 2017;402:91-96.
7.	Ali M. Oxygen governs the inflammatory response and adjudicates the man-microbe conflicts. Townsend Letter for Doctors and Patients. 2005;262:98-103.
8.	Ali M. Insulin Homeostasis for Healthful Ageing. Townsend Letter-The examiner of Alternative Medicine. 2017; In Press.
9.	Goldberg EL, Dixit VD. Drivers of age-related inflammation and strategies for healthspan extension. Immunol Review. 2015?265:63-74.
10.	Ali M. Darwin, Oxygen Homeostasis, and Oxystatic Therapies. Volume X, 3 rd. Edi The Principles and Practice of Integrative Medicine (2009) New York. Institute of Integrative Medicine Press.
11.	 Mowers j, et al. Inflammation produces catecholamine resistance in obesity via activation of PDE3B by the protein kinases IKK? and TBK1. eLife. 2013;e01119.