Intermittent fasting attenuates CNS inflammaging - rebalancing the transposonome

Intermittent fasting attenuates CNS inflammaging - rebalancing the transposonome | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Intermittent fasting attenuates CNS inflammaging - rebalancing the transposonome Mitchell J Cummins, Ethan T Cresswell, Doug W Smith This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6165725/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted You are reading this latest preprint version Abstract A hallmark of CNS aging is sterile, chronic, low-grade neuroinflammation. Understanding how the aging CNS develops chronic inflammation is necessary to achieve extended healthspan. Characterisation of neuroinflammatory molecular triggers remains limited. Interventions that reduce neuroinflammation and extend health and lifespan could be useful in this regard. One such intervention is intermittent fasting (IF), but how IF impacts CNS inflammation is insufficiently understood. To address this, we performed deep RNA-sequencing on young, middle-aged, and old, mouse CNS regions. Additionally, we sequenced spinal cord in animals subject to adult lifelong IF. We found most differentially expressed genes (DEGs) at middle age were CNS region specific (~ 50–84%), whilst this effect weakened (~ 18–72%) in old age, suggesting emergence of a more general global aging profile. DEGs from all regions were enriched for inflammatory and immune ontologies. Surprisingly, SC was the most aging- and neuroinflammation-impacted region at both middle and old ages, with by far the highest number of DEGs, the largest net increase in expression of transposable elements (TEs), the greatest enrichment of immune-related ontologies, and generally larger increases in inflammatory gene expression. Overall, with normal aging we found upregulation of sensors of non-self, DNA/RNA, activation of specific inflammasomes, and upregulation of cGAS-STING1 and interferon response genes, across the CNS. Whilst IF animals still developed an inflammatory profile with aging in SC, average immune gene expression was lower by ~ 50% compared to age-matched controls. IF-specific DEGs were apparent, suggesting IF also acts on separate, potentially targetable, pathways to those impacted by normal aging. Expression of disease associated microglia, phagocytic exhaustion, sensors of non-self, DNA/RNA, STING1, and inflammasome genes were all decreased with IF. Significantly, the TE profile was reversed with a net expression decrease. In summary, we find SC is a CNS aging hotspot, and that IF attenuates neuroinflammaging potentially by rebalancing the transposonome. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Figure 19 Figure 20 Introduction Inflammaging – a term describing the chronic low-grade inflammation that occurs in aging tissues, is a hallmark of aging [ 1 ]. Despite its immune resistant nature, the CNS is not spared this inflammation, with the resident immune cells, microglia, as well as astrocytes, thought to be key drivers of neuroinflammaging. Microglia can adopt multiple states broadly captured by pro- and anti-inflammatory phenotypes, and aging results in a shift to the pro-inflammatory microglial state [ 2 – 8 ]. Many factors can drive microglia into this state including accumulation of lipid droplets, amyloid-β, and cytosolic DNA, all of which have been demonstrated in aging microglia [ 4 , 7 , 9 ]. Whilst some mechanisms driving neuroinflammaging are known, the extent to which this inflammation occurs in different CNS regions has not been adequately addressed, with the spinal cord (SC) being understudied. Furthermore, we do not know if the underlying mechanisms are similar for all CNS regions, given regions are differentially susceptible to some stimuli (e.g. amyloid pathology), and the kinetics of microglial aging appears to be region-dependent [ 10 , 11 ]. If CNS health-span is to be improved, we must understand the causes of neuroinflammaging across the CNS. Intermittent fasting (IF) is an effective anti-aging intervention, but its potential in countering neuroinflammaging is not well understood. IF can be implemented in mice by means of alternate day feeding (ADF) and this can be performed for the adult mouse lifespan [ 12 , 13 ]. It has been reported that ADF did not significantly affect the aging brain transcriptome, however the reported effect of normal aging was also modest (196 differentially expressed genes, DEGs, FDR < 0.1) [ 12 ]. Surprisingly however, ADF markedly reduced the incidence of neuronal inclusions in the thalamus [ 12 ], indicating beneficial CNS impacts in at least some cell types. Furthermore, a separate dietary intervention, a low-fat diet with calorie restriction, reduced white matter microglial activation in aging mice, suggesting dietary interventions can impact neuroinflammaging [ 14 ]. If we are to improve CNS health-span, functionality in all regions needs to be maintained. To expand our understanding of CNS aging in general, and the effects of IF in particular, we performed RNA-sequencing on young, middle-aged, and old C57BL/6 mice to profile inflammaging in four CNS regions, the cortex (CTX), hippocampus (HIP), cerebellum (CB), and SC. We also performed sequencing on SC from middle-age and old IF animals to determine how this intervention influences neuroinflammaging. We found the SC was the most age-impacted CNS region, based on number of DEGs and its overall neuroinflammatory profile. Both SC and CB exhibited a mild inflammatory profile at middle-age that worsened by old age. HIP was pro-inflammatory at old age, while the CTX was largely unaffected. The old-age pro-inflammatory profile was similar to that exhibited by disease-associated microglia (DAM) [ 15 , 16 ], and by microglia displaying phagocytic exhaustion [ 17 ]. Notably, this profile involved upregulation of genes involved in viral-like immune responses, suggesting the presence of non-self, DNA/RNA. This response may be induced by depression of transposable elements, as we found hundreds of TEs to have increased expression in aging CNS, however release of mitochondrial DNA is also a potential contributor [ 18 ]. The neuroinflammaging phenotype in SC was partially rescued by IF, which also resulted in more TEs with reduced expression than with increased expression, an adaptation that we are proposing is a rebalancing of the transposonome. Results Spinal cord is an aging hotspot Using deep RNA-sequencing we compared transcriptomes of CTX, HIP, CB, and SC, of young (3–4 mos), middle-aged (12–14 mos), and old mice (24 + mos). Taking a conservative approach to DEG identification, we found CNS regions were differentially impacted by aging, with all regions having a greater number of DEGs in old age compared to middle-age (Fig. 1 a-b). In middle-aged animals, SC was the most impacted region with 510 DEGs, followed by CTX (463), CB (211), with HIP by far the least impacted region investigated with only 74 DEGs. A somewhat surprising result given the known functional sensitivity of this region to aging [ 19 , 20 ]. Most middle-age DEGs were observed in a single CNS region, however ~ 20% of SC DEGs were found in at least one other region (Fig. 1 c,e). Only 8 genes were differentially expressed in all regions by middle age, those being 9630013A20Rik (a lncRNA thought to be involved in oligodendrocyte maturation [ 21 ]), Abca8a (involved in mature oligodendrocyte stimulation of sphingomyelin and regulation of lipid metabolism [ 22 , 23 ]), collagen genes Col1a1 and Col1a2, Gpr17 (involved in oligodendrocyte progenitor cells [ 24 ]), Pcdhb9 (largely uncharacterised in brain), Tnc (involved in brain development [ 25 ] and neuro-immune functions [ 26 ]), and Zc3hav1 (involved in non-self DNA/RNA detection and STING-inflammation [ 27 , 28 ]). Middle-aged DEGs were approximately equally affected between increases and decreases in expression in all regions. In old animals, HIP was again the least impacted region investigated, with only 325 DEGs. CTX was the second least impacted region (799 DEGs), followed by CB (922 DEGs). SC was by far the most age-affected region with nearly three times the CB, with 2618 DEGs. In all CNS regions, there was a shift to more DEGs with increased expression in old animals, which was especially apparent in CB and SC. Whilst a relatively high number of DEGs were still region specific, we found that a greater proportion were found in multiple CNS regions, with 49 DEGs common to all regions investigated (Fig. 1 d,f). Lists of DEGs for all regions are available in Supplementary Table S1 , Supplementary Material Online. For DEGs identified separately by the three analysis programs, see Supplementary File S1, Supplementary Material Online. While we found limited overlap between CNS regions for individual DEGs, we found greater consistency between regions for Gene Ontologies (GO) at both middle (Fig. 1 e) and old ages (Fig. 1 f). This increased GO overlap between CNS regions indicates aging impacts similar biological processes across the CNS, but through changes in expression of different genes between regions. We used Metascape [ 29 ] to determine and cluster regions by the top 100 GOs enriched in aging DEG sets (Fig. 2 ). Of the top 100 GOs at middle-age, 36 were related to immune system processes, such as immune cell activation, degranulation, antigen presentation, microglial phagocytosis, regulation of IL-1β, and pattern recognition receptor signalling, amongst others. Most of these ontologies were enriched in CB, SC, or both, suggesting age-related neuroinflammatory processes occur earlier in these regions. Other enriched ontologies were generally related to cell adhesion and the extracellular matrix (11), blood vessels (7), development (7), synaptic signalling (5), and lipids (4). Interestingly, response to axon injury was also enriched in CB and SC at middle-age. Similar to middle-age GOs, 37 of the top 100 old-age GOs were immune related, however, they were almost all enriched in HIP, CB, and SC (with higher significance), with some also enriched in CTX. Immune ontologies were similar to those at middle-age but also included more specific pathways such as tumour necrosis family (TNF) cytokine production, regulation of Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), response to type I and type II interferons, as well as endocytosis and phagocytosis. Other enriched ontologies were similar to those found at middle-age, including cell adhesion and the extracellular matrix (8), blood vessels (6), synaptic signalling (6), and lipids (2). Of particular note, the SC was enriched for 98 of the top 100 aging GOs, suggesting the aging SC is recapitulating molecular changes that occur across the CNS. Inflammaging is rampant in the spinal cord GO analysis highlighted the enrichment of inflammation-related ontologies in our aging DEG gene lists, particularly in SC. To investigate these inflammatory mechanisms in greater detail, we compared our DEGs to GO and experimentally derived gene lists of interest. Figure 3 shows a summary of these enrichment analyses. (see also Supplementary Table S2 , Supplementary Material Online for gene lists and Supplementary File S2, Supplementary Material Online for hypergeometric test results and overlapping genes). In the first instance, we compared our DEG lists to all genes in the positive regulation of inflammatory response GO (GO:0050729, 162 genes, Fig. 4 a) and negative regulation of inflammatory response GO (GO:0050728, 171 genes, Fig. 5 a). For positive regulation of inflammatory response, we found HIP, CB, and SC aging DEGs were enriched for this gene set (Fig. 3 a). Note, aging was associated with both positive (Fig. 3 a) and negative (Fig. 3 b) regulation of an inflammatory response in CTX, but the relatively modest numbers of DEGs meant significant enrichment was not reached. In HIP, enrichment was only observed in old age group DEGs (Fig. 3 a). Enriched genes included compliment protein C3 (Fig. 4 b), caspase Casp1, chemokine Ccl3, cathepsin Ctss, Fc receptors Fcer1g, Fcgr1 (Fig. 4 c), and Fcgr3, antigen presentation genes H2-D1 (Fig. 4 d), H2-K1 (Fig. 4 e), H2-Q4, H2-Q6, interleukin IL33, inflammasome component Pycard, a serine proteinase inhibitor Serpine1 (Fig. 4 f), toll-like receptors Tlr2 (Fig. 4 g) and Tlr4 (Fig. 4 h), and the microglial lipid receptor Trem2. Enrichment was observed in CB and SC by middle-age (Fig. 3 a). Middle-age genes were similar to HIP, old-age enriched DEGs with Ctss, Fcer1g, H2-D1, and Trem2 being found in both regions. Casp1, H2-K1, H2-Q4 were in CB only, whilst C3, Ccl3, Fcgr1, Fcgr3, and H2-Q6 were differentially expressed in SC only. In CB, Lgals1 and Snca were also differentially expressed whilst SC DEGs included Btk, Ldlr, Lpl, Plcg2, S100a8, and S100a9. Old-age CB and SC DEGs were also enriched for genes involved in the positive regulation of inflammatory response (Fig. 3 a). CB genes included many of those found in CB and SC middle-age DEGs, but included more histocompatibility locus genes, toll-like receptor Tlr6 (Fig. 4 i), inflammasome gene Gsdmd, genes involved in sensing non-self DNA/RNA (Ifi35 and Zbp1), and the TNF receptor Tnfrsf1a (Fig. 4 j). The SC had similar DEGs to the CB, but also had IL1b and other inflammasome-related genes, cytokine Ccl5 (Fig. 4 k), proton-sensing G protein-coupled receptor Gpr4 (Fig. 4 l), and TNF (Fig. 4 m), amongst others (see Supplementary File S2, Supplementary Material Online for all genes). Genes involved in the negative regulation of the inflammatory response (Fig. 5 a), were only enriched in HIP, CB, and SC at old age (Fig. 3 b and Supplementary File S2, Supplementary Material Online). HIP DEGs included the hyaluronan receptor Cd44, cysteine protease inhibitor Cst7, Fc receptor Fcgr2b, the uracil nucleotide/cysteinyl leukotriene receptor Gpr17, growth factor Hgf (Fig. 5 b), immune-related GTPases Igtp and Irgm1, interleukin IL33, serine/threonine kinase Pbk, tyrosine phosphatase Ptpn6 (Fig. 5 c), and Trem2. CB DEGs included most of the HIP genes, with the addition of lipoprotein Apoe, the cellular communication network factor Ccn3 (Fig. 5 d), chemokine receptor Cx3cr1 (Fig. 5 e) and the interleukin receptor subunit IL20rb. The SC (~ 2.5-fold enriched, 49 genes) contained almost all the genes enriched in HIP and CB at old-age, but also contained other anti-inflammatory genes like acyloxyacyl hydrolase (Aoah, Fig. 5 f), which limits inflammation in response to Gram negative bacteria, transmembrane glycoprotein Cd200r1 (Fig. 5 g), which limits inflammation after spinal cord injury, a hydrolase that metabolizes extracellular nucleotides, Enpp3 (Fig. 5 h), insulin-like growth factor Igf1 (Fig. 5 i), the immune-related GTPase Irgm2 (Fig. 5 j), Lrfn5 (Fig. 5 k), which supresses lipopolysaccharide induced CNS inflammation, Smad3 (Fig. 5 l), which inhibits inflammation-induced PPARβ expression and mediates anti-inflammatory macrophage transition, Sytll (Fig. 5 m), which inhibits cytokine release and phagocytosis in primary microglia, the TNF-α induced protein Tnfaip3 and the RNA-binding protein Zfp36, which downregulates pro-inflammatory cytokines like TNF-α. These inflammation-related GO enrichments again demonstrate the greater impact aging has on SC, with the number of overlapping DEGs in GOs in this region always larger than other regions and in some cases more than double the number. Note, the lower magnitude fold-enrichment in SC simply reflects the much greater number of total DEGs in SC at both middle and old ages, compared to other regions. One important inflammatory mechanism is the interferon response. Interferon-mediated signalling (GO:0140888) genes were enriched by middle-age in CB and at old-age in HIP, CB, and SC (Fig. 3 c and Supplementary File S2, Supplementary Material Online). Most interferon signalling DEGs increased in expression with age (Fig. 6 a). At middle-age, key components of interferon signalling including; Ifih1 (Fig. 6 b) which detects dsRNA, the interferon induced GTPases Igtp (Fig. 6 c) and Irgm1 (Fig. 6 d), the transcription factor Irf7 (Fig. 6 e), immune intracellular signalling molecule Nlrc5, Oas genes Oas1a (Fig. 6 f), which activates RNase L, and it's inhibitor Oas1b (Fig. 6 g), and Parp genes Parp9 and Parp14 that play opposing roles in macrophage activation, were all up-regulated, suggesting activation of interferon signalling and compensatory inhibition. By old age, more interferon signalling genes were differentially expressed. Those DEGs at middle-age in CB were also upregulated at old age, not only in CB but also in the HIP (excluding Oas1b and Parp9) and SC. The matrix metalloprotease Mmp12 (Fig. 6 h), and the Oas gene Oas2 (Fig. 6 i) were also upregulated in old HIP, CB and SC. Oas3 (Fig. 6 j), the dsDNA sensor, Zbp1, and the intracellular signalling molecule, Stat1, were all upregulated in old CB and SC, amongst others. Although interferon signalling genes were enriched in aging CNS, we did not find increased expression of interferon genes Ifna1, Ifnb1, or Ifng, but did find increased expression of the interferon receptor, Ifngr1 (Fig. 6 k), in old SC. Old SC also had decreased expression of anti-viral, Ifitm1 (Fig. 6 l), and increased expression of transcription factor Irf1, a general interferon response regulator (Fig. 6 m) [ 30 ]. We also determined enrichment for another group of inflammatory signalling pathways, the interleukin pathways (Fig. 3 d and Supplementary File S2, Supplementary Material Online). We compiled a non-redundant gene list (111 genes) from interleukin-mediated signalling GOs (GO:0038154, GO:0035722, GO:0035772, GO:0035723, GO:0038173, GO:0097400, GO:0035655, GO:0070498, GO:0038155, GO:0070106, GO:0038110, GO:0038172, GO:0061514, GO:0038156, GO:0035771, GO:0038043, GO:0070102, GO:0038112, GO:0038113). Interleukin signalling genes were enriched in middle-age CTX, and old CB and SC (Fig. 3 d). Interleukin signalling DEGs in middle-age CTX were mostly decreased in expression (Fig. 7 a). These included signalling protein C1qtnf4, the IL1 receptor IL1r1 (Fig. 7 b), the Janus kinase Jak3, and transcription factors Spi1 and St18. Only IL33 (Fig. 7 c), which can be both pro- and anti-inflammatory, and Rps6ka5, which can induce transcription of anti-inflammatory IL10, were upregulated. Like interferon signalling, old-age interleukin signalling DEGs were characterised by increases in expression (Fig. 7 a). Eleven interleukin signalling DEGs were common to old age CB and SC. These included cytokine receptor subunit Csf2rb, IgE receptor Fcer1g, IL1a (Fig. 7 d), Oas like genes Oasl1 (Fig. 7 e) and Oasl2 (Fig. 7 f) (although both also function in interferon signalling), Spi1, and intracellular signalling molecule Stat6. Four genes, Oas2, Parp14, Stat1, and Zbp1 were also in the interferon signalling list. Whilst IL1a was increased in both CB and SC, IL1b was only increased in SC. The SC also had increased expression of other interleukin signalling molecules such as caspase Casp4, cytokine receptor subunit Csf2rb2, interleukin IL33, interleukin receptors IL1rl1, IL17ra, IL12rb2, IL2rb, IL2rg, IL3ra (Fig. 7 g), IL4ra (Fig. 7 h), IL6ra, interleukin receptor associated kinases Irak2 (Fig. 7 i), Irak3 (Fig. 7 j) and Irak4, and interleukin receptor antagonist IL1rn (Fig. 7 k), amongst others. Some interleukin-related genes including interleukin receptors IL2ra (Fig. 7 l) and interleukin accessory protein IL1rap (Fig. 7 m) had decreased expression. As microglia are a major resident potential inflammatory cell type in the CNS, we compared the aging DEGs to experimentally derived gene lists for disease associated microglia (DAMs, Fig. 8 ), and microglial exhaustion (Fig. 9 ) (see Supplementary File S2, Supplementary Material Online for details). DAMs are a microglial phenotype that have downregulated “homeostatic” microglial genes, and upregulated phagocytic, lysosomal, and inflammatory genes [ 15 , 16 ]. Microglial exhaustion refers to a state of phagocytic exhaustion, and microglia in this state produce excessive oxidative and pro-inflammatory molecules [ 17 ]. There was minimal overlap between the 2 gene sets, with only 4 genes common to both. The aging transcriptome exhibited a profile indicating the presence of DAMs in all four CNS regions investigated, but again with much regional and age-related variability (Fig. 8 a). Whilst significant CTX DAM DEGs were found, there were insufficient numbers to reach significant enrichment (Fig. 3 e). However, HIP, CB, and SC were enriched for DAM-associated DEGs at either middle or old age (Fig. 3 e). The HIP was enriched only in old age, whilst the CB and SC were enriched at both middle and old ages. At middle-age, 7 DAM DEGs were common to both CB and SC, and all increased in expression. These included the glycosylphosphatidylinositol (GPI)-anchored glycoprotein Cd52, the pattern recognition receptor Clec7a (Fig. 8 b), H2-D1, the DNA sensor Ifi204, an inhibitor of NF-κB activation, Lgals3bp, the lysozyme Lyz2 (Fig. 8 c), and a signalling molecule downstream of immune receptors (e.g. Trem2) called Tyrobp (Fig. 8 d). Other genes with increased expression by middle-age include MHC-I and anti-viral proteins (e.g. H2-K1, Ifit1, Nlrc5) in CB and chemokines (e.g. Ccl3 (Fig. 8 e) and Ccl6), proteinases (e.g. Ctsz, Fig. 8 f), transmembrane proteins (e.g. Gpnmb, Itgax, Slamf9), lipid-related genes (e.g. Lpl), and the negative regulator of phagocytosis Cd22 (Fig. 8 g) in SC. Expression of Cdca8, Dhcr7, Igf1, Enpp1, and Enpp2 were significantly decreased in middle-age SC. Approximately 74% of DAM genes that were changed by middle-age were differentially expressed in HIP, CB, and SC by old-age. Other genes like Cst7 (Fig. 8 h) were not increased at middle-age but were in old HIP, CB, and SC. Furthermore, 35 DAM genes were differentially expressed in 2/3 regions, including lipoprotein Apoe (Fig. 8 i), transcription factor Atf3, receptor tyrosine kinase Axl, actin capping protein Capg, component of ferritin Ftl1, lysosomal enzyme Gusb, Spp1, and mitochondrial membrane protein Tspo, amongst others. The large majority had increased expression. The old SC had increased expression of many other DAM genes, including cytokine Ccl4 (Fig. 8 j), microglial marker Cd63 (Fig. 8 k), the cholesterol 25-hydroxylase gene Ch25h (Fig. 8 l), and colony-stimulating factor 1 (Csf1, Fig. 8m). In addition to DAM-associated genes, we found enrichment of genes indicating microglial phagocytic exhaustion in our aging DEGs (Fig. 3 f and Fig. 9 a). At middle-age, only the SC was enriched for exhaustion genes, but by old-age, the HIP, CB, and SC were all enriched. In middle-age SC, most exhaustion DEGs were increased. These included transcription factor Bcl6, Clec7a, immune receptor Havcr2 (Fig. 9 b), immune checkpoint molecule Lag3, immune inhibitory receptor Pdcd1, cytokine-like calcium sensing molecule S100a8, and anti-viral signalling molecule Ticam2 (Fig. 9 c). In old CNS, the HIP, CB, and SC all had increased expression of Clec7a, cytokine Cxcl10 (Fig. 9 d), Havcr2, and Lag3. Interleukin IL33 and secreted phosphoprotein Spp1 (a.k.a osteopontin) both had increased expression in HIP and SC. Old CB and SC both had upregulated inhibitory immune receptor ligand Cd274, co-stimulatory receptors Cd72 (Fig. 9 e) and Cd86 (Fig. 9 f), and Stat1. Old SC also had upregulated advanced glycosylation end-product specific receptor Ager (Fig. 9 g), chemokine Ccl2 (Fig. 9 h), IL-2 receptor IL2rb (Fig. 9 i), purine receptor P2rx7 (Fig. 9 j), and Ticam1 (Fig. 9 k) which mediates TLR3–dependent production of IFN-β. Although most exhaustion DEGs were increased in aging CNS, the transmembrane glycoprotein Cd38 (Fig. 9 l), which links cell activation and survival, was downregulated in aging SC as was chemokine receptor Ccr2 (Fig. 9 m). Activation of Aim2, NLRP1, and NLRP3 inflammasomes Activation of Aim2, NLRP1, and NLRP3 inflammasomes Inflammasomes are key intracellular multimeric protein complexes that initiate inflammatory mechanisms in response to activation of pattern recognition receptors. Inflammasomes activate inflammatory caspases in an inflammasome specific manner. Following activation, these caspases cleave pro-Il1β and/or pro-IL18, activating and releasing cytokines. These inflammasomes are activated by specific signals and involve characteristic signalling pathways dependent on the type of inflammasome and initiating danger signal. As our aging transcriptomes indicate widespread inflammation, we profiled which inflammasome signalling pathways were induced during aging in each CNS region (Fig. 10 a and Supplementary File S2, Supplementary Material Online). Inflammasome genes were not significantly enriched at middle age in any CNS region, nor in CTX at old age (Fig. 3 g). At old age, DEGs in HIP, CB, and SC were enriched for inflammasome genes. Caspase-1 (Casp1, Fig. 10 b) was significantly increased in CTX and CB by middle-age, and in all regions at old-age. Caspase-4 (Casp4, Fig. 10 c) was only significantly increased in old SC, as were 3 of the Nlrp genes, Nlrp1a (Fig. 10 d), Nlrp1b (Fig. 10 e), and Nlrp3 (Fig. 10 f), and the Aim2 (Fig. 10 g) pattern recognition receptor which binds dsDNA. Nlrc4 inflammasome components Naip2 (Fig. 10 h) and Naip5 (Fig. 10 i) were both increased at old age in HIP, CB, and SC, and at middle-age in SC. Naip2 was also increased in middle age CB. Naip6 (Fig. 10 j) was also increased in old CB and SC. Pycard (Fig. 10 k), a core component of inflammasomes which contains the caspase recruitment domain, was also increased in old HIP, CB, and SC. Importantly, the pore-forming Gsdmd (Fig. 10 l), which allows release of IL1β and IL18, was increased in old CB and SC. Interestingly, only the SC had a significant increase in IL1b (Fig. 10 m), one of the main cytokines activated by inflammasomes. The other cytokine, IL18, was not changed with age. Increased expression of non-self, DNA/RNA sensors and cGAS-STING in aging CNS Profiling of inflammasome molecules found increased expression of cytoplasmic DNA and non-self RNA activated inflammasomes. Based on the above, we compared our aging-DEGs to genes that are differentially expressed in conditions of cytoplasmic DNA, Tfam +/− mice [ 31 ] and LINE1 derepression in senescent cells [ 32 ] (Fig. 11 a and Supplementary File S2, Supplementary Material Online). These are models of mitochondrial DNA (mtDNA) release and nuclear genome transposable element (TE) derepression and release, respectively. Genes from the Tfam +/− context were enriched in middle-age CB and all regions in old age (Fig. 3 h). Genes in middle-age CB were all increased in expression. Genes included the interferon induced transmembrane protein Bst2 (Fig. 11 b) which inhibits enveloped virus release, the E3 ubiquitin-protein ligase Dtx3l which is involved in a positive feedback loop for interferon gene expression, the inflammasome modulators Gbp3 and Gbp7, genes involved in detection of cytoplasmic dsDNA (e.g. Ifih1) and viral RNA (e.g. Ifit1 (Fig. 11 c), Ifit3 (Fig. 11 d), and Ifit3b), galectin 3 binding protein Lgals3bp, the immunoproteasome subunit Psmb8, E3 ubiquitin ligase and cellular sensor of ISGylated proteins Rnf213, Rtp4 which can inhibit the IFN-I response, negative regulator of STING Trim30a, Trim30d, and Usp18 which reduces cGAS degradation. The others (H2-Q4, Igtp, Irf7, Irgm1, Oas1a, Oasl2, Parp14, Parp9) have been described above. Tfam +/− genes were enriched in old CTX, HIP, CB, and SC. Several genes had increased expression in all old CNS regions. These included H2-Q4, Ifit3, Igtp, Lgals3bp, Rnf213, and Xaf1 (Fig. 11 e), the latter of which forms a positive feedback loop with IRF-1. Furthermore, almost all genes that were increased in middle-age CB (excluding Gbp7, Parp9, Rtp4) were increased in old HIP, CB, and SC. Cxcl10 and Xaf1 were increased in old HIP, CB, and SC, but not in middle-age. Expression of other genes increased in multiple old regions were common to the CB and SC. These included Dhx58, a regulator of RIG-I and MDA5 signalling (Fig. 11 f), inflammasome modulator, Gbp2 (Fig. 11 g), negative regulator of RIG-I signalling, Ifi35 (Fig. 11 h), transcription factor, IRF9, Irgm2, Slfn2, which reduces type I IFN-induced activation of NF-κB signalling, Stat1 (Fig. 11 i), and cytoplasmic dsDNA/dsRNA sensor, Zbp1. Genes in the LINE1 derepression context were enriched in middle-age CB and SC (Fig. 3 i). Only Ifi204 was significantly increased in both CNS regions at middle-age. The CB also had increased expression of Bst2, the interferon inducible protein, Ifi27 (Fig. 11 j), which is involved in type-I interferon-induced apoptosis and can inhibit RIG-I signalling, Ifih1, interferon induced proteins with tetratricopeptide repeats (Ifit1, Ifit2 (Fig. 11 k), Ifit3) that inhibit translation and modulate apoptosis, and the GTPase, Mx1. The SC had increased lysosomal thiol reductase Ifi30 (Fig. 11 l), transcription factor Irf5, and reduced expression of the IFN-γ and toll-like receptor adapter protein Mal. At old age, the HIP, CB, and SC were all enriched for genes in the LINE1 derepression context. Both Ifi27 and Ifit3 were increased in old CTX, HIP, CB, and SC. Seven other genes were found in HIP, CB, and SC. These included Bst2, Cxcl10, Ifi204, Ifih1, Ifit1, Mx1, and Oas2. Old CB and SC both had increased Ifit2, Ifitm3 (which stops viruses that enter the cell in endosomes from entering the cytoplasm and negatively regulates type I IFN signalling by enhancing the autophagic degradation of IRF3), Irf5, and Stat1. Mal was once again decreased. Expression of the intracellular signalling molecule Stat3 (Fig. 11 m) was also increased in old SC. As aging CNS was enriched for genes induced by cytoplasmic DNA (or dsRNA, DNA:RNA hybrids), we examined expression of cGAS-STING pathway components in our aging transcriptomes (Fig. 12 a and Supplementary File S2, Supplementary Material Online). Although the CTX, HIP, CB, and SC were all enriched for Tfam +/− and/or LINE1 derepression-related genes, only the old SC was enriched for cGAS-STING pathway components (Fig. 3 j), while other regions (that did not have increased IL-1β) were not. Two components of the cGAS-STING components were changed by middle-age, Ifi204 (Fig. 12 b) was increased in CB and SC, and Zdhhc9 (Fig. 12 c) was decreased in SC. By old-age, Ifi204 was also increased in HIP, whilst Irf9 (Fig. 12 d) and Zbp1 (Fig. 12 e) were both increased in CB and SC. The old SC also contained increased expression of the DNA sensor, Cgas (Fig. 12 f), transcription factor Nfkb1 (Fig. 12 g), Sting1 (Fig. 12 h), which is activated by cGAS, negative regulator of cGAS by palmitoylation, Zdhhc18 (Fig. 12 i), and decreased Zdhhc9, which positively regulates cGAS by palmitoylation. Furthermore, Zc3hav1, a viral DNA/RNA detector and STING-inflammation enhancer [ 27 , 28 ] was increased in all CNS regions at both middle and old ages (Fig. 13 ). Increased expression of hundreds of transposable elements in aging spinal cord One possible activator of the cGAS-STING pathway is derepression of TEs, resulting in formation of cytosolic DNA:RNA hybrids, cytosolic cDNA, and/or dsRNA [ 33 ]. We therefore tested whether TEs were differentially expressed in aging CNS. We examined expression of TE families, as well as expression of individual elements. We found total expression of TE classes did not differ with age in any CNS region (Fig. 14 a), nor did expression of the main TE families (Fig. 14 b). However, we did find significant differential expression (FDR < 0.05) of individual TEs (DE-TEs), with most being increased (Fig. 14 c and Supplementary Table S3 , Supplementary Material Online). We found most DE-TEs were region specific, although some overlap between regions occurred (Fig. 14 d-e). In SC, more than half the DE-TEs at middle-age were also differentially expressed at old-age, and always in the same direction (Fig. 14 f). Aging impacted TE expression, particularly in SC, so to evaluate processes potentially involved, we curated a gene set (Supplementary Table S2 , Supplementary Material Online) covering the many layers of TE regulation [ 34 , 35 ]. The 141-gene list represents processes of chromatin and histone modification, DNA and RNA methylation, and piRNA and siRNA pathways that collectively regulate TE expression. Given the number of TE expression changes, there were surprisingly few changes in expression of TE regulatory genes (Table 1 ). In CTX, with the exception of zinc finger protein genes (Zfp, see below), there were no TE regulation DEGs at either middle or old age (for complete analyses see Supplementary File S2, Supplementary Material Online). In HIP, Uhrf1 was a TE regulation DEG, with ~ 50% reductions in expression (based on DESeq2 analyses), at both ages. As a repressor of TE expression, the aging-related reduction in Uhrf1 may contribute to HIP TE expression (Fig. 14 ) [ 36 , 37 ]. In CB there were no TE regulation DEGs at either age. In SC, expression of 4 TE regulation DEGs was increased at old age, with no changes at middle age. Ehmt2 (previously known as G9a) catalyses H3K9 mono- and di-methylation, maintains global DNA methylation, and regulates the 3D genome [ 38 ]. Methyl-CpG-binding-domain protein, Mbd6, recognises chromatin-associated retrotransposon RNA that has been methylated (m 5 C), promoting an open chromatin state [ 39 ]. That Mbd6 preferentially recognises m 5 C in repeat RNA may explain its increased expression with aging when there is increased expression of retrotransposons (Fig. 14 ). Tet1 is a ten-eleven translocation enzyme that oxidises m 5 C to cause DNA demethylation, although it is also thought to have TE repressive actions, with Tet1 binding and resultant action being dependent on underlying epigenetic profiles [ 40 ]. DNA ligase 1, Lig1, is methylated by Ehmt2, which results in Uhrf1 recruitment for DNA methylation maintenance [ 41 ]. These TE regulation gene expression changes in old SC possibly reflect a compensatory, albeit unsuccessful, attempt to restore TE repression. The substantial increase in DE-TEs between middle and old ages is consistent with this possibility. H3K9me2/3 histones are critical for heterochromatin establishment and maintenance, in particular for repeat elements and lineage-specific genes [ 42 ]. We used a 99-protein subset (Supplementary Table S2 , Supplementary Material Online) of a recently characterised H3K9me3 proteome [ 43 , 44 ] to probe our DEGs for potential impacts of aging on this critical heterochromatin protein complex. As with TE regulation DEGs, there was little effect of aging on H3K9me3 proteome gene expression across the CNS (Table 2 ), with the exception of one gene in CB and 5 genes in SC at old age. Crebrf expression was increased in CB of old mice, but the role of this transcription factor in the H3K9me3 proteome is not known. Crebrf has been shown to be involved in metabolic phenotype switching in muscle [ 45 ] and therefore may be involved in the H3K9me3 complex of facultative heterochromatin that is involved in changing cell fate, differentiation status, or phenotype [ 42 ]. In SC expression of two lysine oxidases (Loxl1, Loxl2) was impacted by aging. Through histone interactions, Loxl proteins can affect chromatin compaction state, and Loxl2 oxidises H3K4me3, a histone mark associated with transcription, to remove the me3 group, thereby acting as a repressor [ 46 ]. What the nuclear receptor co-activator 7 (Ncoa7) protein does in the H3K9me9 proteome is not known, but it has been implicated in defence against oxidative stress, cancer, and viruses [ 47 – 49 ]. Polypyrimidine tract-binding protein 1 (Ptbp1) is a heteronuclear ribonucleoprotein with a prominent role in alternative splicing [ 50 ]. A H3K9me3 related role has not been reported. Rela is a subunit of the transcription factor NF-kB that regulates inflammatory responses. Rela- NF-kB is primarily localised in the cytoplasm under basal conditions, although some Rela is bound to chromatin constitutively. Mono-methylation of Rela by a non-histone methyltransferase, results in Ehmt1-mediated chromatin silencing, thereby attenuating NF-kB [ 51 ]. Increased Rela expression in old SC may indicate another mechanism initiated to silence derepressed heterochromatin. DE-TEs were evident across the CNS by middle age, but markedly more so at old age, with increased TE expression much more common than decreased expression in all regions (Fig. 14 ). TE derepression, as opposed to repression, therefore, is a major aging-related CNS TE phenomenon. Somewhat surprisingly, there was relatively modest change in expression of genes encoding proteins involved in TE regulation and the H3K9me3 proteome. However, we noticed a number of Zfp genes in our DEGs for all regions at both middle and old ages, although again there were generally many more Zfps in the old DEG lists and especially in SC. Zfps constitute the largest transcription factor family in mammals and are thought to have an important role in TE repression [ 52 ]. Many Zfp genes encode the Krüppel-associated box (KRAB), a potent repressor of transcription [ 53 ], called KRAB domain-containing zinc-finger proteins (KZfps). KZfp genes are organised into clusters in the genome, with cluster members being evolutionary-related and transcriptionally co-regulated [ 52 ]. KZfp regulation is thought to be cluster related, that is, KZfp tissue or cell-type-specific expression profiles are similar for cluster members. Approximately 85% of mouse and human KZfp genes belong to clusters, and cluster members have similar DNA binding fingerprints [ 52 ]. KZfps bind to TEs (and other genomic elements) to repress their transcription [ 54 ]. Therefore, we determined whether KZfp aging DEGs were members of the same clusters, using previously described criteria; clusters must be of two or more KZfp genes that are not spaced more than 500 kilobases apart [ 52 , 55 ]. We used our DESeq2 analyses to capture the broadest range of KZfp DEGs (FDR < 0.05) (Table 3 ). There was a general trend showing the CNS regions with more aging-related DE-TEs (Fig. 14 ) also had a greater number of KZfp DEGs (Table 3 ) with good correlations between the numbers of differentially expressed TEs and Zfps at both middle (r = 0.85) and old ages (r = 0.99). We found that DE KZfps were generally not related by cluster (Table 3 ), except for the CB. In CTX at middle age 2 clusters on Chr7 had multiple KZfp genes with decreased expression (Zfp428, Zfp575; Zfp579, Zfp580) while no clusters had multiple DE KZfps at old age. No clusters contained multiple HIP DEGs at either age. CB middle age DEGs contained KZfps in the same cluster (4930522L14Rik, Zfp1007 on Chr5). At old age, 5 clusters contained multiple CB DEGs (Zfp937, Zfp442 on Chr2; 4930522L14Rik, Zfp1007 on Chr5; Zfp937 and Zfp442 on Chr1; 4/5 cluster KZfps Gm3604, Zfp1008, Zfp808, Zfp934 on Chr13; Zfp960, Zfp97 on Chr17). All cluster CB KZfps were increased. SC DEGs also contained KZfps from the same cluster at old age (Gm14419, Gm14305 on Chr2; Zfp28, Zfp667 on Chr7; 3/5 cluster KZfps Zfp26, Zfp266, Zfp846 on Chr9; Zfp811, Zfp871 on Chr17). Where multiple DEG KZfps were in the same cluster, DEGs had the same direction of expression change, except in old SC where 2 clusters (Chr2 and Chr7) had KZfps with different directions, possibly indicating loss of cluster co-transcriptional regulation. Interestingly, while the CB contained greater expression of KZfps at old age, most SC KZfp DEGs were decreased in expression. No KZfp DEG was common to all comparisons, although Zfp57 (Fig. 15 a) was consistently decreased in expression in all but CTX middle age. Zfp57 is involved in the acquisition and maintenance of methylation-dependent gene imprinting in neural development [ 56 ], as well as the repression of TEs and nonimprinted genes [ 57 ]. Zfp419 (Fig. 15 b) was a DEG in 5 comparisons, being consistently increased in expression, except in CTX (both ages) and CB (YvM) where it was not reliably detected. Zfp268 (increased, Fig. 15 c), Zfp518a (decreased, Fig. 15 d), and Zfp831 (CTX decreased, SC increased) were DEGs in 3 of the 8 comparisons. With the exception of Zfp831 and Zfp703, all KZfps that were DEGs in more than one comparison, showed the same direction of expression change. Other than as general repressors of TE expression, little is known about most individual KZfps. Adult lifelong intermittent fasting (IF) attenuates expression of microglial inflammatory markers, inflammasomes, non-self DNA/RNA sensors, and re-balances the transposonome As the SC was generally the most age-affected CNS region, we investigated whether IF, a dietary intervention that increases both lifespan and health-span in rodents, attenuates SC inflammaging (see Supplementary Table S4 , Supplementary Material Online for consensus DEG lists and Supplementary Files S1-2, Supplementary Material Online for all DEGs lists and hypergeometric tests). We found IF had a modest effect on gene expression in middle-aged animals, with 212 DEGs (M v M-IF), with 39 of these being age-affected genes (~ 8% of the 510 middle-age-related DEGs, i.e .Y v M) (Fig. 16 a). However, IF had a profound effect in old animals, with an ~ 11-fold increase in the number of DEGs compared to middle-age IF (2351 DEGs, O v O-IF). Furthermore, IF significantly impacted ~ 26% of the old age-related (676 of 2618, Y v O) DEGs. The DEGs were highly enriched for immune-related ontologies (Fig. 16 b), suggesting IF modifies inflammaging. These data demonstrate that IF significantly affects expression of aging and non-aging-related genes. We found that IF reduced expression of a number of key genes involved in neuro-inflammaging processes, DAM (~ 3-fold, 52/162, corrected p = 1.49x10 − 12 ), microglial exhaustion (~ 3-fold, 14/40, corrected p = 0.0004), Tfam knockout (~ 4-fold, 23/50, corrected p = 2.81x10 − 9 ) and LINE1 derepression (~ 3-fold, 8/25, corrected p = 0.035) genes enriched in IF old-age DEGs. Of the 68 DAM genes that were differentially expressed in old SC, 43 were significantly affected by IF. Of these, ~ 90% (38 genes) that showed increased in expression in old age, were universally reduced by IF, with an average ~ 40% reduction in expression (Fig. 16 c). These genes included pattern recognition receptors Clec7a and Ifi204, lipid and inflammation-related genes Apoe, Ch25h, Lpl, cysteine protease inhibitor Cst7, lysosomal genes Ctsz and Lyz2, negative regulator of phagocytosis Cd22, transmembrane proteins Gpnmb and Tyrobp, and proton-sensing G protein-coupled receptor Gpr65. We also found significant reduction in expression of microglial exhaustion genes. Of the 28 exhaustion genes that were differentially expressed in old SC, ~ 40% (12 genes) were affected by IF (Fig. 16 d). Expression of 11 of these genes that was increased with aging, was reduced by IF. These included cytokines and chemokines Ccl2, Cxcl10, IL33, and Spp1, immune inhibitory receptor ligand Cd274 and its receptor Pdcd1, co-stimulatory receptors Cd72 and Cd86, Clec7a, transmembrane enzyme Entpd1 which converts extracellular ATP to ADP, and Socs3 which inhibits pro-inflammatory M2 polarisation pathways. A number of other microglial exhaustion genes (e.g. Cd244a) were non-significantly decreased. Contrastingly, expression of Bcl6 was increased with age, and more so with IF. IF also reduced expression of many aging DEGs associated with the Tfam +/− and LINE1 derepression contexts (Fig. 16 e). Of the 34 old-age DEGs from Tfam +/− genes, ~ 55% (19 genes) were affected by IF. We found reduced expression of non-self, DNA/RNA sensors Ifih1, Ifit3, Ifit3b, Dhx58, and Zbp1, and Bst2 which can inhibit production of IFN and proinflammatory cytokines, as well as cytokines Ccl2 and Cxcl10, which modulate Rigi (Ddx58) and Mda5 (Ifih1) signalling in response to viral nucleic acids, inflammasome modulators Gbp2 and Gbp3, transcription factor Irf7, inhibitor of NF-κB activation Lgals3bp, Oas1a which is involved in activation of RNase L to degrade viral RNA, Oasl2 which enhances RIGI signalling, Parp9 which is involved in pro-inflammatory macrophage activation, immunoproteasome component Psmb8 which is involved in microglial mediated neuroinflammation, and genes that regulate the response to viral infection Trim30a and Trim30d. The lysophospholipase D, Enpp2, was reduced in both aging, and further with IF. With the LINE1 derepression genes, ~ 40% of aging DEGs (7 of 17) were affected by IF. Bst2, Cxcl10, Ifih1, and Ifit3 also appear in the Tfam +/− context. Expression of the DNA sensors Ifi204, Ifit2 (which binds viral ssRNA), and Ifitm3 (which blocks viral membrane fusion and cytoplasmic entry) were all reduced by IF. Expression of Rigi/Ddx58), which is a pattern recognition receptor for viral nucleic acids (dsRNA), was non-significantly increased in old, but returned to young levels in the IF group. Expression of key inflammasome (Fig. 16 f) and cGAS-STING (Fig. 16 g) pathway genes was also decreased by IF in old animals. The non-canonical inflammasome Casp4, pore forming Gsdmd, Nlrc4 inflammasome components Naip2 and Naip5, and Nlrp1a and Nlrp1b all had reduced expression with IF. IL1b was non-significantly decreased. In the cGAS-STING pathway, downregulation occurred in non-self, DNA/RNA sensing molecules Ifi204 and Zbp1, the phosphatase Ppp6c, a negative regulator of cGAS, and Table 1 which is required for Tak1 activation of STING. Interestingly, a number of genes changed more in the IF groups than aging counterparts. For example, expression of Cgas, Irf9, and Zdhhc18 was increased in old, and further increased with IF. Zdhhc9 expression was decreased in old and further decreased with IF. Sting1 was non-significantly reduced by IF. Heatmaps of IF comparisons for the DAM, microglial exhaustion, inflammasome, and cGAS-STING gene lists, as well as heatmaps for the other gene lists, are available in Supplementary Fig. S1 -9, Supplementary Material Online. Intermittent fasting drives differential expression of transposable elements We did not find any obvious global changes in the expression of repeat classes (Fig. 17 a) or the major repeat families (Fig. 17 b) with IF. While expression of some repeat elements was differentially expressed with age, and more-so at old-age, there was an unexpected finding with IF. The number of DE-TEs between young and IF groups was ~ 5-6-fold greater than normal aging counterparts (244 Y v M, 1270 Y v M-IF, 1343 Y v O, 7906 Y v O-IF, Fig. 17 c and Supplementary Table S5 , Supplementary Material Online). However, while aging generally increased expression of DE-TEs, IF resulted in a greater proportion of TEs having significantly decreased expression compared to young animals, especially at old-age (ratio TE increase:TE decrease; Y v M ~ 2.5:1; Y v M-IF ~ 1:1.5; Y v O ~ 3:1; Y v O-IF ~ 1:2.25). IF also resulted in a greater proportion of TEs with significantly decreased expression compared to age matched controls (MvM-IF ~ 1:1.15, OvO-IF ~ 1:1.73). We also found a more general increase in expression of TEs in middle age and old animals compared to young (Fig. 17 d), which was reversed in IF animals. Reduced expression of TEs was also found in IF animals compared to age-matched controls (Fig. 17 e). As was carried out for aging, the IF DEGs (M v M-IF and O v O-IF) were compared to a 141-gene TE regulation gene set. In contrast to the Y v M comparison, where there were no TE regulation DEGs, two DEGs were increased in expression in M v M-IF (Table 4 ). Suv39h1 trimethylates H3k9 to form heterochromatin to repress TEs [ 58 ]. Yy1 is a transcription factor that activates transcription of the mouse Tf subfamily of L1 in early development [ 59 ]. However, whether Yy1 activates or represses transcription is cell type and locus-dependent [ 60 ] and in differentiated cells such as neurons, it is a repressor of L1 transcription [ 61 ]. The increase in Yy1 expression seen at middle and old age in IF animals is, therefore, predicted to result in L1 repression. In the O v O-IF comparison, there were 26 TE regulation DEGs, an effect that constituted a significant 2.3-fold enrichment (p = 4.7x10 5 ). With the exception of H3f3b and Pbrm1 that decreased expression, the other 24 DEGs all increased expression with IF. Pbrm1, Arid2, and Dpf2 are part of the chromatin remodelling complex, BRM-associated factors (BAF), which regulates epigenetic modifications and chromatin accessibility that are important for CNS function [ 62 ]. Ehmt1, Ehmt2, Kmt5c, Setdb1, and Suv39h1 are all histone methyltransferases, and Dnmt3a is a DNA methyltransferase. Alkbh5 is a RNA demethylase that “erases” methylation (m6A) in RNA, an RNA modification involved in TE repression [ 34 ]. Brd4 and Dgcr8 are members of RNA interference (RNAi) mechanisms now appreciated to silence certain repeat elements [ 63 ]. Chd5 (chromodomain/helicase/DNA-binding domain 5) is part of the nucleosome-remodelling and deacetylase (NuRD) complex, that is expressed in neurons and important for establishing and maintaining neuronal cell fate through the co-repression of non-fate-specific and co-activation of fate-specific genes [ 64 – 66 ]. Hinfp (histone H4 transcription factor) is a zinc finger transcriptional regulator that silences TEs in somatic cells. Loss of Hinfp results in increased expression of most TEs and enhanced aging-related phenotypes [ 67 ]. Safb (scaffold attachment factor B) encodes proteins that protect somatic cell genomes by preventing retrotransposition of transcribed intronic L1s and ERVs through a mechanism that recognises TE-related biased RNA coding sequence to retain TE transcripts in the nucleus [ 68 ]. Sltm (Safb-like transcription modulator) is one of three Scabf-related genes involved in prevention of TE retrotransposition [ 68 ]. Sinhcaf (SIN3-HDAC complex associated factor) is a member of the SIN3 histone deacetylase complex that is recruited to repress L1 and ERV transcriprion [ 40 , 69 ]. Tnrc18 (trinucleotide repeat containing 18; previously known as Zfp46/469) is an H3K9me3-specific reader that recruits co-repressors such as Sin3-Hdac complex to repress ERVs [ 69 ]. Zcchc8 (zinc finger, CCHC domain containing 8), Zfc3h1 (zinc finger, C3H1-type containing), Pabpn1 (poly(A) binding protein, nuclear 1), and Zc3h3 (zinc finger CCCH type containing 3) are members of the NEXT (nuclear exosome targeting) and PAXT (polyA tail exosome targeting) complexes that are important for nuclear RNA degradation [ 70 ]. NEXT has recently been shown to cooperate with human silencing hub (HUSH) complexes to degrade TE RNAs [ 70 , 71 ]. Spen (spen family transcription repressor), a RNA-binding protein essential for X chromosome inactivation, also represses ERV by binding to ERV transcripts and recruiting chromatin-silencing proteins [ 72 ]. Sirt1 (sirtuin 1) is a lysine deacetylase with pleiotropic effects in the cell. Pharmacological activation of Sirt1 re-established heterochromatin and re-repression of retrotransposons in aging cells [ 73 ]. Overall, IF impacts most if not all levels of TE regulation. Comparing IF DEGs with the H3K9me3 proteome list, there was a single overlapping gene at middle age (increased expression of the histone methyltransferase gene Suv39h1, Fig. 18 ) but 14 overlapping genes at old age in the IF SC, which was a significant enrichment (1.77-fold over-enriched, p = 0.025). Except for decreased expression of the Loxl genes and Myef2, expression of all other genes was increased in IF compared to control, ad libitum-fed groups (Table 5 ). Myelin expression factor 2 (Myef2) is an RNA-binding protein involved in regulating oligodendrocyte differentiation and myelination of the CNS [ 74 ]. Myef2 has also been implicated in cancer and atherosclerosis [ 75 , 76 ], but its role in the H3K9me3 proteome is not known. PR-domain-containing, zinc finger proteins (PRDMs) are chromatin factors that regulate transcription and chromatin structure, for example by repressing genes involved in DNA methylation. Prdm15 maintains stem cell pluripotency, binding to both euchromatin and heterochromatin to activate or repress transcription in a context specific way [ 77 ]. A specific role in H3K9me3 has not been reported. RNA-binding motif proteins (RBMs), such as Rbm6, are involved in splicing, DNA damage repair [ 78 ], and tumour suppression [ 79 ]. As with other proteins in the H3K9me3 proteome, roles for Rbm6 and Rbm12b1 have not been reported for this repressive histone modification, but the RNA binding ability of these proteins may function in RNA-related aspects of heterochromatin formation and maintenance [ 34 ]. Splicing factor proline- and glutamine-rich (Sfpq) is a RNA-binding protein with multiple roles in splicing, regulation of transcription, DNA damage repair, genome stability, and paraspeckle formation in response to cell stressors, such as viral invasion [ 80 , 81 ]. Interestingly, Sfpq was shown to repress Rela (see above) expression, thereby muting the innate immune system’s interferon response to virus. This was removed by a long non-coding RNA that competed with Sfpq’s repressive binding to Rela [ 82 ]. Transformer 2 alpha homolog (Tra2a), another RNA-binding protein that is a serine/arginine-rich mRNA splicing factor, dysregulation of which has been implicated in a number of cancers [ 83 ]. Tra2a is also thought to be a trans-acting RNA methylator [ 84 , 85 ]. Overall, in the aging SC, IF appears to modify the H3K9me3 proteome resulting in changes in expression of histone methyltransferase genes (Setdb1, Suv39h1), RNA-binding and modifying genes (Myef2, Rbm121, Rbm6, Tra2a), genes involved in chromatin structure (Prdm15, Loxl1, Loxl2), and genes involved in prevention of transposition of already transcribed TE RNA (Safb, Sltm). Adult life-long IF was associated with differential expression of KZfps when comparing control ad libitum-fed animals with IF animals of the same age. While there were only 4 KZfp DEGs at middle age (M v M-IF), there were 63 KZfp DEGs at old age (O v O-IF) (Table 6 ), far more than the 40 KZfp DEGs that resulted from aging to old age alone (Table 3 ). The majority (2/4 at M, 39/63 at O) of IF KZfp DEGs had decreased expression. Only 6 KZfps that were DEGs with aging (Zfp40, Zfp41, Zfp407, Zfp419, Zfp518a, Zfp57) were also DEGs with IF (asterisked KZfps in Table 6 ), and 2 of these showed the opposite direction of expression change (italicised KZfps, Table 6 ). None of the 4 middle age KZfps were from the same cluster. In old age IF, 7 clusters had multiple KZfps DE. Most clusters were in the same direction (Zfp979, Zfp982 on Chr4 – decreased; Zfp105, Zfp660 on Chr9 – decreased; Zfp354a, Zfp354c on Chr11 – increased; Zfp808, Zfp934 on Chr13 – decreased; Zfp273, Zfp458, Zfp65, Zfp748, Zfp759, Zfp953 on Chr13 – decreased), however 2 clusters had genes DE in opposite directions (Zfp110 – decreased, Zfp324 – increased on Chr7; Zfp192 – decreased, Zkscan3 – increased on Chr13). Only 6 KZfps that were altered by IF were also changed in aging alone, Zfp40, Zfp41, Zfp407, Zfp419, Zfp518a, Zfp57. Of these, Zfp40, Zfp41, Zfp407, Zfp419, Zfp518a were all reduced by aging and further reduced by IF. However, Zfp57 (Fig. 19 ) was reduced by aging (~ 67% of young expression) but increased by IF, though not quite to young expression levels (~ 84% of young expression). Discussion Chronic inflammation is deleterious to cell, organ, and organism function. Inflammation is a hallmark of CNS aging, a chronic, sterile form of inflammation that is a major factor in functional declines across cognitive and sensorimotor domains. Chronic CNS inflammaging (neuroinflammaging), is characterised by sustained pro-inflammatory signalling, dyshomeostatic microglia that inappropriately prune synapses, dysfunctional mitochondria, and a pro-inflammatory senescence associated secretory phenotype (SASP) associated with senescent cells [ 86 – 88 ]. For the most part, studies have focussed on the cellular responses that characterise neuroinflammation, with causative mechanisms having received limited focus, with some exceptions [ 7 , 32 ]. Additionally, most studies are limited to one or two CNS regions. Here, we have taken a deep-sequencing genomics approach to better appreciate how neuroinflammaging develops across the CNS. We demonstrate in four CNS regions of broad interest; cortex, hippocampus, cerebellum, and spinal cord, there is not a universal impact of neuroinflammaging, nor indeed by aging in general, and the timing, extent of inflammation, and inflammatory pathways involved are to a certain degree CNS region dependent. Somewhat surprisingly, we found SC to be profoundly affected by aging in general and neuroinflammaging in particular. Remarkably, at old age SC had almost triple the number of aging-related DEGs compared to the CB, the next most impacted region. The CTX and HIP, arguably the most investigated CNS regions, were the least aging affected (Fig. 1 ). SC was also a neuroinflammaging hotspot, although CB was also markedly impacted (Fig. 2 ). Indeed, by most transcriptomic indicators of neuroinflammaging, SC was the most impacted region: inflammation (Figs. 2 – 3 ), interleukin signalling (Fig. 7 ), DAM (Fig. 8 ), microglial exhaustion (Fig. 9 ), inflammasome activation (Fig. 10 ), and TE derepression (Fig. 14 and Tables 1 – 4 ), were all most markedly affected in SC, with CB showing greater impact with interferon signalling (Fig. 6 ), non-self, DNA/RNA signalling (Fig. 11 and see Supplementary Table S6, Supplementary Material Online), and cGAS-STING signalling (Figs. 12 – 13 ). Notably, both SC and CB were significantly pro-inflammatory by middle-age, although the effects (number of affected genes and effect sizes) were generally modest. Unexpectedly, the CTX seems relatively resistant to neuroinflammaging, with only modest changes in inflammatory markers by old age. In contrast, the HIP while largely unaffected at middle age, was pro-inflammatory by old age. Why SC is a neuroinflammaging hotspot is an intriguing question. One possible cause is aging-related increase in blood-SC-barrier (BSCB) leakiness that would result in entry of normally excluded, potentially inflammatory, blood-borne factors and immune cells. Increased blood-CNS barrier leakiness is a common feature of neuroinflammation. Normally, the BSCB is leakier than the BBB [ 89 ] and while we confirmed this difference, we did not find an age-related increase in BSCB paracellular leakiness [ 90 ]. Increased BSCB leakiness, therefore, does not appear to be a reason for the relatively high SC neuroinflammaging. Another possibility is based on myelin debris. Myelin degradation is a characteristic of aging white matter (WM) [ 91 – 93 ] and degenerating myelin proteins can produce highly immunogenic peptides [ 94 ] that may trigger an inflammatory response. It is noteworthy the two CNS regions most inflamed with aging (SC and CB) have the highest relative myelin contents of the regions studied. WM associated microglia (WAM) phagocytose myelin debris in aging CNS [ 95 ], but prolonged or overwhelming myelin degradation could lead to formation of DAM (see below), consistent with the WAM-DAM continuum proposed by Safaiyan et al . (2021). Such prolonged myelin debris engulfment may be one reason microglia end up exhausted (see below). Whatever the causes, the heightened SC neuroinflammatory profile should lead to increased interest in this CNS region. The whole CNS needs to be functionally preserved if CNS health-span is to be improved. It is also of note that the mouse SC more accurately reflects the human brain in terms of relative GM/WM and therefore may, in certain aspects, be a more appropriate model of human brain aging. DAM constitute a novel subpopulation of microglia characterised by association with neurological diseases. For example, DAMs are spatially associated with amyloid beta (Aβ) plaques in AD, and have upregulation of phagocytic and lipid processing genes [ 15 ]. Why normally aging C57B/6 mouse would exhibit a similar profile to DAM is not entirely clear, as mouse brain does not form amyloid plaques [ 96 ]. However, the aging CNS is also characterised by build-up of other waste and toxic products including certain lipids [ 97 – 99 ], proteins [ 100 ], and a lipid-protein pigment called lipofuscin [ 101 ]. Also, as mentioned above, debris from degenerating myelin is processed by a population of microglia (WAM) that has transcriptomic overlap with DAM [ 95 ]. It also could be that the aged SC, given its heightened inflammatory state, is more disease-like and ‘pathologically’ aging than ‘normally’ aging. The relationship between DAM and WAM and other microglial subpopulations awaits further scrutiny. Immune cell exhaustion, while best understood in T cells [ 102 ], has also been reported in other immune cells [ 103 ], including macrophages [ 104 ]. This type of exhaustion is a result of chronic exposure to stimuli, subsequently dampening immune cell effectiveness, and causing metabolic signalling changes. Unsurprisingly, microglia, the resident macrophages of the CNS, also suffer prolonged exposure to stimuli and are thought to develop exhaustion states [ 17 , 105 ]. For example, microglia exhaustion is characterised by impaired phagosome formation, and excessive production of oxidative and proinflammatory molecules [ 17 ]. This results in both an inability to clear myelin debris and extracellular waste adequately, and the production of damaging molecules by exhausted microglia. Importantly, these microglial products can induce neuronal loss [ 17 ]. Cd22, a canonical B cell receptor, was recently found to be specifically expressed on brain microglia, expression that increased with aging, and that negatively regulates microglial phagocytosis of myelin debris, amyloid-b oligomers, and a-synuclein fibrils. Blockade of Cd22 signalling reprograms aged microglia towards a homeostatic transcriptional state and improves cognitive function [ 106 ]. Consistent with a role of Cd22 in microglial exhaustion, we found age-related expression of Cd22 to be increased in all regions, with SC having by far the largest increase (Fig. 8 g). While Cd22 is also considered a DAM gene, there are gene expression overlaps between microglia subpopulations, and it is possible the DAM state is a precursor to microglial exhaustion. Inflammaging is considered sterile, therefore inflammatory triggers must be from within the tissue and not result from invasive pathogens. While myelin debris is a potential trigger for sterile inflammaging, there are other possibilities. There are two genomes in eukaryotic cells that may be responsible for induction of innate immune responses under sterile conditions, the nuclear and mitochondrial genomes. These genomes contain viral and bacterial sequences, respectively. Nucleic acids from either genome may elicit infection-like immune responses from cells under certain conditions. For the nuclear genome, the most likely scenario is an age-related loss of TE repression, which has been reported by others [ 32 , 107 – 109 ]. Simon et al. (2019) found a loss of LINE1 TE repression with age that presented as an increase in L1 cytoplasmic DNA, but not RNA [ 107 ]. Gulen et al . (2023) showed microglial mitochondria in old mouse brains released mtDNA into the cytoplasm to trigger cGAS-STING signalling [ 7 ]. There are many potential causes of mtDNA leak into the cytoplasm (see [ 110 ]) and much work remains to determine the causes of mtDNA (or mtRNA) release in the aging CNS. An intriguing possibility is the role of cholesterol, with overload of this lipid leading to disruption of mitochondrial and ER membranes and attachment of nucleoids, subsequently resulting in mtDNA release [ 110 ]. We have previously reported increased cholesterol in SC of old animals [ 111 , 112 ], raising the possibility of dysregulated cholesterol being involved in SC neuroinflammaging. Regarding the nuclear genome, we did not find significant increases in TE expression in any region with age at the TE class or family levels. We did, however, identify hundreds of individual TEs were differentially expressed in old CNS. Importantly, TEs with increased expression vastly outnumbered those with decreased expression in all four CNS regions and both age comparisons (Fig. 14 ). While derepression suggests increased expression of TEs, we found expression of a considerable number of TEs was decreased. There is precedent for this phenomenon. For example, when TEs are derepressed via knockout of methyltransferases involved in heterochromatin formation or DNA methylation, there are both increases and decreases in TE expression [ 113 , 114 ], changes that are consistent with the increases and decreases in H3k9me3 and DNA methylation observed following knock out of Setdb1 [ 114 ]. Our results are in line with those found by Ramirez et al. (2022), who found hundreds of generally over-expressed TEs in the hippocampus with age [ 108 ]. In contrast, Wahl et al. (2023) did not find any significant differences in individual expression of TEs, but found that most TE classes (LINEs, SINEs, LTRs, and DNA transposons) were more highly expressed in old hippocampus [ 109 ]. Together, our data supports the hypothesis that derepressed transposable elements are driving, at least in part, CNS inflammaging. To better understand the mechanisms that might be involved with the widespread TE expression changes, we curated a list comprising genes involved in the many layers of TE regulation. Surprisingly, of the 141 genes in the list, expression of only four was significantly changed by aging in SC, the most affected CNS region. Expression of all four SC genes was increased (Table 1 ). The four genes are involved in various aspects of histone, DNA, and RNA methylation, and thus chromatin accessibility. That increased expression of these genes did not prevent aging-related TE expression, potentially indicates a failed compensatory response to chromatin changes and TE derepression. There was little association between extent of TE derepression and TE regulation gene expression changes, with no changes in CTX or CB, but decreased expression of Uhrf1, a repressor of TEs [ 36 , 37 ], was seen in HIP, the least impacted CNS region for TE derepression. H3K9me3 is important in the establishment and maintenance of heterochromatin, and TE repression. We investigated the impact of aging on expression of genes encoding proteins of the H3K9me3 proteome. As with the TE regulation gene set, the old SC was most affected, with five genes differentially expressed between Y and O (Table 2 ). Roles for most of the proteins encoded by H3K9me3 proteome DEGs are not known, but Loxl and Rela proteins have putative chromatin-silencing activities [ 46 , 51 ]. Given the degree to which TE expression was affected by aging, the relatively modest molecular signature for changes in TE regulation and H3K9me9 were somewhat surprising and indicate dysregulation at other levels. In this regard, and in stark contrast to the aforementioned TE regulation and H3K9me3 proteome genes, we found expression of KZfp genes to be more impacted by aging. There was good correlation between the numbers of differentially expressed TEs and KZfps at both middle (r = 0.85) and old ages (r = 0.99), consistent with an important role of KZfps in TE regulation. KZfps are organised in clusters across the mouse genome, with clusters characterised by KZfps with similar genome sequence specificity. Cluster members also tend to be transcribed together [ 52 , 55 ]. However, when we examined cluster membership of aging-related KZfp DEGs, only ~ 26% (27/102) of aging differences were related by cluster, although approximately half of old age CB DE KZfps were in a cluster with another DE KZfp. We also found some cluster members in the old SC with discordant expression changes which is suggestive of dysregulation of KZfp co-transcriptional regulation. While there has been intensive interest in targets of KZfps, characterisation of the mechanisms regulating their expression has received less focus. Accumulating evidence, in particular from the cancer field, however, indicates epigenetics, transcription factors, and non-coding RNAs, all play a role [ 115 ]. Indeed, aging-related changes in histone modifications and DNA methylation have been widely reported [ 116 ]. Hypo- or hypermethylation of KZfp regulatory sequences would alter KZfp transcription and the overall lack of common KZfp DEGs across the CNS and between middle and old ages is consistent with the stochastic nature of aging-related DNA methylome and epigenetic changes in general [ 117 ]. Unlike TEs, we were not able to measure cytoplasmic mtDNA with our genomics approach, therefore it was not possible to determine cytoplasmic levels of this inflammatory trigger. However, Zbp1 protein was recently demonstrated to stabilise a form of stressed mtDNA and initiate cytoplasmic cGAS signalling [ 31 ], and we found Zbp1 expression was increased in all four CNS regions (Fig. 11 a and Fig. 12 e). While this increase could be considered an indication of aging-related accumulation of cytoplasmic mtDNA, Zbp1 is not specific for mtDNA as it can also bind viral nuclei acids [ 118 ]. Further work is required to determine which molecular species (viral/bacterial DNA/RNA) trigger neuroinflammaging in the various cell types of the CNS. In summary, we have established different CNS regions are differentially impacted by neuroinflammation with normal aging. The neuroinflammaging is associated with a substantial number of differentially expressed TEs, with SC the most inflamed region having by far the largest number of these derepressed elements, a finding consistent with the notion these evolutionary, virally-derived sequences, are a significant underlying cause of neuroinflammaging. Dietary restriction interventions have universally been shown to improve neuroinflammation, including calorie restriction and the various forms of IF (for reviews see [ 119 – 121 ]). However, with the exception of one report [ 109 ], such studies have not investigated the association of TE regulation with neuroinflammaging. Here, we have demonstrated IF has a profound effect on TE regulation. We first confirmed findings of previous studies demonstrating the effects of dietary interventions on various aspects of CNS inflammaging. Adult lifelong IF had a robust effect on microglia with both DAM and exhaustion phenotypes improved. IF downregulated expression of Tyrobp pathway signalling molecules Trem2 and Tyrobp, cholesterol-related genes Apoe and Ch25h, lysosomal genes Ctsz and Lyz2, and the negative regulator of phagocytosis Cd22, and pattern recognition receptors Clec7a and Ifi204. The reduced Cd22 expression is consistent with reports that blockade of Cd22 restores homeostatic function in aged microglia, including restoration of phagocytosis of myelin debris and amyloid peptides [ 106 , 122 ]. The microglial exhaustion-related gene, Cd72, is traditionally associated with B cells, but has recently been shown to be expressed by microglia and involved in inflammation and phagocytosis [ 123 ]. Taken together, the reduction in expression of DAM and microglial exhaustion genes, our data suggests that IF attenuates aging pro-inflammatory microglial phenotypes. In turn, we found reductions in inflammasome genes, particularly Nlrp1 and Casp4. While Cgas expression was unaffected by IF, we found a mild reduction in Sting1 expression and a non-significant reduction in Il1b expression. Long term IF is directly or indirectly modifying mechanisms that are driving these microglial phenotypes. One possible explanation may be the induction of autophagy and mitophagy by IF [ 124 ], which limits the continual build-up of waste products over the lifespan. Overall, IF improves neuroinflammaging in SC consistent with that demonstrated in other CNS regions. As we were particularly interested in potential drivers of aging-related sterile CNS inflammation, we wanted to determine whether IF could mitigate or reduce these inflammatory triggers. To do this we made use of gene expression profiles derived from models of cytoplasmic accumulation of non-self, nucleic acids, specifically, the Tfam +/− (bacterially derived) mtDNA and (virally derived) LINE1 derepression models [ 31 , 32 ]. We found aging resulted in increased expression of many genes associated with these models across the CNS, especially at old age (Fig. 11 ). Genes of the cGAS-STING pathway that is activated following non-self, nucleic acid detection, were also upregulated as expected (Fig. 12 ). Note, many upregulated genes associated with these models are, unsurprisingly, detectors of non-self, nucleic acids (Figs. 10 – 12 , and Supplementary Table S2 , Supplementary Material Online; Aim2, Ifit1, Ifit2, Ifit3, Dhx58, Ifi204, Zbp1, cGAS). IF was effective at reducing the expression of many of these Tfam +/− and LINE1 derepression related genes (Fig. 17 ), indicating a decreased burden of non-self, nucleic acids. We found expression of the viral RNA-sensing gene, Zc3hav1, was significantly increased at middle and old ages in all four CNS regions, the only non-self, nucleic acid (DNA and/or RNA) sensor to do so (Fig. 13 ). Zc3hav1 encodes a zinc finger CCCH domain-containing protein that binds to specific RNA viruses, targeting them for degradation [ 27 ]. Recently, Zc3hav1 was shown to have a role in cGAS-STING activation and therefore is also involved anti-DNA virus activity [ 28 ]. It also improves NLRP3 oligomerisation, a critical step in the activation of the cytoplasmic NLRP3 inflammasome that plays important roles in innate immunity [ 28 ]. Significantly, in the context of the present work, Zc3hav1 also inhibits LINE1 and Alu transposition [ 125 ]. Although Zc3hav1 expression in SC was not impacted by IF, given its prolonged, CNS-wide involvement, ability to detect multiple viral DNA and RNA sequences, and various antiviral activities [ 28 ], Zc3hav1 may be a therapeutic target for neuroinflammaging and warrants further investigation. An important issue is identification of the proximate triggers of neuroinflammaging. Non-self, nucleic acids are considered likely triggers of sterile neuroinflammation, albeit not the only ones. However, due to the number of different TEs and the difficulty in determining the intracellular localisation of non-self, nucleic acids (for example, mtDNA residing in nucleoids within mitochondria would not be a trigger), we used an alternate method for quantifying non-self, nucleic acid triggers. By quantifying expression of non-self, DNA/RNA sensors as surrogates for nucleic acids themselves, we could assess inflammation triggers and showed the SC and CB had by far the greatest trigger load (see Supplementary Table S6, Supplementary Material Online). We curated a 33-gene non-self, DNA/RNA sensor list and, based on the number of significantly changed sensor genes in each region at both middle and old age, the SC and CB had by far the largest number of sensor DEGs, followed by HIP, with CTX being relatively unaffected. Notably, expression of all sensor DEGs was increased with aging in all four CNS regions, with CB having the largest inflammation trigger load at middle age. Furthermore, based on the nucleic acids these sensors detect, there does not appear to be a single, dominating trigger, with sensors for both single- and double-stranded RNA and DNA all being upregulated. Remarkably, the majority of sensor DEGs (14/24) that were upregulated at old age in SC, were universally downregulated with IF. Why IF did not reduce expression of all upregulated sensors requires further evaluation. Nonetheless, IF has a profound effect on non-self, DNA/RNA sensor expression, presumably reflecting decreases in the presence of these nucleic acid species in the aged CNS, a finding consistent with the overall beneficial effects of this dietary intervention. Relatively little is known about the effects of IF, or other dietary interventions for that matter, on TE expression. We found the number of TEs that were differentially expressed when comparing IF with age-matched ad libitum-fed controls, was markedly higher than for aging alone, which seems counterintuitive in the context of the anti-inflammation effects associated with IF. Intriguingly, however, there was a flip in the ratio of numbers of TEs with increased to decreased expression, with aging associated with ~ 2.5–3-fold greater number of TEs with increased expression relative to those with decreased (Fig. 18 ), whereas IF saw ratios of ~ 1.2–1.7 in favour of decreased expression. Exactly how this results in reduction in inflammation remains to be determined, but a role for TE transcripts themselves may be at play. Indeed, methylation of chromatin-associated TE RNAs has been shown to regulate heterochromatin [ 34 ]. Whatever the mechanism, the aforementioned decreased expression of non-self, nucleic acid sensors indicates the trigger load has dropped with IF. It is also thought that TE expression needs to be balanced for somatic cell survival [ 126 , 127 ]. Given the markedly changed TE expression profile induced by IF we probed for TE regulation and H3k9me3 proteome gene expression changes. Unlike with aging, where expression of only a few genes in these sets was changed (Tables 1 – 2 ), IF resulted in markedly more changes (Tables 4 – 5 ). For TE regulation there were over six-times as many DEGs as there were for aging alone, and expression of ~ 92% (22/24) of these was elevated. Similarly, for H3K9me3 proteome genes, IF was associated with a ~ 3-fold increase in DEGs, with expression of ~ 79% (11/14) of these being elevated. Considering just the broad increases in expression in TE regulation and H3K9me3 related genes, one interpretation is there was a substantial increase in chromatin remodelling, presumably resulting in re-heterochromatisation from the relatively de-heterochromatised state of the aging CNS. Importantly, IF impacted multiple levels of TE regulation including histone, DNA, and RNA methylation, RNA interference, chromatin remodelling, TE pre- and post-transcriptional activities such as nuclear RNA retention (to prevent cytoplasmic translation) and export via nuclear exosomes for lysosomal degradation, transcriptional repressor protein recruitment, and histone deacetylation. The IF-associated DEGs for the H3K9me3 proteome largely have uncharacterised roles for this heterochromatin structure but many have RNA binding properties. IF has also been previously described to alter epigenetic mechanisms, including in H3K9me3 in cerebellum [ 128 ]. The final layer of TE regulation we investigated was the KZfps. Compared to aging alone, IF was associated with more KZfp DEGs (63 v 40 aging alone at old age) in SC with most of these showing decreased expression compared to age-matched ad libitum-fed controls. Surprisingly few (6/63) Zfp DEGs were also DEGs with aging alone. Since the beneficial effects of IF on TE regulation involves many regulatory layers, it may be difficult to target pharmacologically. Limitations Due to availability issues, and also to allow comparison with the most comprehensive report to date using this IF model [ 12 ], only male mice were used in the present work. Studies will need to be replicated in female mice to identify any sex-related differences. Use of an inbred mouse strain may also be considered a limitation in the context of mimicking human aging. However, even with an isogenic strain we observed marked differences in overt characteristics such as body weights, degree of thoracic spine kyphosis, neoplasms, and fur greying, including between cage mates. Aging is stochastic by nature and using inbred mice could in fact be considered an experimental advantage, as it allows assessment of behavioural, cellular and molecular variabilities caused by aging per se and not genetic variability. Our bulk RNA sequencing (RNAseq) analyses used RNA extracted from CNS region tissue homogenates and, therefore, cell type resolution is lost. With the exception of genes known to be expressed in specific cell types, it is generally not possible to ascribe aging and IF related expression changes to particular types of cells using this type of bulk RNAseq approach. Importantly, recent single-cell RNAseq (scRNAseq) reports have demonstrated that most aging-related (non-TE) DEGs are cell-type- and CNS-region-type specific, further underscoring the need to carry out cell-type-specific gene expression analyses in different CNS regions [ 129 , 130 ]. An important consideration, however, is that sc/snRNAseq (single nucleus) technologies typically use short, single-end, 3' derived reads that compromises mapping accuracy for TEs as they are highly repetitive. Further developments are needed to improve sensitivity and accuracy of scTE mapping and expression quantification [ 131 ] before this approach can be broadly implemented. The present genomics study was undertaken to improve our understanding of potential drivers of CNS neuroinflammaging. Deep RNAseq confers a sensitive and robust, transcriptome-wide, discovery approach that is ideal for initial investigations of poorly understood mechanisms. Molecular signatures have been identified that hopefully will provide the neurobiology of aging field bases to build on using complementary and confirmatory protein, epigenetic, and chromatin-based approaches. Ideally, process(es) will be identified that can be therapeutically targeted to obviate the need to comply with a challenging, long-term, dietary intervention. Conclusions We have demonstrated, based on transcriptomics indices along with gene ontology analyses, that SC is an aging hotspot region of the CNS, both in terms of the impacts of aging on gene expression as well as the degree of inflammation. The aged SC appears to suffer from substantial TE derepression, and it is likely increased TE expression is a significant causal trigger to the neuroinflammation, although aging-related cytoplasmic release of mtDNA cannot be ruled out as an additional inflammatory trigger. Encouragingly, we identify IF as a potential mitigator of neuroinflammaging, as this dietary intervention profoundly influenced the TE expression profile and reduced most inflammatory markers. We propose IF resulted in a rebalancing of the transposonome. Future work will hopefully lead to a more mechanistic understanding of how IF achieves its beneficial effects and the identification of processes that could be targeted by a more widely acceptable intervention. Methods Animals Healthy C57BL/6 male mice were used for all experiments. Animals were maintained under standard housing conditions, on a 12-hour light-dark cycle, with food (Specialty Feeds SF00-100) and water available ad libitum. ADF animals had no food access every second day from ~ 8 weeks of age for their lifespan. All mice were euthanized with 1mL i.p. Lethabarb (325mg/ml) before tissue collection. All animal work was undertaken in strict accordance with the University of Newcastle Animal Ethics Committee, and New South Wales and Australian animal research guidelines. Young mice were 3–4 months old, middle-aged were 12–14 months old, and old mice were 24 months old. Tissue Dissection and Cryosectioning All phosphate buffered saline (PBS) used was diethyl pyrocarbonate (DEPC) treated and autoclaved before use to inactivate RNases. Mice were transcardially perfused on ice with 50mL ice-cold PBS, and brains and spinal cords were dissected out, frozen in isopentane on dry ice and stored at -80°C for later use. Tissues were then mounted in optimum cutting temperature (O.C.T.) compound and cryosectioned at 100µm thickness. Cryosections were thaw mounted on RNase-free glass microscope slides and stored at -80°C. Tissue preparation for bulk RNA sequencing CNS regions were dissected out from brain sections using a clean scalpel blade and collected in a tube containing RNAlater (ThermoFisher). RNA was extracted using the miRNeasy Micro Kit (Qiagen) following manufacturer’s instructions. All RNA samples were DNAse treated in solution using DNase I (Invitrogen) as per manufacturer instructions. Briefly, RNA was DNase treated for 15 mins with DNase I and the DNase subsequently inactivated by addition of 25mM EDTA and heating to 65°C for 10 minutes. DNA-free, RNA samples were sent to the Australian Genome Research Facility (AGRF, Melbourne) for sequencing. RNA sequencing Total RNA was rRNA depleted, fragmented and first and second (dUTP) cDNA strands synthesised. Adaptors were ligated and the first strand underwent 13 cycles of PCR amplification. cDNA was sequenced using TruSeq PE Cluster Kit v3 reagents and the NovaSeq 6000 system (Illumina) with between ~ 74–121 million (CTX), ~ 92–123 million (HIP), ~ 40–108 million (CB), ~ 79–146 million (SC), 150 bp, paired end reads, per sample. For each region, all samples were run on a single lane. Bioinformatics Read files were first subject to QC using the FASTQC tool [ 132 ]. Adaptors were removed using the Cutadapt tool [ 133 ]. Forward and reverse reads files were aligned using the STAR aligner [ 134 ]. Aligned files were assessed to determine DEGs using Cuffdiff [ 135 ], HTSeq-count [ 136 ] followed by Deseq2 [ 137 ] or edgeR [ 138 ]. DEG lists were compiled using an FDR cut-off of < 0.05, and requiring significance in 2 of 3 DEG analysis programs. DEG lists were analysed for Gene Ontology (GO) using Metascape [ 29 ]. DEG lists were compared with the inflammation-related gene lists in Supplementary Table S2 , Supplementary Material Online. Enrichment of gene sets was determined using a hypergeometric overlap calculator ( https://systems.crump.ucla.edu/hypergeometric/ ). p-values were corrected for multiple comparisons. For expression of transposable elements, read files were aligned using STAR with specific parameters as suggested by TEtranscripts [ 139 ]. We performed testing of differential expression for transposable elements using Telescope with an FDR cut-off of < 0.05 [ 140 ]. Images Volcano plots, heatmaps, and bar graphs were generated with Graphpad Prism . For heatmaps of the counts per million (CPM) for each gene was calculated. Gene counts for each sample were divided by either the average CPM of the region (CTX, HIP, CB, SC heatmaps), or the average CPM of the young (IF heatmaps). For bar graphs, gene counts for each sample were divided by the young average CPM of the region. Venn diagrams were generated using an online tool available at https://bioinformatics.psb.ugent.be/webtools/Venn/ . Gene Ontology cluster heatmaps were generated with Metascape [ 29 ]. Graphical methods generated with BioRender.com . Declarations All animal work was undertaken in strict accordance with the University of Newcastle Animal Ethics Committee (Approvals: A-2021-137, A-2016-625, A-2015-526) and New South Wales and Australian animal research guidelines. Author Contribution M.C., E.C., and D.S. conceived the study. M.C. was involved in the planning and performing of all experiments and analyses, generated all figures, tables, and supplements, and wrote the manuscript. 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Supplementary Files Tables.docx SupplementaryFileS1DEGsfromDESeq2edgeRandcuffdiff.xlsx SupplementaryFileS2HypergeometricTestsandGeneOverlaps.xlsx SupplementaryFileS3KZfpclustersandexpressiondirections.xlsx InflammationSupplementalFigures.docx SupplementaryTables.xlsx Cite Share Download PDF Status: Under Revision Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6165725","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":425234166,"identity":"cd7db9c1-9689-48c2-81fc-33935363c03c","order_by":0,"name":"Mitchell J Cummins","email":"","orcid":"","institution":"University of Newcastle","correspondingAuthor":false,"prefix":"","firstName":"Mitchell","middleName":"J","lastName":"Cummins","suffix":""},{"id":425234167,"identity":"368cada0-281a-48e7-a979-9e314adde351","order_by":1,"name":"Ethan T Cresswell","email":"","orcid":"","institution":"University of Newcastle","correspondingAuthor":false,"prefix":"","firstName":"Ethan","middleName":"T","lastName":"Cresswell","suffix":""},{"id":425234168,"identity":"36ac34b6-28cd-4ee4-b5ee-670c1c41de99","order_by":2,"name":"Doug W Smith","email":"data:image/png;base64,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","orcid":"","institution":"University of Newcastle","correspondingAuthor":true,"prefix":"","firstName":"Doug","middleName":"W","lastName":"Smith","suffix":""}],"badges":[],"createdAt":"2025-03-05 22:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6165725/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6165725/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78357019,"identity":"c73aff96-583a-4c16-89b1-d277c55d15e2","added_by":"auto","created_at":"2025-03-12 11:38:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":182518,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe spinal cord is an aging hotspot.\u003c/strong\u003e (\u003cstrong\u003ea\u003c/strong\u003e) Volcano plots of all genes based on DESeq2 fold changes and FDR, in multiple CNS regions with aging. Red lines indicate fold change and statistical cut-offs for defining differentially expressed genes (DEGs). (\u003cstrong\u003eb\u003c/strong\u003e) Number of DEGs (2 from 3 criterion) in each CNS region (FDR \u0026lt;0.05). (\u003cstrong\u003ec-d\u003c/strong\u003e) Venn diagrams of DEGs at middle age (\u003cstrong\u003ec\u003c/strong\u003e) and old age (\u003cstrong\u003ed\u003c/strong\u003e) indicating limited overlap of DEGs across regions. Circos plots (\u003cstrong\u003ee-f\u003c/strong\u003e) of DEGs at middle age (\u003cstrong\u003ee\u003c/strong\u003e) and old age (\u003cstrong\u003ef\u003c/strong\u003e). Circos plots indicate limited gene (purple) but prominent Gene Ontology (blue) regional overlap. CTX - Cortex, HIP - Hippocampus, CB - cerebellum, SC - spinal cord.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/4b68ee04068feeacbda756e9.png"},{"id":78357026,"identity":"02fe7355-9a08-40ed-af5b-20b7d7e14427","added_by":"auto","created_at":"2025-03-12 11:38:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":168357,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetascape Top 100 Gene Ontology clustering of CNS regions aging differentially expressed genes (DEGs).\u003c/strong\u003e Note, SC has the most significant GOs of any CNS region at both ages. (\u003cstrong\u003ea\u003c/strong\u003e) Clustering of middle-age DEGs. (\u003cstrong\u003eb\u003c/strong\u003e) Clustering of old age DEGs. CTX - Cortex, HIP - Hippocampus, CB - cerebellum, SC - spinal cord.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/a8f8803af84678f699f97dcf.png"},{"id":78358490,"identity":"40132f08-c126-48d5-bb16-57655d6827ba","added_by":"auto","created_at":"2025-03-12 11:54:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96540,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnrichment of inflammatory processes in aging CNS. \u003c/strong\u003e(\u003cstrong\u003ea-j\u003c/strong\u003e) Fold enrichment (left y-axis) and number of differentially expressed genes (DEGs, right y-axis) of inflammatory processes impacted by aging. Note, fold enrichment (FE) is impacted by overall DEG number in a CNS region, including non-inflammation-related DEGs. For that reason, SC which had a larger number of aging-related DEGs compared to other regions, appears to not always be the most impacted region, based on FE. However, when comparing inflammation impact based on number of genes, SC had the highest number in 9 of 10 inflammatory processes. (\u003cstrong\u003ea\u003c/strong\u003e) Positive regulation of inflammatory response. (\u003cstrong\u003eb\u003c/strong\u003e) Negative regulation of inflammatory response. (\u003cstrong\u003ec\u003c/strong\u003e) Interferon-mediated signalling. (\u003cstrong\u003ed\u003c/strong\u003e) Interleukin-mediated signalling. (\u003cstrong\u003ee\u003c/strong\u003e) Disease-associated microglia. (\u003cstrong\u003ef\u003c/strong\u003e) Microglial phagocytic exhaustion. (\u003cstrong\u003eg\u003c/strong\u003e) Inflammasomes. (\u003cstrong\u003eh\u003c/strong\u003e) Tfam\u003csup\u003e+/-\u003c/sup\u003e. (\u003cstrong\u003ei\u003c/strong\u003e) LINE1 derepression. (\u003cstrong\u003ej\u003c/strong\u003e) cGAS-STING. Legend below holds for all graphs. CTX - Cortex, HIP - Hippocampus, CB - cerebellum, SC - spinal cord. * hypergeometric p-value \u0026lt;0.05, **\u0026lt;0.01, ***\u0026lt;0.001, ****\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/cc91f04f3cf98c6fdcb812f5.png"},{"id":78358493,"identity":"5b81827a-d63e-49e6-9f07-56665b75b04e","added_by":"auto","created_at":"2025-03-12 11:54:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":262407,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePro-inflammatory genes in aging CNS. \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Heatmap of positive regulation of inflammatory response genes (GO:0050729). Expression is represented as a fold change relative to the region average, with a maximum expression of linear 2-fold for scaling. (\u003cstrong\u003eb-m\u003c/strong\u003e) Fold changes are relative to the CNS region average in young, which is set at 1.0. Error bars are ±SD. (\u003cstrong\u003eb\u003c/strong\u003e) C3. (\u003cstrong\u003ec\u003c/strong\u003e) Fcgr1. (\u003cstrong\u003ed\u003c/strong\u003e) H2-D1. (\u003cstrong\u003ee\u003c/strong\u003e) H2-K1. (\u003cstrong\u003ef\u003c/strong\u003e) Serpine1. (\u003cstrong\u003eg\u003c/strong\u003e) Tlr2. (\u003cstrong\u003eh\u003c/strong\u003e) Tlr4. (\u003cstrong\u003ei\u003c/strong\u003e) Tlr6. (\u003cstrong\u003ej\u003c/strong\u003e) Tnfrsf1a. (\u003cstrong\u003ek\u003c/strong\u003e) Ccl5. (\u003cstrong\u003el\u003c/strong\u003e) Gpr4. (\u003cstrong\u003em\u003c/strong\u003e) Tnf. CTX - Cortex, HIP - Hippocampus, CB - cerebellum, SC - spinal cord. * p-value \u0026lt;0.05, **\u0026lt;0.01, ***\u0026lt;0.001, ****\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/88dd79d33b4ecf2411332a46.png"},{"id":78357043,"identity":"b9d76e8e-b2c6-4823-86a6-a3133ca4cb27","added_by":"auto","created_at":"2025-03-12 11:38:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":251699,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnti-inflammatory genes in aging CNS. \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Heatmap of negative regulation of inflammatory response genes (GO:0050728). Expression is represented as a fold change relative to the region average, with a maximum expression of linear 2-fold for scaling. (\u003cstrong\u003eb-m\u003c/strong\u003e) Fold changes are relative to the CNS region average in young, which is set at 1.0. Error bars are ±SD. (\u003cstrong\u003eb\u003c/strong\u003e) Hgf. (\u003cstrong\u003ec\u003c/strong\u003e) Ptpn6. (\u003cstrong\u003ed\u003c/strong\u003e) Ccn3. (\u003cstrong\u003ee\u003c/strong\u003e) Cx3cr1. (\u003cstrong\u003ef\u003c/strong\u003e) Aoah. (\u003cstrong\u003eg\u003c/strong\u003e) Cd200r1. (\u003cstrong\u003eh\u003c/strong\u003e) Enpp3. (\u003cstrong\u003ei\u003c/strong\u003e) Igf1. (\u003cstrong\u003ej\u003c/strong\u003e) Irgm2. (\u003cstrong\u003ek\u003c/strong\u003e) Lrfn5. (\u003cstrong\u003el\u003c/strong\u003e) Smad3. (\u003cstrong\u003em\u003c/strong\u003e) Sytll. CTX - Cortex, HIP - Hippocampus, CB - cerebellum, SC - spinal cord. * p-value \u0026lt;0.05, **\u0026lt;0.01, ***\u0026lt;0.001, ****\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/6bb7722e9f64669b86b45cd7.png"},{"id":78357125,"identity":"510bd531-dee7-4726-ac58-08ab44cc2b8a","added_by":"auto","created_at":"2025-03-12 11:38:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":178752,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInterferon signalling genes in aging CNS. \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Heatmap of interferon-mediated signalling pathway genes (GO:0140888). Expression is represented as a fold change relative to the region average, with a maximum expression of linear 2-fold for scaling. (\u003cstrong\u003eb-m\u003c/strong\u003e) Bar charts of selected genes. Fold changes are relative to the CNS region average in young, which is set at 1.0. Error bars are ±SD. (\u003cstrong\u003eb\u003c/strong\u003e) Ifih1. (\u003cstrong\u003ec\u003c/strong\u003e) Igtp. (\u003cstrong\u003ed\u003c/strong\u003e) Irgm1. (\u003cstrong\u003ee\u003c/strong\u003e) Irf7. (\u003cstrong\u003ef\u003c/strong\u003e) Oas1a. (\u003cstrong\u003eg\u003c/strong\u003e) Oas1b. (\u003cstrong\u003eh\u003c/strong\u003e) Mmp12. (\u003cstrong\u003ei\u003c/strong\u003e) Oas2. (\u003cstrong\u003ej\u003c/strong\u003e) Oas3. (\u003cstrong\u003ek\u003c/strong\u003e) Ifngr1. (\u003cstrong\u003el\u003c/strong\u003e) Ifitm1. (\u003cstrong\u003em\u003c/strong\u003e) Irf1. CTX - Cortex, HIP - Hippocampus, CB - cerebellum, SC - spinal cord. *\u0026lt;0.05, **\u0026lt;0.01, ***\u0026lt;0.001, ****\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/898012a8d8b4d745ec7fc9ab.png"},{"id":78357412,"identity":"18c6e111-e86e-4660-9dcb-32964dc8ceac","added_by":"auto","created_at":"2025-03-12 11:46:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":207970,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInterleukin signalling genes in aging CNS. \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Heatmap of interleukin-mediated signalling pathway genes (GO:0038154, GO:0035722, GO:0035772, GO:0035723, GO:0038173, GO:0097400, GO:0035655, GO:0070498, GO:0038155, GO:0070106, GO:0038110, GO:0038172, GO:0061514, GO:0038156, GO:0035771, GO:0038043, GO:0070102, GO:0038112, GO:0038113). Expression is represented as a fold change relative to the region average, with a maximum expression of linear 2-fold for scaling. (\u003cstrong\u003eb-m\u003c/strong\u003e) Fold changes are relative to the CNS region average in young, which is set at 1.0. Error bars are ±SD. (\u003cstrong\u003eb\u003c/strong\u003e) IL1r1. (\u003cstrong\u003ec\u003c/strong\u003e) IL33. (\u003cstrong\u003ed\u003c/strong\u003e) IL1ra. (\u003cstrong\u003ee\u003c/strong\u003e) Oasl1. (\u003cstrong\u003ef\u003c/strong\u003e) Oasl2. (\u003cstrong\u003eg\u003c/strong\u003e) IL3ra. (\u003cstrong\u003eh\u003c/strong\u003e) IL4ra. (\u003cstrong\u003ei\u003c/strong\u003e) Irak2. (\u003cstrong\u003ej\u003c/strong\u003e) Irak3. (\u003cstrong\u003ek\u003c/strong\u003e) IL1rn. (\u003cstrong\u003el\u003c/strong\u003e) IL2ra. (\u003cstrong\u003em\u003c/strong\u003e) IL2rap. CTX - Cortex, HIP - Hippocampus, CB - cerebellum, SC - spinal cord. *\u0026lt;0.05, **\u0026lt;0.01, ***\u0026lt;0.001, ****\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/592ffb1303f4b5bb8f2da233.png"},{"id":78357421,"identity":"0da5fe64-8069-46b7-ad49-524b41d0f742","added_by":"auto","created_at":"2025-03-12 11:46:49","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":244671,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDisease-associated microglia (DAM) genes in aging CNS. \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Heatmap of DAM genes from a recently published list (Sobue et al., 2021). Expression is represented as a fold change relative to the region average, with a maximum expression of linear 2-fold for scaling. (\u003cstrong\u003eb-m\u003c/strong\u003e) Fold changes are relative to the CNS region average in young, which is set at 1.0. Error bars are ±SD. (\u003cstrong\u003eb\u003c/strong\u003e) Clec7a. (\u003cstrong\u003ec\u003c/strong\u003e) Lyz2. (\u003cstrong\u003ed\u003c/strong\u003e) Tyrobp. (\u003cstrong\u003ee\u003c/strong\u003e) Ccl3. (\u003cstrong\u003ef\u003c/strong\u003e) Ctsz. (\u003cstrong\u003eg\u003c/strong\u003e) Cd22. (\u003cstrong\u003eh\u003c/strong\u003e) Cst7. (\u003cstrong\u003ei\u003c/strong\u003e) Apoe. (\u003cstrong\u003ej\u003c/strong\u003e) Ccl4. (\u003cstrong\u003ek\u003c/strong\u003e) Cd63. (\u003cstrong\u003el\u003c/strong\u003e) Ch25h. (\u003cstrong\u003em\u003c/strong\u003e) Csf1. CTX - Cortex, HIP - Hippocampus, CB - cerebellum, SC - spinal cord. *\u0026lt;0.05, **\u0026lt;0.01, ***\u0026lt;0.001, ****\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/643db3e29d8c622b4be27200.png"},{"id":78357140,"identity":"7756045b-24c2-4a89-a7be-abb3f01fcd94","added_by":"auto","created_at":"2025-03-12 11:38:54","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":148905,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicroglial exhaustion genes in aging CNS. \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Heatmap of microglial phagocytic exhaustion genes from a recently published list (Bido et al., 2021). Expression is represented as a fold change relative to the region average, with a maximum expression of linear 2-fold for scaling. (\u003cstrong\u003eb-m\u003c/strong\u003e) Fold changes are relative to the CNS region average in young, which is set at 1.0. Error bars are ±SD. (\u003cstrong\u003eb\u003c/strong\u003e) Havcr2. (\u003cstrong\u003ec\u003c/strong\u003e) Ticam2. (\u003cstrong\u003ed\u003c/strong\u003e) Cxcl10. (\u003cstrong\u003ee\u003c/strong\u003e) Cd72. (\u003cstrong\u003ef\u003c/strong\u003e) Cd86. (\u003cstrong\u003eg\u003c/strong\u003e) Ager. (\u003cstrong\u003eh\u003c/strong\u003e) Ccl2. (\u003cstrong\u003ei\u003c/strong\u003e) IL2rb. (\u003cstrong\u003ej\u003c/strong\u003e) P2rx7. (\u003cstrong\u003ek\u003c/strong\u003e) Ticam1. (\u003cstrong\u003el\u003c/strong\u003e) Cd38. (\u003cstrong\u003em\u003c/strong\u003e) Ccr2. CTX - Cortex, HIP - Hippocampus, CB - cerebellum, SC - spinal cord. *\u0026lt;0.05, **\u0026lt;0.01, ***\u0026lt;0.001, ****\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/e7d2f0d6ea3d43314fe51af1.png"},{"id":78357437,"identity":"8d003892-57e8-431a-9b0e-a347ca869e62","added_by":"auto","created_at":"2025-03-12 11:46:53","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":135592,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInflammasome genes in aging CNS. \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Heatmap of inflammasome genes. Expression is represented as a fold change relative to the region average, with a maximum expression of linear 2-fold for scaling. (\u003cstrong\u003eb-m\u003c/strong\u003e) Fold changes are relative to the CNS region average in young, which is set at 1.0. Error bars are ±SD. (\u003cstrong\u003eb\u003c/strong\u003e) Casp1. (\u003cstrong\u003ec\u003c/strong\u003e) Casp4. (\u003cstrong\u003ed\u003c/strong\u003e) Nlrp1a. (\u003cstrong\u003ee\u003c/strong\u003e) Nlrp1b. (\u003cstrong\u003ef\u003c/strong\u003e) Nlrp3. (\u003cstrong\u003eg\u003c/strong\u003e) Aim2. (\u003cstrong\u003eh\u003c/strong\u003e) Naip2. (\u003cstrong\u003ei\u003c/strong\u003e) Naip5. (\u003cstrong\u003ej\u003c/strong\u003e) Naip6. (\u003cstrong\u003ek\u003c/strong\u003e) Pycard. (\u003cstrong\u003el\u003c/strong\u003e) Gsdmd. (\u003cstrong\u003em\u003c/strong\u003e) IL1b. CTX - Cortex, HIP - Hippocampus, CB - cerebellum, SC - spinal cord. *\u0026lt;0.05, **\u0026lt;0.01, ***\u0026lt;0.001, ****\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/9be492a9c64bb2958fb9aac0.png"},{"id":78357061,"identity":"dcfb1be2-4e3f-45f1-a9d0-e0fa3a1ea5cd","added_by":"auto","created_at":"2025-03-12 11:38:48","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":185387,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResponse to non-self, DNA/RNA detection in aging CNS. \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Heatmap of genes expressed in the Tfam\u003csup\u003e+/-\u003c/sup\u003e (Lei \u003cem\u003eet al.\u003c/em\u003e, 2023) and LINE1 derepression (De Cecco\u003cem\u003e et al.\u003c/em\u003e, 2019) contexts. Expression is represented as a fold change relative to the region average, with a maximum expression of linear 2-fold for scaling. (\u003cstrong\u003eb-m\u003c/strong\u003e) Fold changes are relative to the CNS region average in young, which is set at 1.0. Error bars are ±SD. (\u003cstrong\u003eb\u003c/strong\u003e) Bst2. (\u003cstrong\u003ec\u003c/strong\u003e) Ifit1. (\u003cstrong\u003ed\u003c/strong\u003e) Ifit3. (\u003cstrong\u003ee\u003c/strong\u003e) Xaf1. (\u003cstrong\u003ef\u003c/strong\u003e) Dhx58. (\u003cstrong\u003eg\u003c/strong\u003e) Gbp2. (\u003cstrong\u003eh\u003c/strong\u003e) Ifi35. (\u003cstrong\u003ei\u003c/strong\u003e) Stat1. (\u003cstrong\u003ej\u003c/strong\u003e) Ifi27. (\u003cstrong\u003ek\u003c/strong\u003e) Ifit2. (\u003cstrong\u003el\u003c/strong\u003e) Ifi30. (\u003cstrong\u003em\u003c/strong\u003e) Stat3. CTX - Cortex, HIP - Hippocampus, CB - cerebellum, SC - spinal cord. *\u0026lt;0.05, **\u0026lt;0.01, ***\u0026lt;0.001, ****\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/8e0a9e2c2fbdcdf9c2ee6000.png"},{"id":78357448,"identity":"13db31b5-9b01-4453-bf8a-cbe7a2f97d78","added_by":"auto","created_at":"2025-03-12 11:46:54","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":130285,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ecGAS-STING genes in aging CNS. \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Heatmap of cGAS-STING genes. Expression is represented as a fold change relative to the region average, with a maximum expression of linear 2-fold for scaling. (\u003cstrong\u003eb-i\u003c/strong\u003e) Fold changes are relative to the CNS region average in young, which is set at 1.0. Error bars are ±SD. (\u003cstrong\u003eb\u003c/strong\u003e) Ifi204. (\u003cstrong\u003ec\u003c/strong\u003e) Zdhhc9. (\u003cstrong\u003ed\u003c/strong\u003e) Irf9 (\u003cstrong\u003ee\u003c/strong\u003e) Zbp1. (\u003cstrong\u003ef\u003c/strong\u003e) Cgas. (\u003cstrong\u003eg\u003c/strong\u003e) Nfkb1 (\u003cstrong\u003eh\u003c/strong\u003e) Sting1. (\u003cstrong\u003ei\u003c/strong\u003e) Zdhhc18. CTX - Cortex, HIP - Hippocampus, CB - cerebellum, SC - spinal cord. *\u0026lt;0.05, **\u0026lt;0.01, ***\u0026lt;0.001, ****\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/46bb9b4b2566be9900cf39d4.png"},{"id":78357114,"identity":"adb8bc7d-358d-48e2-9578-b033c6b0fe4a","added_by":"auto","created_at":"2025-03-12 11:38:53","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":45803,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eZc3hav1 expression in aging CNS.\u003c/strong\u003e Fold changes are relative to the CNS region average in young, which is set at 1.0. Error bars are ±SD. *\u0026lt;0.05, **\u0026lt;0.01, ***\u0026lt;0.001, ****\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/187219548b2622ab0af9e2ff.png"},{"id":78357109,"identity":"cd264b9d-df62-4e3f-b32b-7c51ec2e66dd","added_by":"auto","created_at":"2025-03-12 11:38:51","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":127318,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAging differentially affects transposable element (TE) expression across the CNS.\u003c/strong\u003e (\u003cstrong\u003ea\u003c/strong\u003e) TEs classes were not differentially expressed with age. (\u003cstrong\u003eb\u003c/strong\u003e) The main repeat families were not differentially expressed between young (blue) and middle-age (purple) or old (red). (\u003cstrong\u003ec\u003c/strong\u003e) Hundreds of individual TEs were DE in at least 1 CNS region, with the majority increasing in expression with age. (\u003cstrong\u003ed-e\u003c/strong\u003e) The large majority of DE-TEs were only changed in one region at both (\u003cstrong\u003ed\u003c/strong\u003e) middle age and (\u003cstrong\u003ee\u003c/strong\u003e) old age. (\u003cstrong\u003ef\u003c/strong\u003e) Over half of the DE-TEs in the spinal cord at middle age were also changed at old age, and always in the same direction. CTX - Cortex, HIP - Hippocampus, CB - cerebellum, SC - spinal cord.\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/03eb90a9f87a9a4857ecd5a3.png"},{"id":78357046,"identity":"fba8c9f9-5278-4879-826b-bda24c8693ea","added_by":"auto","created_at":"2025-03-12 11:38:47","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":77390,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene expression of Krüppel-associated box (KRAB) domain-containing zinc-finger protein in aging CNS.\u003c/strong\u003e (\u003cstrong\u003ea-d\u003c/strong\u003e) Fold changes are relative to the CNS region average in young, which is set at 1.0. Error bars are ±SD. (\u003cstrong\u003ea\u003c/strong\u003e) Zfp57. (\u003cstrong\u003eb\u003c/strong\u003e) Zfp419. (\u003cstrong\u003ec\u003c/strong\u003e) Zfp268 (\u003cstrong\u003ed\u003c/strong\u003e) Zfp518a. CTX - Cortex, HIP - Hippocampus, CB - cerebellum, SC - spinal cord. *\u0026lt;0.05, **\u0026lt;0.01, ***\u0026lt;0.001, ****\u0026lt;0.0001. Note Zfp419 HIP and SC comparisons were significant using the 2/3 programs criteria.\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/212a346e8f45785758f62343.png"},{"id":78357449,"identity":"6f065f3c-6879-4df2-9285-fd9676901d5c","added_by":"auto","created_at":"2025-03-12 11:46:54","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":193266,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntermittent fasting attenuates inflammatory gene expression in aging spinal cord.\u003c/strong\u003e (\u003cstrong\u003ea\u003c/strong\u003e) Venn diagram of differentially expressed genes (DEGs) in normal aging at middle (YvM) and old (YvO) age, and DEGs induced by intermittent fasting (IF). (\u003cstrong\u003eb\u003c/strong\u003e) Metascape ontology enrichment of old age DEGs that were affected by IF. (\u003cstrong\u003ec\u003c/strong\u003e) Selected disease associated microglia (DAM) genes. (\u003cstrong\u003ed\u003c/strong\u003e) Selected microglial phagocytic exhaustion genes. (\u003cstrong\u003ee\u003c/strong\u003e) Selected non-self, DNA/RNA induced genes. (\u003cstrong\u003ef\u003c/strong\u003e) Selected inflammasome genes. (\u003cstrong\u003eg\u003c/strong\u003e) Selected cGAS-STING genes. (\u003cstrong\u003ec-g\u003c/strong\u003e) Fold changes are relative to the average in young, which is set at 1.0. Error bars are ±SD. *\u0026lt;0.05, **\u0026lt;0.01, ***\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/f41cfc8c793c37637e9f5508.png"},{"id":78357099,"identity":"ca2951cb-d68d-494a-84b6-bb8159061ade","added_by":"auto","created_at":"2025-03-12 11:38:51","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":90816,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntermittent fasting decreases transposable element expression in aging spinal cord.\u003c/strong\u003e (\u003cstrong\u003ea\u003c/strong\u003e) TEs classes were not globally differentially expressed with age or intermittent fasting (IF). (\u003cstrong\u003eb\u003c/strong\u003e) The main repeat families were not differentially expressed after IF. (\u003cstrong\u003ec\u003c/strong\u003e) Thousands of individual TEs were repressed by IF. (\u003cstrong\u003ed\u003c/strong\u003e) TEs had a trend to be increased with age which was reversed by IF. (\u003cstrong\u003ee\u003c/strong\u003e) Reduction of TEs of age matched controls. Y - young, M - middle-age, O - old, TE - transposable element, IF - intermittent fasting\u003c/p\u003e","description":"","filename":"17.png","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/e759fe355f2c531d1c689ab5.png"},{"id":78357032,"identity":"af164a39-2cca-4280-8e15-4bf1beb4cdb9","added_by":"auto","created_at":"2025-03-12 11:38:45","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":68769,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntermittent fasting increases Suv39h1 gene expression in aging spinal cord.\u003c/strong\u003e Fold changes are relative to the young expression average, which is set at 1.0. Error bars are ±SD. IF - intermittent fasting. *\u0026lt;0.05, **\u0026lt;0.01, ***\u0026lt;0.001, ****\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"18.png","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/c12f97e0a695a0b8a8b26b29.png"},{"id":78357144,"identity":"f0610625-0a22-4008-9e7f-30c71c023574","added_by":"auto","created_at":"2025-03-12 11:38:55","extension":"png","order_by":19,"title":"Figure 19","display":"","copyAsset":false,"role":"figure","size":60061,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntermittent fasting increases Zfp57 gene expression in aging spinal cord.\u003c/strong\u003e Fold changes are relative to the young expression average, which is set at 1.0. Error bars are ±SD. IF - intermittent fasting. *\u0026lt;0.05, **\u0026lt;0.01, ***\u0026lt;0.001, ****\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"19.png","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/4d2f5cd91f552b62efd77acd.png"},{"id":78357133,"identity":"8e91f1fd-b8f7-42ba-b70d-f6cb44142e14","added_by":"auto","created_at":"2025-03-12 11:38:54","extension":"png","order_by":20,"title":"Figure 20","display":"","copyAsset":false,"role":"figure","size":134079,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Methods section.\u003c/p\u003e\n\u003cp\u003eGraphical Methods Summary\u003c/p\u003e","description":"","filename":"UF1.png","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/e15c94e1277987839d46c7ba.png"},{"id":78358836,"identity":"fce1c5d1-8a58-4d01-b562-b25063b7f2e7","added_by":"auto","created_at":"2025-03-12 12:02:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4879365,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/fce415ad-2efe-4db2-a262-543a5ffbedcd.pdf"},{"id":78358498,"identity":"5f25824d-3d94-4d7e-9456-38bc65e58da7","added_by":"auto","created_at":"2025-03-12 11:54:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":31685,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/398fc74760e497be566dc5fa.docx"},{"id":78358491,"identity":"68a8a8a1-5b11-4285-9ad0-7d39551dec30","added_by":"auto","created_at":"2025-03-12 11:54:50","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1492803,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFileS1DEGsfromDESeq2edgeRandcuffdiff.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/fdcba62b3e7730211a931dbc.xlsx"},{"id":78357411,"identity":"3297d37c-8eaf-4d7c-9ff4-11e67de1df86","added_by":"auto","created_at":"2025-03-12 11:46:47","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":548566,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFileS2HypergeometricTestsandGeneOverlaps.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/6f4fa2097df17f1069d8837b.xlsx"},{"id":78357034,"identity":"5f4519df-a74d-465a-965c-cb20781316d6","added_by":"auto","created_at":"2025-03-12 11:38:46","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1306189,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFileS3KZfpclustersandexpressiondirections.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/98d478845434f1da3de8f2a1.xlsx"},{"id":78357028,"identity":"49c7ebff-6f39-4936-881d-a7f1ec528124","added_by":"auto","created_at":"2025-03-12 11:38:45","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":2650043,"visible":true,"origin":"","legend":"","description":"","filename":"InflammationSupplementalFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/bfe643fad534708386af55c6.docx"},{"id":78357410,"identity":"6e9ec4d5-1ebc-4adb-8250-fdffe4669811","added_by":"auto","created_at":"2025-03-12 11:46:47","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":317870,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6165725/v1/04c1aefc1529a3a2a12dcded.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Intermittent fasting attenuates CNS inflammaging - rebalancing the transposonome","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInflammaging \u0026ndash; a term describing the chronic low-grade inflammation that occurs in aging tissues, is a hallmark of aging [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite its immune resistant nature, the CNS is not spared this inflammation, with the resident immune cells, microglia, as well as astrocytes, thought to be key drivers of neuroinflammaging. Microglia can adopt multiple states broadly captured by pro- and anti-inflammatory phenotypes, and aging results in a shift to the pro-inflammatory microglial state [\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Many factors can drive microglia into this state including accumulation of lipid droplets, amyloid-β, and cytosolic DNA, all of which have been demonstrated in aging microglia [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhilst some mechanisms driving neuroinflammaging are known, the extent to which this inflammation occurs in different CNS regions has not been adequately addressed, with the spinal cord (SC) being understudied. Furthermore, we do not know if the underlying mechanisms are similar for all CNS regions, given regions are differentially susceptible to some stimuli (e.g. amyloid pathology), and the kinetics of microglial aging appears to be region-dependent [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. If CNS health-span is to be improved, we must understand the causes of neuroinflammaging across the CNS.\u003c/p\u003e \u003cp\u003eIntermittent fasting (IF) is an effective anti-aging intervention, but its potential in countering neuroinflammaging is not well understood. IF can be implemented in mice by means of alternate day feeding (ADF) and this can be performed for the adult mouse lifespan [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It has been reported that ADF did not significantly affect the aging brain transcriptome, however the reported effect of normal aging was also modest (196 differentially expressed genes, DEGs, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.1) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Surprisingly however, ADF markedly reduced the incidence of neuronal inclusions in the thalamus [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], indicating beneficial CNS impacts in at least some cell types. Furthermore, a separate dietary intervention, a low-fat diet with calorie restriction, reduced white matter microglial activation in aging mice, suggesting dietary interventions can impact neuroinflammaging [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIf we are to improve CNS health-span, functionality in all regions needs to be maintained. To expand our understanding of CNS aging in general, and the effects of IF in particular, we performed RNA-sequencing on young, middle-aged, and old C57BL/6 mice to profile inflammaging in four CNS regions, the cortex (CTX), hippocampus (HIP), cerebellum (CB), and SC. We also performed sequencing on SC from middle-age and old IF animals to determine how this intervention influences neuroinflammaging.\u003c/p\u003e \u003cp\u003eWe found the SC was the most age-impacted CNS region, based on number of DEGs and its overall neuroinflammatory profile. Both SC and CB exhibited a mild inflammatory profile at middle-age that worsened by old age. HIP was pro-inflammatory at old age, while the CTX was largely unaffected. The old-age pro-inflammatory profile was similar to that exhibited by disease-associated microglia (DAM) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and by microglia displaying phagocytic exhaustion [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Notably, this profile involved upregulation of genes involved in viral-like immune responses, suggesting the presence of non-self, DNA/RNA. This response may be induced by depression of transposable elements, as we found hundreds of TEs to have increased expression in aging CNS, however release of mitochondrial DNA is also a potential contributor [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The neuroinflammaging phenotype in SC was partially rescued by IF, which also resulted in more TEs with reduced expression than with increased expression, an adaptation that we are proposing is a rebalancing of the transposonome.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eSpinal cord is an aging hotspot\u003c/h2\u003e\n \u003cp\u003eUsing deep RNA-sequencing we compared transcriptomes of CTX, HIP, CB, and SC, of young (3\u0026ndash;4 mos), middle-aged (12\u0026ndash;14 mos), and old mice (24\u0026thinsp;+\u0026thinsp;mos). Taking a conservative approach to DEG identification, we found CNS regions were differentially impacted by aging, with all regions having a greater number of DEGs in old age compared to middle-age (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea-b). In middle-aged animals, SC was the most impacted region with 510 DEGs, followed by CTX (463), CB (211), with HIP by far the least impacted region investigated with only 74 DEGs. A somewhat surprising result given the known functional sensitivity of this region to aging [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. Most middle-age DEGs were observed in a single CNS region, however\u0026thinsp;~\u0026thinsp;20% of SC DEGs were found in at least one other region (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec,e). Only 8 genes were differentially expressed in all regions by middle age, those being 9630013A20Rik (a lncRNA thought to be involved in oligodendrocyte maturation [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]), Abca8a (involved in mature oligodendrocyte stimulation of sphingomyelin and regulation of lipid metabolism [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]), collagen genes Col1a1 and Col1a2, Gpr17 (involved in oligodendrocyte progenitor cells [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]), Pcdhb9 (largely uncharacterised in brain), Tnc (involved in brain development [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] and neuro-immune functions [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]), and Zc3hav1 (involved in non-self DNA/RNA detection and STING-inflammation [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]). Middle-aged DEGs were approximately equally affected between increases and decreases in expression in all regions.\u003c/p\u003e\n \u003cp\u003eIn old animals, HIP was again the least impacted region investigated, with only 325 DEGs. CTX was the second least impacted region (799 DEGs), followed by CB (922 DEGs). SC was by far the most age-affected region with nearly three times the CB, with 2618 DEGs. In all CNS regions, there was a shift to more DEGs with increased expression in old animals, which was especially apparent in CB and SC. Whilst a relatively high number of DEGs were still region specific, we found that a greater proportion were found in multiple CNS regions, with 49 DEGs common to all regions investigated (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ed,f). Lists of DEGs for all regions are available in Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e, Supplementary Material Online. For DEGs identified separately by the three analysis programs, see Supplementary File S1, Supplementary Material Online.\u003c/p\u003e\n \u003cp\u003eWhile we found limited overlap between CNS regions for individual DEGs, we found greater consistency between regions for Gene Ontologies (GO) at both middle (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ee) and old ages (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ef). This increased GO overlap between CNS regions indicates aging impacts similar biological processes across the CNS, but through changes in expression of different genes between regions.\u003c/p\u003e\n \u003cp\u003eWe used \u003cem\u003eMetascape\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e] to determine and cluster regions by the top 100 GOs enriched in aging DEG sets (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Of the top 100 GOs at middle-age, 36 were related to immune system processes, such as immune cell activation, degranulation, antigen presentation, microglial phagocytosis, regulation of IL-1\u0026beta;, and pattern recognition receptor signalling, amongst others. Most of these ontologies were enriched in CB, SC, or both, suggesting age-related neuroinflammatory processes occur earlier in these regions. Other enriched ontologies were generally related to cell adhesion and the extracellular matrix (11), blood vessels (7), development (7), synaptic signalling (5), and lipids (4). Interestingly, response to axon injury was also enriched in CB and SC at middle-age.\u003c/p\u003e\n \u003cp\u003eSimilar to middle-age GOs, 37 of the top 100 old-age GOs were immune related, however, they were almost all enriched in HIP, CB, and SC (with higher significance), with some also enriched in CTX. Immune ontologies were similar to those at middle-age but also included more specific pathways such as tumour necrosis family (TNF) cytokine production, regulation of Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-\u0026kappa;B), response to type I and type II interferons, as well as endocytosis and phagocytosis. Other enriched ontologies were similar to those found at middle-age, including cell adhesion and the extracellular matrix (8), blood vessels (6), synaptic signalling (6), and lipids (2). Of particular note, the SC was enriched for 98 of the top 100 aging GOs, suggesting the aging SC is recapitulating molecular changes that occur across the CNS.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eInflammaging is rampant in the spinal cord\u003c/h3\u003e\n\u003cp\u003eGO analysis highlighted the enrichment of inflammation-related ontologies in our aging DEG gene lists, particularly in SC. To investigate these inflammatory mechanisms in greater detail, we compared our DEGs to GO and experimentally derived gene lists of interest. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows a summary of these enrichment analyses. (see also Supplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e, Supplementary Material Online for gene lists and Supplementary File S2, Supplementary Material Online for hypergeometric test results and overlapping genes).\u003c/p\u003e\n\u003cp\u003eIn the first instance, we compared our DEG lists to all genes in the positive regulation of inflammatory response GO (GO:0050729, 162 genes, Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea) and negative regulation of inflammatory response GO (GO:0050728, 171 genes, Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea). For positive regulation of inflammatory response, we found HIP, CB, and SC aging DEGs were enriched for this gene set (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). Note, aging was associated with both positive (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea) and negative (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb) regulation of an inflammatory response in CTX, but the relatively modest numbers of DEGs meant significant enrichment was not reached.\u003c/p\u003e\n\u003cp\u003eIn HIP, enrichment was only observed in old age group DEGs (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). Enriched genes included compliment protein C3 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb), caspase Casp1, chemokine Ccl3, cathepsin Ctss, Fc receptors Fcer1g, Fcgr1 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec), and Fcgr3, antigen presentation genes H2-D1 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ed), H2-K1 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ee), H2-Q4, H2-Q6, interleukin IL33, inflammasome component Pycard, a serine proteinase inhibitor Serpine1 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ef), toll-like receptors Tlr2 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eg) and Tlr4 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eh), and the microglial lipid receptor Trem2.\u003c/p\u003e\n\u003cp\u003eEnrichment was observed in CB and SC by middle-age (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). Middle-age genes were similar to HIP, old-age enriched DEGs with Ctss, Fcer1g, H2-D1, and Trem2 being found in both regions. Casp1, H2-K1, H2-Q4 were in CB only, whilst C3, Ccl3, Fcgr1, Fcgr3, and H2-Q6 were differentially expressed in SC only. In CB, Lgals1 and Snca were also differentially expressed whilst SC DEGs included Btk, Ldlr, Lpl, Plcg2, S100a8, and S100a9.\u003c/p\u003e\n\u003cp\u003eOld-age CB and SC DEGs were also enriched for genes involved in the positive regulation of inflammatory response (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). CB genes included many of those found in CB and SC middle-age DEGs, but included more histocompatibility locus genes, toll-like receptor Tlr6 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ei), inflammasome gene Gsdmd, genes involved in sensing non-self DNA/RNA (Ifi35 and Zbp1), and the TNF receptor Tnfrsf1a (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ej). The SC had similar DEGs to the CB, but also had IL1b and other inflammasome-related genes, cytokine Ccl5 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ek), proton-sensing G protein-coupled receptor Gpr4 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003el), and TNF (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003em), amongst others (see Supplementary File S2, Supplementary Material Online for all genes).\u003c/p\u003e\n\u003cp\u003eGenes involved in the negative regulation of the inflammatory response (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea), were only enriched in HIP, CB, and SC at old age (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb and Supplementary File S2, Supplementary Material Online). HIP DEGs included the hyaluronan receptor Cd44, cysteine protease inhibitor Cst7, Fc receptor Fcgr2b, the uracil nucleotide/cysteinyl leukotriene receptor Gpr17, growth factor Hgf (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb), immune-related GTPases Igtp and Irgm1, interleukin IL33, serine/threonine kinase Pbk, tyrosine phosphatase Ptpn6 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec), and Trem2. CB DEGs included most of the HIP genes, with the addition of lipoprotein Apoe, the cellular communication network factor Ccn3 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed), chemokine receptor Cx3cr1 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ee) and the interleukin receptor subunit IL20rb. The SC (~\u0026thinsp;2.5-fold enriched, 49 genes) contained almost all the genes enriched in HIP and CB at old-age, but also contained other anti-inflammatory genes like acyloxyacyl hydrolase (Aoah, Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ef), which limits inflammation in response to Gram negative bacteria, transmembrane glycoprotein Cd200r1 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eg), which limits inflammation after spinal cord injury, a hydrolase that metabolizes extracellular nucleotides, Enpp3 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eh), insulin-like growth factor Igf1 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ei), the immune-related GTPase Irgm2 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ej), Lrfn5 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ek), which supresses lipopolysaccharide induced CNS inflammation, Smad3 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003el), which inhibits inflammation-induced PPAR\u0026beta; expression and mediates anti-inflammatory macrophage transition, Sytll (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003em), which inhibits cytokine release and phagocytosis in primary microglia, the TNF-\u0026alpha; induced protein Tnfaip3 and the RNA-binding protein Zfp36, which downregulates pro-inflammatory cytokines like TNF-\u0026alpha;.\u003c/p\u003e\n\u003cp\u003eThese inflammation-related GO enrichments again demonstrate the greater impact aging has on SC, with the number of overlapping DEGs in GOs in this region always larger than other regions and in some cases more than double the number. Note, the lower magnitude fold-enrichment in SC simply reflects the much greater number of total DEGs in SC at both middle and old ages, compared to other regions.\u003c/p\u003e\n\u003cp\u003eOne important inflammatory mechanism is the interferon response. Interferon-mediated signalling (GO:0140888) genes were enriched by middle-age in CB and at old-age in HIP, CB, and SC (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec and Supplementary File S2, Supplementary Material Online). Most interferon signalling DEGs increased in expression with age (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea). At middle-age, key components of interferon signalling including; Ifih1 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb) which detects dsRNA, the interferon induced GTPases Igtp (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ec) and Irgm1 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ed), the transcription factor Irf7 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ee), immune intracellular signalling molecule Nlrc5, Oas genes Oas1a (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ef), which activates RNase L, and it\u0026apos;s inhibitor Oas1b (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eg), and Parp genes Parp9 and Parp14 that play opposing roles in macrophage activation, were all up-regulated, suggesting activation of interferon signalling and compensatory inhibition.\u003c/p\u003e\n\u003cp\u003eBy old age, more interferon signalling genes were differentially expressed. Those DEGs at middle-age in CB were also upregulated at old age, not only in CB but also in the HIP (excluding Oas1b and Parp9) and SC. The matrix metalloprotease Mmp12 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eh), and the Oas gene Oas2 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ei) were also upregulated in old HIP, CB and SC. Oas3 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ej), the dsDNA sensor, Zbp1, and the intracellular signalling molecule, Stat1, were all upregulated in old CB and SC, amongst others. Although interferon signalling genes were enriched in aging CNS, we did not find increased expression of interferon genes Ifna1, Ifnb1, or Ifng, but did find increased expression of the interferon receptor, Ifngr1 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ek), in old SC. Old SC also had decreased expression of anti-viral, Ifitm1 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003el), and increased expression of transcription factor Irf1, a general interferon response regulator (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003em) [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eWe also determined enrichment for another group of inflammatory signalling pathways, the interleukin pathways (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed and Supplementary File S2, Supplementary Material Online). We compiled a non-redundant gene list (111 genes) from interleukin-mediated signalling GOs (GO:0038154, GO:0035722, GO:0035772, GO:0035723, GO:0038173, GO:0097400, GO:0035655, GO:0070498, GO:0038155, GO:0070106, GO:0038110, GO:0038172, GO:0061514, GO:0038156, GO:0035771, GO:0038043, GO:0070102, GO:0038112, GO:0038113). Interleukin signalling genes were enriched in middle-age CTX, and old CB and SC (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e\n\u003cp\u003eInterleukin signalling DEGs in middle-age CTX were mostly decreased in expression (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea). These included signalling protein C1qtnf4, the IL1 receptor IL1r1 (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eb), the Janus kinase Jak3, and transcription factors Spi1 and St18. Only IL33 (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ec), which can be both pro- and anti-inflammatory, and Rps6ka5, which can induce transcription of anti-inflammatory IL10, were upregulated. Like interferon signalling, old-age interleukin signalling DEGs were characterised by increases in expression (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea). Eleven interleukin signalling DEGs were common to old age CB and SC. These included cytokine receptor subunit Csf2rb, IgE receptor Fcer1g, IL1a (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ed), Oas like genes Oasl1 (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ee) and Oasl2 (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ef) (although both also function in interferon signalling), Spi1, and intracellular signalling molecule Stat6. Four genes, Oas2, Parp14, Stat1, and Zbp1 were also in the interferon signalling list. Whilst IL1a was increased in both CB and SC, IL1b was only increased in SC. The SC also had increased expression of other interleukin signalling molecules such as caspase Casp4, cytokine receptor subunit Csf2rb2, interleukin IL33, interleukin receptors IL1rl1, IL17ra, IL12rb2, IL2rb, IL2rg, IL3ra (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eg), IL4ra (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eh), IL6ra, interleukin receptor associated kinases Irak2 (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ei), Irak3 (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ej) and Irak4, and interleukin receptor antagonist IL1rn (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ek), amongst others. Some interleukin-related genes including interleukin receptors IL2ra (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003el) and interleukin accessory protein IL1rap (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003em) had decreased expression.\u003c/p\u003e\n\u003cp\u003eAs microglia are a major resident potential inflammatory cell type in the CNS, we compared the aging DEGs to experimentally derived gene lists for disease associated microglia (DAMs, Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e), and microglial exhaustion (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e) (see Supplementary File S2, Supplementary Material Online for details). DAMs are a microglial phenotype that have downregulated \u0026ldquo;homeostatic\u0026rdquo; microglial genes, and upregulated phagocytic, lysosomal, and inflammatory genes [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. Microglial exhaustion refers to a state of phagocytic exhaustion, and microglia in this state produce excessive oxidative and pro-inflammatory molecules [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. There was minimal overlap between the 2 gene sets, with only 4 genes common to both.\u003c/p\u003e\n\u003cp\u003eThe aging transcriptome exhibited a profile indicating the presence of DAMs in all four CNS regions investigated, but again with much regional and age-related variability (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ea). Whilst significant CTX DAM DEGs were found, there were insufficient numbers to reach significant enrichment (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ee). However, HIP, CB, and SC were enriched for DAM-associated DEGs at either middle or old age (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ee). The HIP was enriched only in old age, whilst the CB and SC were enriched at both middle and old ages. At middle-age, 7 DAM DEGs were common to both CB and SC, and all increased in expression. These included the glycosylphosphatidylinositol (GPI)-anchored glycoprotein Cd52, the pattern recognition receptor Clec7a (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eb), H2-D1, the DNA sensor Ifi204, an inhibitor of NF-\u0026kappa;B activation, Lgals3bp, the lysozyme Lyz2 (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ec), and a signalling molecule downstream of immune receptors (e.g. Trem2) called Tyrobp (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ed). Other genes with increased expression by middle-age include MHC-I and anti-viral proteins (e.g. H2-K1, Ifit1, Nlrc5) in CB and chemokines (e.g. Ccl3 (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ee) and Ccl6), proteinases (e.g. Ctsz, Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ef), transmembrane proteins (e.g. Gpnmb, Itgax, Slamf9), lipid-related genes (e.g. Lpl), and the negative regulator of phagocytosis Cd22 (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eg) in SC. Expression of Cdca8, Dhcr7, Igf1, Enpp1, and Enpp2 were significantly decreased in middle-age SC.\u003c/p\u003e\n\u003cp\u003eApproximately 74% of DAM genes that were changed by middle-age were differentially expressed in HIP, CB, and SC by old-age. Other genes like Cst7 (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eh) were not increased at middle-age but were in old HIP, CB, and SC. Furthermore, 35 DAM genes were differentially expressed in 2/3 regions, including lipoprotein Apoe (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ei), transcription factor Atf3, receptor tyrosine kinase Axl, actin capping protein Capg, component of ferritin Ftl1, lysosomal enzyme Gusb, Spp1, and mitochondrial membrane protein Tspo, amongst others. The large majority had increased expression. The old SC had increased expression of many other DAM genes, including cytokine Ccl4 (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ej), microglial marker Cd63 (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ek), the cholesterol 25-hydroxylase gene Ch25h (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003el), and colony-stimulating factor 1 (Csf1, Fig. 8m).\u003c/p\u003e\n\u003cp\u003eIn addition to DAM-associated genes, we found enrichment of genes indicating microglial phagocytic exhaustion in our aging DEGs (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ef and Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ea). At middle-age, only the SC was enriched for exhaustion genes, but by old-age, the HIP, CB, and SC were all enriched.\u003c/p\u003e\n\u003cp\u003eIn middle-age SC, most exhaustion DEGs were increased. These included transcription factor Bcl6, Clec7a, immune receptor Havcr2 (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eb), immune checkpoint molecule Lag3, immune inhibitory receptor Pdcd1, cytokine-like calcium sensing molecule S100a8, and anti-viral signalling molecule Ticam2 (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ec).\u003c/p\u003e\n\u003cp\u003eIn old CNS, the HIP, CB, and SC all had increased expression of Clec7a, cytokine Cxcl10 (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ed), Havcr2, and Lag3. Interleukin IL33 and secreted phosphoprotein Spp1 (a.k.a osteopontin) both had increased expression in HIP and SC. Old CB and SC both had upregulated inhibitory immune receptor ligand Cd274, co-stimulatory receptors Cd72 (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ee) and Cd86 (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ef), and Stat1. Old SC also had upregulated advanced glycosylation end-product specific receptor Ager (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eg), chemokine Ccl2 (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eh), IL-2 receptor IL2rb (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ei), purine receptor P2rx7 (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ej), and Ticam1 (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ek) which mediates TLR3\u0026ndash;dependent production of IFN-\u0026beta;. Although most exhaustion DEGs were increased in aging CNS, the transmembrane glycoprotein Cd38 (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003el), which links cell activation and survival, was downregulated in aging SC as was chemokine receptor Ccr2 (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003em).\u003c/p\u003e\n\u003ch3\u003eActivation of Aim2, NLRP1, and NLRP3 inflammasomes\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eActivation of Aim2, NLRP1, and NLRP3 inflammasomes\u003c/div\u003e\n\u003cp\u003eInflammasomes are key intracellular multimeric protein complexes that initiate inflammatory mechanisms in response to activation of pattern recognition receptors. Inflammasomes activate inflammatory caspases in an inflammasome specific manner. Following activation, these caspases cleave pro-Il1\u0026beta; and/or pro-IL18, activating and releasing cytokines. These inflammasomes are activated by specific signals and involve characteristic signalling pathways dependent on the type of inflammasome and initiating danger signal. As our aging transcriptomes indicate widespread inflammation, we profiled which inflammasome signalling pathways were induced during aging in each CNS region (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003ea and Supplementary File S2, Supplementary Material Online).\u003c/p\u003e\n\u003cp\u003eInflammasome genes were not significantly enriched at middle age in any CNS region, nor in CTX at old age (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eg). At old age, DEGs in HIP, CB, and SC were enriched for inflammasome genes. Caspase-1 (Casp1, Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eb) was significantly increased in CTX and CB by middle-age, and in all regions at old-age. Caspase-4 (Casp4, Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003ec) was only significantly increased in old SC, as were 3 of the Nlrp genes, Nlrp1a (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003ed), Nlrp1b (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003ee), and Nlrp3 (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003ef), and the Aim2 (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eg) pattern recognition receptor which binds dsDNA.\u003c/p\u003e\n\u003cp\u003eNlrc4 inflammasome components Naip2 (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eh) and Naip5 (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003ei) were both increased at old age in HIP, CB, and SC, and at middle-age in SC. Naip2 was also increased in middle age CB. Naip6 (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003ej) was also increased in old CB and SC. Pycard (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003ek), a core component of inflammasomes which contains the caspase recruitment domain, was also increased in old HIP, CB, and SC. Importantly, the pore-forming Gsdmd (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003el), which allows release of IL1\u0026beta; and IL18, was increased in old CB and SC. Interestingly, only the SC had a significant increase in IL1b (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003em), one of the main cytokines activated by inflammasomes. The other cytokine, IL18, was not changed with age.\u003c/p\u003e\n\u003ch3\u003eIncreased expression of non-self, DNA/RNA sensors and cGAS-STING in aging CNS\u003c/h3\u003e\n\u003cp\u003eProfiling of inflammasome molecules found increased expression of cytoplasmic DNA and non-self RNA activated inflammasomes.\u003c/p\u003e\n\u003cp\u003eBased on the above, we compared our aging-DEGs to genes that are differentially expressed in conditions of cytoplasmic DNA, Tfam\u003csup\u003e+/\u0026minus;\u003c/sup\u003e mice [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e] and LINE1 derepression in senescent cells [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e] (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003ea and Supplementary File S2, Supplementary Material Online). These are models of mitochondrial DNA (mtDNA) release and nuclear genome transposable element (TE) derepression and release, respectively.\u003c/p\u003e\n\u003cp\u003eGenes from the Tfam\u003csup\u003e+/\u0026minus;\u003c/sup\u003e context were enriched in middle-age CB and all regions in old age (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eh). Genes in middle-age CB were all increased in expression. Genes included the interferon induced transmembrane protein Bst2 (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eb) which inhibits enveloped virus release, the E3 ubiquitin-protein ligase Dtx3l which is involved in a positive feedback loop for interferon gene expression, the inflammasome modulators Gbp3 and Gbp7, genes involved in detection of cytoplasmic dsDNA (e.g. Ifih1) and viral RNA (e.g. Ifit1 (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003ec), Ifit3 (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003ed), and Ifit3b), galectin 3 binding protein Lgals3bp, the immunoproteasome subunit Psmb8, E3 ubiquitin ligase and cellular sensor of ISGylated proteins Rnf213, Rtp4 which can inhibit the IFN-I response, negative regulator of STING Trim30a, Trim30d, and Usp18 which reduces cGAS degradation. The others (H2-Q4, Igtp, Irf7, Irgm1, Oas1a, Oasl2, Parp14, Parp9) have been described above.\u003c/p\u003e\n\u003cp\u003eTfam\u003csup\u003e+/\u0026minus;\u003c/sup\u003e genes were enriched in old CTX, HIP, CB, and SC. Several genes had increased expression in all old CNS regions. These included H2-Q4, Ifit3, Igtp, Lgals3bp, Rnf213, and Xaf1 (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003ee), the latter of which forms a positive feedback loop with IRF-1. Furthermore, almost all genes that were increased in middle-age CB (excluding Gbp7, Parp9, Rtp4) were increased in old HIP, CB, and SC. Cxcl10 and Xaf1 were increased in old HIP, CB, and SC, but not in middle-age. Expression of other genes increased in multiple old regions were common to the CB and SC. These included Dhx58, a regulator of RIG-I and MDA5 signalling (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003ef), inflammasome modulator, Gbp2 (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eg), negative regulator of RIG-I signalling, Ifi35 (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eh), transcription factor, IRF9, Irgm2, Slfn2, which reduces type I IFN-induced activation of NF-\u0026kappa;B signalling, Stat1 (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003ei), and cytoplasmic dsDNA/dsRNA sensor, Zbp1.\u003c/p\u003e\n\u003cp\u003eGenes in the LINE1 derepression context were enriched in middle-age CB and SC (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ei). Only Ifi204 was significantly increased in both CNS regions at middle-age. The CB also had increased expression of Bst2, the interferon inducible protein, Ifi27 (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003ej), which is involved in type-I interferon-induced apoptosis and can inhibit RIG-I signalling, Ifih1, interferon induced proteins with tetratricopeptide repeats (Ifit1, Ifit2 (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003ek), Ifit3) that inhibit translation and modulate apoptosis, and the GTPase, Mx1. The SC had increased lysosomal thiol reductase Ifi30 (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003el), transcription factor Irf5, and reduced expression of the IFN-\u0026gamma; and toll-like receptor adapter protein Mal.\u003c/p\u003e\n\u003cp\u003eAt old age, the HIP, CB, and SC were all enriched for genes in the LINE1 derepression context. Both Ifi27 and Ifit3 were increased in old CTX, HIP, CB, and SC. Seven other genes were found in HIP, CB, and SC. These included Bst2, Cxcl10, Ifi204, Ifih1, Ifit1, Mx1, and Oas2. Old CB and SC both had increased Ifit2, Ifitm3 (which stops viruses that enter the cell in endosomes from entering the cytoplasm and negatively regulates type I IFN signalling by enhancing the autophagic degradation of IRF3), Irf5, and Stat1. Mal was once again decreased. Expression of the intracellular signalling molecule Stat3 (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003em) was also increased in old SC.\u003c/p\u003e\n\u003cp\u003eAs aging CNS was enriched for genes induced by cytoplasmic DNA (or dsRNA, DNA:RNA hybrids), we examined expression of cGAS-STING pathway components in our aging transcriptomes (Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003ea and Supplementary File S2, Supplementary Material Online). Although the CTX, HIP, CB, and SC were all enriched for Tfam\u003csup\u003e+/\u0026minus;\u003c/sup\u003e and/or LINE1 derepression-related genes, only the old SC was enriched for cGAS-STING pathway components (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ej), while other regions (that did not have increased IL-1\u0026beta;) were not. Two components of the cGAS-STING components were changed by middle-age, Ifi204 (Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003eb) was increased in CB and SC, and Zdhhc9 (Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003ec) was decreased in SC. By old-age, Ifi204 was also increased in HIP, whilst Irf9 (Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003ed) and Zbp1 (Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003ee) were both increased in CB and SC. The old SC also contained increased expression of the DNA sensor, Cgas (Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003ef), transcription factor Nfkb1 (Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003eg), Sting1 (Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003eh), which is activated by cGAS, negative regulator of cGAS by palmitoylation, Zdhhc18 (Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003ei), and decreased Zdhhc9, which positively regulates cGAS by palmitoylation. Furthermore, Zc3hav1, a viral DNA/RNA detector and STING-inflammation enhancer [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e] was increased in all CNS regions at both middle and old ages (Fig. \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eIncreased expression of hundreds of transposable elements in aging spinal cord\u003c/h3\u003e\n\u003cp\u003eOne possible activator of the cGAS-STING pathway is derepression of TEs, resulting in formation of cytosolic DNA:RNA hybrids, cytosolic cDNA, and/or dsRNA [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. We therefore tested whether TEs were differentially expressed in aging CNS. We examined expression of TE families, as well as expression of individual elements. We found total expression of TE classes did not differ with age in any CNS region (Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003ea), nor did expression of the main TE families (Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003eb). However, we did find significant differential expression (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) of individual TEs (DE-TEs), with most being increased (Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003ec and Supplementary Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e, Supplementary Material Online). We found most DE-TEs were region specific, although some overlap between regions occurred (Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003ed-e). In SC, more than half the DE-TEs at middle-age were also differentially expressed at old-age, and always in the same direction (Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003ef).\u003c/p\u003e\n\u003cp\u003eAging impacted TE expression, particularly in SC, so to evaluate processes potentially involved, we curated a gene set (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e, Supplementary Material Online) covering the many layers of TE regulation [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. The 141-gene list represents processes of chromatin and histone modification, DNA and RNA methylation, and piRNA and siRNA pathways that collectively regulate TE expression. Given the number of TE expression changes, there were surprisingly few changes in expression of TE regulatory genes (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In CTX, with the exception of zinc finger protein genes (Zfp, see below), there were no TE regulation DEGs at either middle or old age (for complete analyses see Supplementary File S2, Supplementary Material Online). In HIP, Uhrf1 was a TE regulation DEG, with ~\u0026thinsp;50% reductions in expression (based on DESeq2 analyses), at both ages. As a repressor of TE expression, the aging-related reduction in Uhrf1 may contribute to HIP TE expression (Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e) [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. In CB there were no TE regulation DEGs at either age.\u003c/p\u003e\n\u003cp\u003eIn SC, expression of 4 TE regulation DEGs was increased at old age, with no changes at middle age. Ehmt2 (previously known as G9a) catalyses H3K9 mono- and di-methylation, maintains global DNA methylation, and regulates the 3D genome [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]. Methyl-CpG-binding-domain protein, Mbd6, recognises chromatin-associated retrotransposon RNA that has been methylated (m\u003csup\u003e5\u003c/sup\u003eC), promoting an open chromatin state [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]. That Mbd6 preferentially recognises m\u003csup\u003e5\u003c/sup\u003eC in repeat RNA may explain its increased expression with aging when there is increased expression of retrotransposons (Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e). Tet1 is a ten-eleven translocation enzyme that oxidises m\u003csup\u003e5\u003c/sup\u003eC to cause DNA demethylation, although it is also thought to have TE repressive actions, with Tet1 binding and resultant action being dependent on underlying epigenetic profiles [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]. DNA ligase 1, Lig1, is methylated by Ehmt2, which results in Uhrf1 recruitment for DNA methylation maintenance [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]. These TE regulation gene expression changes in old SC possibly reflect a compensatory, albeit unsuccessful, attempt to restore TE repression. The substantial increase in DE-TEs between middle and old ages is consistent with this possibility.\u003c/p\u003e\n\u003cp\u003eH3K9me2/3 histones are critical for heterochromatin establishment and maintenance, in particular for repeat elements and lineage-specific genes [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. We used a 99-protein subset (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e, Supplementary Material Online) of a recently characterised H3K9me3 proteome [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e] to probe our DEGs for potential impacts of aging on this critical heterochromatin protein complex.\u003c/p\u003e\n\u003cp\u003eAs with TE regulation DEGs, there was little effect of aging on H3K9me3 proteome gene expression across the CNS (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), with the exception of one gene in CB and 5 genes in SC at old age. Crebrf expression was increased in CB of old mice, but the role of this transcription factor in the H3K9me3 proteome is not known. Crebrf has been shown to be involved in metabolic phenotype switching in muscle [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e] and therefore may be involved in the H3K9me3 complex of facultative heterochromatin that is involved in changing cell fate, differentiation status, or phenotype [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. In SC expression of two lysine oxidases (Loxl1, Loxl2) was impacted by aging. Through histone interactions, Loxl proteins can affect chromatin compaction state, and Loxl2 oxidises H3K4me3, a histone mark associated with transcription, to remove the me3 group, thereby acting as a repressor [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]. What the nuclear receptor co-activator 7 (Ncoa7) protein does in the H3K9me9 proteome is not known, but it has been implicated in defence against oxidative stress, cancer, and viruses [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]. Polypyrimidine tract-binding protein 1 (Ptbp1) is a heteronuclear ribonucleoprotein with a prominent role in alternative splicing [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e]. A H3K9me3 related role has not been reported. Rela is a subunit of the transcription factor NF-kB that regulates inflammatory responses. Rela- NF-kB is primarily localised in the cytoplasm under basal conditions, although some Rela is bound to chromatin constitutively. Mono-methylation of Rela by a non-histone methyltransferase, results in Ehmt1-mediated chromatin silencing, thereby attenuating NF-kB [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]. Increased Rela expression in old SC may indicate another mechanism initiated to silence derepressed heterochromatin.\u003c/p\u003e\n\u003cp\u003eDE-TEs were evident across the CNS by middle age, but markedly more so at old age, with increased TE expression much more common than decreased expression in all regions (Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e). TE derepression, as opposed to repression, therefore, is a major aging-related CNS TE phenomenon. Somewhat surprisingly, there was relatively modest change in expression of genes encoding proteins involved in TE regulation and the H3K9me3 proteome. However, we noticed a number of Zfp genes in our DEGs for all regions at both middle and old ages, although again there were generally many more Zfps in the old DEG lists and especially in SC. Zfps constitute the largest transcription factor family in mammals and are thought to have an important role in TE repression [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]. Many Zfp genes encode the Kr\u0026uuml;ppel-associated box (KRAB), a potent repressor of transcription [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e], called KRAB domain-containing zinc-finger proteins (KZfps). KZfp genes are organised into clusters in the genome, with cluster members being evolutionary-related and transcriptionally co-regulated [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]. KZfp regulation is thought to be cluster related, that is, KZfp tissue or cell-type-specific expression profiles are similar for cluster members. Approximately 85% of mouse and human KZfp genes belong to clusters, and cluster members have similar DNA binding fingerprints [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]. KZfps bind to TEs (and other genomic elements) to repress their transcription [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]. Therefore, we determined whether KZfp aging DEGs were members of the same clusters, using previously described criteria; clusters must be of two or more KZfp genes that are not spaced more than 500 kilobases apart [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e]. We used our \u003cem\u003eDESeq2\u003c/em\u003e analyses to capture the broadest range of KZfp DEGs (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThere was a general trend showing the CNS regions with more aging-related DE-TEs (Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e) also had a greater number of KZfp DEGs (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) with good correlations between the numbers of differentially expressed TEs and Zfps at both middle (r\u0026thinsp;=\u0026thinsp;0.85) and old ages (r\u0026thinsp;=\u0026thinsp;0.99).\u003c/p\u003e\n\u003cp\u003eWe found that DE KZfps were generally not related by cluster (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), except for the CB. In CTX at middle age 2 clusters on Chr7 had multiple KZfp genes with decreased expression (Zfp428, Zfp575; Zfp579, Zfp580) while no clusters had multiple DE KZfps at old age. No clusters contained multiple HIP DEGs at either age. CB middle age DEGs contained KZfps in the same cluster (4930522L14Rik, Zfp1007 on Chr5). At old age, 5 clusters contained multiple CB DEGs (Zfp937, Zfp442 on Chr2; 4930522L14Rik, Zfp1007 on Chr5; Zfp937 and Zfp442 on Chr1; 4/5 cluster KZfps Gm3604, Zfp1008, Zfp808, Zfp934 on Chr13; Zfp960, Zfp97 on Chr17). All cluster CB KZfps were increased.\u003c/p\u003e\n\u003cp\u003eSC DEGs also contained KZfps from the same cluster at old age (Gm14419, Gm14305 on Chr2; Zfp28, Zfp667 on Chr7; 3/5 cluster KZfps Zfp26, Zfp266, Zfp846 on Chr9; Zfp811, Zfp871 on Chr17). Where multiple DEG KZfps were in the same cluster, DEGs had the same direction of expression change, except in old SC where 2 clusters (Chr2 and Chr7) had KZfps with different directions, possibly indicating loss of cluster co-transcriptional regulation. Interestingly, while the CB contained greater expression of KZfps at old age, most SC KZfp DEGs were decreased in expression.\u003c/p\u003e\n\u003cp\u003eNo KZfp DEG was common to all comparisons, although Zfp57 (Fig. \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003ea) was consistently decreased in expression in all but CTX middle age. Zfp57 is involved in the acquisition and maintenance of methylation-dependent gene imprinting in neural development [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e], as well as the repression of TEs and nonimprinted genes [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e]. Zfp419 (Fig. \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003eb) was a DEG in 5 comparisons, being consistently increased in expression, except in CTX (both ages) and CB (YvM) where it was not reliably detected. Zfp268 (increased, Fig. \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003ec), Zfp518a (decreased, Fig. \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003ed), and Zfp831 (CTX decreased, SC increased) were DEGs in 3 of the 8 comparisons. With the exception of Zfp831 and Zfp703, all KZfps that were DEGs in more than one comparison, showed the same direction of expression change. Other than as general repressors of TE expression, little is known about most individual KZfps.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdult lifelong intermittent fasting (IF) attenuates expression of microglial inflammatory markers, inflammasomes, non-self DNA/RNA sensors, and re-balances the transposonome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs the SC was generally the most age-affected CNS region, we investigated whether IF, a dietary intervention that increases both lifespan and health-span in rodents, attenuates SC inflammaging (see Supplementary Table \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003e, Supplementary Material Online for consensus DEG lists and Supplementary Files S1-2, Supplementary Material Online for all DEGs lists and hypergeometric tests). We found IF had a modest effect on gene expression in middle-aged animals, with 212 DEGs (M v M-IF), with 39 of these being age-affected genes (~\u0026thinsp;8% of the 510 middle-age-related DEGs, i.e .Y v M) (Fig. \u003cspan class=\"InternalRef\"\u003e16\u003c/span\u003ea). However, IF had a profound effect in old animals, with an ~\u0026thinsp;11-fold increase in the number of DEGs compared to middle-age IF (2351 DEGs, O v O-IF). Furthermore, IF significantly impacted\u0026thinsp;~\u0026thinsp;26% of the old age-related (676 of 2618, Y v O) DEGs. The DEGs were highly enriched for immune-related ontologies (Fig. \u003cspan class=\"InternalRef\"\u003e16\u003c/span\u003eb), suggesting IF modifies inflammaging. These data demonstrate that IF significantly affects expression of aging and non-aging-related genes.\u003c/p\u003e\n\u003cp\u003eWe found that IF reduced expression of a number of key genes involved in neuro-inflammaging processes, DAM (~\u0026thinsp;3-fold, 52/162, corrected p\u0026thinsp;=\u0026thinsp;1.49x10\u003csup\u003e\u0026minus;\u0026thinsp;12\u003c/sup\u003e), microglial exhaustion (~\u0026thinsp;3-fold, 14/40, corrected p\u0026thinsp;=\u0026thinsp;0.0004), Tfam knockout (~\u0026thinsp;4-fold, 23/50, corrected p\u0026thinsp;=\u0026thinsp;2.81x10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e) and LINE1 derepression (~\u0026thinsp;3-fold, 8/25, corrected p\u0026thinsp;=\u0026thinsp;0.035) genes enriched in IF old-age DEGs.\u003c/p\u003e\n\u003cp\u003eOf the 68 DAM genes that were differentially expressed in old SC, 43 were significantly affected by IF. Of these, ~\u0026thinsp;90% (38 genes) that showed increased in expression in old age, were universally reduced by IF, with an average\u0026thinsp;~\u0026thinsp;40% reduction in expression (Fig. \u003cspan class=\"InternalRef\"\u003e16\u003c/span\u003ec). These genes included pattern recognition receptors Clec7a and Ifi204, lipid and inflammation-related genes Apoe, Ch25h, Lpl, cysteine protease inhibitor Cst7, lysosomal genes Ctsz and Lyz2, negative regulator of phagocytosis Cd22, transmembrane proteins Gpnmb and Tyrobp, and proton-sensing G protein-coupled receptor Gpr65.\u003c/p\u003e\n\u003cp\u003eWe also found significant reduction in expression of microglial exhaustion genes. Of the 28 exhaustion genes that were differentially expressed in old SC, ~\u0026thinsp;40% (12 genes) were affected by IF (Fig. \u003cspan class=\"InternalRef\"\u003e16\u003c/span\u003ed). Expression of 11 of these genes that was increased with aging, was reduced by IF. These included cytokines and chemokines Ccl2, Cxcl10, IL33, and Spp1, immune inhibitory receptor ligand Cd274 and its receptor Pdcd1, co-stimulatory receptors Cd72 and Cd86, Clec7a, transmembrane enzyme Entpd1 which converts extracellular ATP to ADP, and Socs3 which inhibits pro-inflammatory M2 polarisation pathways. A number of other microglial exhaustion genes (e.g. Cd244a) were non-significantly decreased. Contrastingly, expression of Bcl6 was increased with age, and more so with IF.\u003c/p\u003e\n\u003cp\u003eIF also reduced expression of many aging DEGs associated with the Tfam\u003csup\u003e+/\u0026minus;\u003c/sup\u003e and LINE1 derepression contexts (Fig. \u003cspan class=\"InternalRef\"\u003e16\u003c/span\u003ee). Of the 34 old-age DEGs from Tfam\u003csup\u003e+/\u0026minus;\u003c/sup\u003e genes, ~\u0026thinsp;55% (19 genes) were affected by IF. We found reduced expression of non-self, DNA/RNA sensors Ifih1, Ifit3, Ifit3b, Dhx58, and Zbp1, and Bst2 which can inhibit production of IFN and proinflammatory cytokines, as well as cytokines Ccl2 and Cxcl10, which modulate Rigi (Ddx58) and Mda5 (Ifih1) signalling in response to viral nucleic acids, inflammasome modulators Gbp2 and Gbp3, transcription factor Irf7, inhibitor of NF-\u0026kappa;B activation Lgals3bp, Oas1a which is involved in activation of RNase L to degrade viral RNA, Oasl2 which enhances RIGI signalling, Parp9 which is involved in pro-inflammatory macrophage activation, immunoproteasome component Psmb8 which is involved in microglial mediated neuroinflammation, and genes that regulate the response to viral infection Trim30a and Trim30d. The lysophospholipase D, Enpp2, was reduced in both aging, and further with IF.\u003c/p\u003e\n\u003cp\u003eWith the LINE1 derepression genes, ~\u0026thinsp;40% of aging DEGs (7 of 17) were affected by IF. Bst2, Cxcl10, Ifih1, and Ifit3 also appear in the Tfam\u003csup\u003e+/\u0026minus;\u003c/sup\u003e context. Expression of the DNA sensors Ifi204, Ifit2 (which binds viral ssRNA), and Ifitm3 (which blocks viral membrane fusion and cytoplasmic entry) were all reduced by IF. Expression of Rigi/Ddx58), which is a pattern recognition receptor for viral nucleic acids (dsRNA), was non-significantly increased in old, but returned to young levels in the IF group.\u003c/p\u003e\n\u003cp\u003eExpression of key inflammasome (Fig. \u003cspan class=\"InternalRef\"\u003e16\u003c/span\u003ef) and cGAS-STING (Fig. \u003cspan class=\"InternalRef\"\u003e16\u003c/span\u003eg) pathway genes was also decreased by IF in old animals. The non-canonical inflammasome Casp4, pore forming Gsdmd, Nlrc4 inflammasome components Naip2 and Naip5, and Nlrp1a and Nlrp1b all had reduced expression with IF. IL1b was non-significantly decreased. In the cGAS-STING pathway, downregulation occurred in non-self, DNA/RNA sensing molecules Ifi204 and Zbp1, the phosphatase Ppp6c, a negative regulator of cGAS, and Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e which is required for Tak1 activation of STING. Interestingly, a number of genes changed more in the IF groups than aging counterparts. For example, expression of Cgas, Irf9, and Zdhhc18 was increased in old, and further increased with IF. Zdhhc9 expression was decreased in old and further decreased with IF. Sting1 was non-significantly reduced by IF.\u003c/p\u003e\n\u003cp\u003eHeatmaps of IF comparisons for the DAM, microglial exhaustion, inflammasome, and cGAS-STING gene lists, as well as heatmaps for the other gene lists, are available in Supplementary Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e-9, Supplementary Material Online.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eIntermittent fasting drives differential expression of transposable elements\u003c/h2\u003e\n \u003cp\u003eWe did not find any obvious global changes in the expression of repeat classes (Fig. \u003cspan class=\"InternalRef\"\u003e17\u003c/span\u003ea) or the major repeat families (Fig. \u003cspan class=\"InternalRef\"\u003e17\u003c/span\u003eb) with IF. While expression of some repeat elements was differentially expressed with age, and more-so at old-age, there was an unexpected finding with IF. The number of DE-TEs between young and IF groups was ~\u0026thinsp;5-6-fold greater than normal aging counterparts (244 Y v M, 1270 Y v M-IF, 1343 Y v O, 7906 Y v O-IF, Fig. \u003cspan class=\"InternalRef\"\u003e17\u003c/span\u003ec and Supplementary Table \u003cspan class=\"InternalRef\"\u003eS5\u003c/span\u003e, Supplementary Material Online). However, while aging generally increased expression of DE-TEs, IF resulted in a greater proportion of TEs having significantly decreased expression compared to young animals, especially at old-age (ratio TE increase:TE decrease; Y v M\u0026thinsp;~\u0026thinsp;2.5:1; Y v M-IF\u0026thinsp;~\u0026thinsp;1:1.5; Y v O\u0026thinsp;~\u0026thinsp;3:1; Y v O-IF\u0026thinsp;~\u0026thinsp;1:2.25). IF also resulted in a greater proportion of TEs with significantly decreased expression compared to age matched controls (MvM-IF\u0026thinsp;~\u0026thinsp;1:1.15, OvO-IF\u0026thinsp;~\u0026thinsp;1:1.73). We also found a more general increase in expression of TEs in middle age and old animals compared to young (Fig. \u003cspan class=\"InternalRef\"\u003e17\u003c/span\u003ed), which was reversed in IF animals. Reduced expression of TEs was also found in IF animals compared to age-matched controls (Fig. \u003cspan class=\"InternalRef\"\u003e17\u003c/span\u003ee).\u003c/p\u003e\n \u003cp\u003eAs was carried out for aging, the IF DEGs (M v M-IF and O v O-IF) were compared to a 141-gene TE regulation gene set. In contrast to the Y v M comparison, where there were no TE regulation DEGs, two DEGs were increased in expression in M v M-IF (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Suv39h1 trimethylates H3k9 to form heterochromatin to repress TEs [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e]. Yy1 is a transcription factor that activates transcription of the mouse Tf subfamily of L1 in early development [\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e]. However, whether Yy1 activates or represses transcription is cell type and locus-dependent [\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e] and in differentiated cells such as neurons, it is a repressor of L1 transcription [\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e]. The increase in Yy1 expression seen at middle and old age in IF animals is, therefore, predicted to result in L1 repression.\u003c/p\u003e\n \u003cp\u003eIn the O v O-IF comparison, there were 26 TE regulation DEGs, an effect that constituted a significant 2.3-fold enrichment (p\u0026thinsp;=\u0026thinsp;4.7x10\u003csup\u003e5\u003c/sup\u003e). With the exception of H3f3b and Pbrm1 that decreased expression, the other 24 DEGs all increased expression with IF. Pbrm1, Arid2, and Dpf2 are part of the chromatin remodelling complex, BRM-associated factors (BAF), which regulates epigenetic modifications and chromatin accessibility that are important for CNS function [\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e]. Ehmt1, Ehmt2, Kmt5c, Setdb1, and Suv39h1 are all histone methyltransferases, and Dnmt3a is a DNA methyltransferase. Alkbh5 is a RNA demethylase that \u0026ldquo;erases\u0026rdquo; methylation (m6A) in RNA, an RNA modification involved in TE repression [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. Brd4 and Dgcr8 are members of RNA interference (RNAi) mechanisms now appreciated to silence certain repeat elements [\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eChd5 (chromodomain/helicase/DNA-binding domain 5) is part of the nucleosome-remodelling and deacetylase (NuRD) complex, that is expressed in neurons and important for establishing and maintaining neuronal cell fate through the co-repression of non-fate-specific and co-activation of fate-specific genes [\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e]. Hinfp (histone H4 transcription factor) is a zinc finger transcriptional regulator that silences TEs in somatic cells. Loss of Hinfp results in increased expression of most TEs and enhanced aging-related phenotypes [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e]. Safb (scaffold attachment factor B) encodes proteins that protect somatic cell genomes by preventing retrotransposition of transcribed intronic L1s and ERVs through a mechanism that recognises TE-related biased RNA coding sequence to retain TE transcripts in the nucleus [\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eSltm (Safb-like transcription modulator) is one of three Scabf-related genes involved in prevention of TE retrotransposition [\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e]. Sinhcaf (SIN3-HDAC complex associated factor) is a member of the SIN3 histone deacetylase complex that is recruited to repress L1 and ERV transcriprion [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e]. Tnrc18 (trinucleotide repeat containing 18; previously known as Zfp46/469) is an H3K9me3-specific reader that recruits co-repressors such as Sin3-Hdac complex to repress ERVs [\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e]. Zcchc8 (zinc finger, CCHC domain containing 8), Zfc3h1 (zinc finger, C3H1-type containing), Pabpn1 (poly(A) binding protein, nuclear 1), and Zc3h3 (zinc finger CCCH type containing 3) are members of the NEXT (nuclear exosome targeting) and PAXT (polyA tail exosome targeting) complexes that are important for nuclear RNA degradation [\u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eNEXT has recently been shown to cooperate with human silencing hub (HUSH) complexes to degrade TE RNAs [\u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e]. Spen (spen family transcription repressor), a RNA-binding protein essential for X chromosome inactivation, also represses ERV by binding to ERV transcripts and recruiting chromatin-silencing proteins [\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e]. Sirt1 (sirtuin 1) is a lysine deacetylase with pleiotropic effects in the cell. Pharmacological activation of Sirt1 re-established heterochromatin and re-repression of retrotransposons in aging cells [\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e]. Overall, IF impacts most if not all levels of TE regulation.\u003c/p\u003e\n \u003cp\u003eComparing IF DEGs with the H3K9me3 proteome list, there was a single overlapping gene at middle age (increased expression of the histone methyltransferase gene Suv39h1, Fig. \u003cspan class=\"InternalRef\"\u003e18\u003c/span\u003e) but 14 overlapping genes at old age in the IF SC, which was a significant enrichment (1.77-fold over-enriched, p\u0026thinsp;=\u0026thinsp;0.025). Except for decreased expression of the Loxl genes and Myef2, expression of all other genes was increased in IF compared to control, ad libitum-fed groups (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eMyelin expression factor 2 (Myef2) is an RNA-binding protein involved in regulating oligodendrocyte differentiation and myelination of the CNS [\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e]. Myef2 has also been implicated in cancer and atherosclerosis [\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e], but its role in the H3K9me3 proteome is not known.\u003c/p\u003e\n \u003cp\u003ePR-domain-containing, zinc finger proteins (PRDMs) are chromatin factors that regulate transcription and chromatin structure, for example by repressing genes involved in DNA methylation. Prdm15 maintains stem cell pluripotency, binding to both euchromatin and heterochromatin to activate or repress transcription in a context specific way [\u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e]. A specific role in H3K9me3 has not been reported.\u003c/p\u003e\n \u003cp\u003eRNA-binding motif proteins (RBMs), such as Rbm6, are involved in splicing, DNA damage repair [\u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e], and tumour suppression [\u003cspan class=\"CitationRef\"\u003e79\u003c/span\u003e]. As with other proteins in the H3K9me3 proteome, roles for Rbm6 and Rbm12b1 have not been reported for this repressive histone modification, but the RNA binding ability of these proteins may function in RNA-related aspects of heterochromatin formation and maintenance [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eSplicing factor proline- and glutamine-rich (Sfpq) is a RNA-binding protein with multiple roles in splicing, regulation of transcription, DNA damage repair, genome stability, and paraspeckle formation in response to cell stressors, such as viral invasion [\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e]. Interestingly, Sfpq was shown to repress Rela (see above) expression, thereby muting the innate immune system\u0026rsquo;s interferon response to virus. This was removed by a long non-coding RNA that competed with Sfpq\u0026rsquo;s repressive binding to Rela [\u003cspan class=\"CitationRef\"\u003e82\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eTransformer 2 alpha homolog (Tra2a), another RNA-binding protein that is a serine/arginine-rich mRNA splicing factor, dysregulation of which has been implicated in a number of cancers [\u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e]. Tra2a is also thought to be a trans-acting RNA methylator [\u003cspan class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e85\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eOverall, in the aging SC, IF appears to modify the H3K9me3 proteome resulting in changes in expression of histone methyltransferase genes (Setdb1, Suv39h1), RNA-binding and modifying genes (Myef2, Rbm121, Rbm6, Tra2a), genes involved in chromatin structure (Prdm15, Loxl1, Loxl2), and genes involved in prevention of transposition of already transcribed TE RNA (Safb, Sltm).\u003c/p\u003e\n \u003cp\u003eAdult life-long IF was associated with differential expression of KZfps when comparing control ad libitum-fed animals with IF animals of the same age. While there were only 4 KZfp DEGs at middle age (M v M-IF), there were 63 KZfp DEGs at old age (O v O-IF) (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), far more than the 40 KZfp DEGs that resulted from aging to old age alone (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The majority (2/4 at M, 39/63 at O) of IF KZfp DEGs had decreased expression. Only 6 KZfps that were DEGs with aging (Zfp40, Zfp41, Zfp407, Zfp419, Zfp518a, Zfp57) were also DEGs with IF (asterisked KZfps in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), and 2 of these showed the opposite direction of expression change (italicised KZfps, Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). None of the 4 middle age KZfps were from the same cluster. In old age IF, 7 clusters had multiple KZfps DE. Most clusters were in the same direction (Zfp979, Zfp982 on Chr4 \u0026ndash; decreased; Zfp105, Zfp660 on Chr9 \u0026ndash; decreased; Zfp354a, Zfp354c on Chr11 \u0026ndash; increased; Zfp808, Zfp934 on Chr13 \u0026ndash; decreased; Zfp273, Zfp458, Zfp65, Zfp748, Zfp759, Zfp953 on Chr13 \u0026ndash; decreased), however 2 clusters had genes DE in opposite directions (Zfp110 \u0026ndash; decreased, Zfp324 \u0026ndash; increased on Chr7; Zfp192 \u0026ndash; decreased, Zkscan3 \u0026ndash; increased on Chr13). Only 6 KZfps that were altered by IF were also changed in aging alone, Zfp40, Zfp41, Zfp407, Zfp419, Zfp518a, Zfp57. Of these, Zfp40, Zfp41, Zfp407, Zfp419, Zfp518a were all reduced by aging and further reduced by IF. However, Zfp57 (Fig. \u003cspan class=\"InternalRef\"\u003e19\u003c/span\u003e) was reduced by aging (~\u0026thinsp;67% of young expression) but increased by IF, though not quite to young expression levels (~\u0026thinsp;84% of young expression).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003c/p\u003e \u003cp\u003eChronic inflammation is deleterious to cell, organ, and organism function. Inflammation is a hallmark of CNS aging, a chronic, sterile form of inflammation that is a major factor in functional declines across cognitive and sensorimotor domains. Chronic CNS inflammaging (neuroinflammaging), is characterised by sustained pro-inflammatory signalling, dyshomeostatic microglia that inappropriately prune synapses, dysfunctional mitochondria, and a pro-inflammatory senescence associated secretory phenotype (SASP) associated with senescent cells [\u003cspan additionalcitationids=\"CR87\" citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. For the most part, studies have focussed on the cellular responses that characterise neuroinflammation, with causative mechanisms having received limited focus, with some exceptions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Additionally, most studies are limited to one or two CNS regions.\u003c/p\u003e \u003cp\u003eHere, we have taken a deep-sequencing genomics approach to better appreciate how neuroinflammaging develops across the CNS. We demonstrate in four CNS regions of broad interest; cortex, hippocampus, cerebellum, and spinal cord, there is not a universal impact of neuroinflammaging, nor indeed by aging in general, and the timing, extent of inflammation, and inflammatory pathways involved are to a certain degree CNS region dependent. Somewhat surprisingly, we found SC to be profoundly affected by aging in general and neuroinflammaging in particular. Remarkably, at old age SC had almost triple the number of aging-related DEGs compared to the CB, the next most impacted region. The CTX and HIP, arguably the most investigated CNS regions, were the least aging affected (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). SC was also a neuroinflammaging hotspot, although CB was also markedly impacted (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Indeed, by most transcriptomic indicators of neuroinflammaging, SC was the most impacted region: inflammation (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), interleukin signalling (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), DAM (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), microglial exhaustion (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), inflammasome activation (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e), and TE derepression (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e and Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), were all most markedly affected in SC, with CB showing greater impact with interferon signalling (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), non-self, DNA/RNA signalling (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e and see Supplementary Table S6, Supplementary Material Online), and cGAS-STING signalling (Figs.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e). Notably, both SC and CB were significantly pro-inflammatory by middle-age, although the effects (number of affected genes and effect sizes) were generally modest. Unexpectedly, the CTX seems relatively resistant to neuroinflammaging, with only modest changes in inflammatory markers by old age. In contrast, the HIP while largely unaffected at middle age, was pro-inflammatory by old age.\u003c/p\u003e \u003cp\u003eWhy SC is a neuroinflammaging hotspot is an intriguing question. One possible cause is aging-related increase in blood-SC-barrier (BSCB) leakiness that would result in entry of normally excluded, potentially inflammatory, blood-borne factors and immune cells. Increased blood-CNS barrier leakiness is a common feature of neuroinflammation. Normally, the BSCB is leakier than the BBB [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e] and while we confirmed this difference, we did not find an age-related increase in BSCB paracellular leakiness [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. Increased BSCB leakiness, therefore, does not appear to be a reason for the relatively high SC neuroinflammaging.\u003c/p\u003e \u003cp\u003eAnother possibility is based on myelin debris. Myelin degradation is a characteristic of aging white matter (WM) [\u003cspan additionalcitationids=\"CR92\" citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e] and degenerating myelin proteins can produce highly immunogenic peptides [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e] that may trigger an inflammatory response. It is noteworthy the two CNS regions most inflamed with aging (SC and CB) have the highest relative myelin contents of the regions studied. WM associated microglia (WAM) phagocytose myelin debris in aging CNS [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e], but prolonged or overwhelming myelin degradation could lead to formation of DAM (see below), consistent with the WAM-DAM continuum proposed by Safaiyan \u003cem\u003eet al\u003c/em\u003e. (2021). Such prolonged myelin debris engulfment may be one reason microglia end up exhausted (see below). Whatever the causes, the heightened SC neuroinflammatory profile should lead to increased interest in this CNS region. The whole CNS needs to be functionally preserved if CNS health-span is to be improved. It is also of note that the mouse SC more accurately reflects the human brain in terms of relative GM/WM and therefore may, in certain aspects, be a more appropriate model of human brain aging.\u003c/p\u003e \u003cp\u003eDAM constitute a novel subpopulation of microglia characterised by association with neurological diseases. For example, DAMs are spatially associated with amyloid beta (Aβ) plaques in AD, and have upregulation of phagocytic and lipid processing genes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Why normally aging C57B/6 mouse would exhibit a similar profile to DAM is not entirely clear, as mouse brain does not form amyloid plaques [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]. However, the aging CNS is also characterised by build-up of other waste and toxic products including certain lipids [\u003cspan additionalcitationids=\"CR98\" citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e], proteins [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e], and a lipid-protein pigment called lipofuscin [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]. Also, as mentioned above, debris from degenerating myelin is processed by a population of microglia (WAM) that has transcriptomic overlap with DAM [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. It also could be that the aged SC, given its heightened inflammatory state, is more disease-like and \u0026lsquo;pathologically\u0026rsquo; aging than \u0026lsquo;normally\u0026rsquo; aging. The relationship between DAM and WAM and other microglial subpopulations awaits further scrutiny.\u003c/p\u003e \u003cp\u003eImmune cell exhaustion, while best understood in T cells [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e], has also been reported in other immune cells [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e], including macrophages [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]. This type of exhaustion is a result of chronic exposure to stimuli, subsequently dampening immune cell effectiveness, and causing metabolic signalling changes. Unsurprisingly, microglia, the resident macrophages of the CNS, also suffer prolonged exposure to stimuli and are thought to develop exhaustion states [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e]. For example, microglia exhaustion is characterised by impaired phagosome formation, and excessive production of oxidative and proinflammatory molecules [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This results in both an inability to clear myelin debris and extracellular waste adequately, and the production of damaging molecules by exhausted microglia. Importantly, these microglial products can induce neuronal loss [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Cd22, a canonical B cell receptor, was recently found to be specifically expressed on brain microglia, expression that increased with aging, and that negatively regulates microglial phagocytosis of myelin debris, amyloid-b oligomers, and a-synuclein fibrils. Blockade of Cd22 signalling reprograms aged microglia towards a homeostatic transcriptional state and improves cognitive function [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e]. Consistent with a role of Cd22 in microglial exhaustion, we found age-related expression of Cd22 to be increased in all regions, with SC having by far the largest increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eg). While Cd22 is also considered a DAM gene, there are gene expression overlaps between microglia subpopulations, and it is possible the DAM state is a precursor to microglial exhaustion.\u003c/p\u003e \u003cp\u003eInflammaging is considered sterile, therefore inflammatory triggers must be from within the tissue and not result from invasive pathogens. While myelin debris is a potential trigger for sterile inflammaging, there are other possibilities. There are two genomes in eukaryotic cells that may be responsible for induction of innate immune responses under sterile conditions, the nuclear and mitochondrial genomes. These genomes contain viral and bacterial sequences, respectively. Nucleic acids from either genome may elicit infection-like immune responses from cells under certain conditions. For the nuclear genome, the most likely scenario is an age-related loss of TE repression, which has been reported by others [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan additionalcitationids=\"CR108\" citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e]. Simon \u003cem\u003eet al.\u003c/em\u003e (2019) found a loss of LINE1 TE repression with age that presented as an increase in L1 cytoplasmic DNA, but not RNA [\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e]. Gulen \u003cem\u003eet al\u003c/em\u003e. (2023) showed microglial mitochondria in old mouse brains released mtDNA into the cytoplasm to trigger cGAS-STING signalling [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. There are many potential causes of mtDNA leak into the cytoplasm (see [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]) and much work remains to determine the causes of mtDNA (or mtRNA) release in the aging CNS. An intriguing possibility is the role of cholesterol, with overload of this lipid leading to disruption of mitochondrial and ER membranes and attachment of nucleoids, subsequently resulting in mtDNA release [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. We have previously reported increased cholesterol in SC of old animals [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e], raising the possibility of dysregulated cholesterol being involved in SC neuroinflammaging.\u003c/p\u003e \u003cp\u003eRegarding the nuclear genome, we did not find significant increases in TE expression in any region with age at the TE class or family levels. We did, however, identify hundreds of individual TEs were differentially expressed in old CNS. Importantly, TEs with increased expression vastly outnumbered those with decreased expression in all four CNS regions and both age comparisons (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e). While derepression suggests increased expression of TEs, we found expression of a considerable number of TEs was decreased. There is precedent for this phenomenon. For example, when TEs are derepressed via knockout of methyltransferases involved in heterochromatin formation or DNA methylation, there are both increases and decreases in TE expression [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e, \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e], changes that are consistent with the increases and decreases in H3k9me3 and DNA methylation observed following knock out of Setdb1 [\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e]. Our results are in line with those found by Ramirez \u003cem\u003eet al.\u003c/em\u003e (2022), who found hundreds of generally over-expressed TEs in the hippocampus with age [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e]. In contrast, Wahl \u003cem\u003eet al.\u003c/em\u003e (2023) did not find any significant differences in individual expression of TEs, but found that most TE classes (LINEs, SINEs, LTRs, and DNA transposons) were more highly expressed in old hippocampus [\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e]. Together, our data supports the hypothesis that derepressed transposable elements are driving, at least in part, CNS inflammaging.\u003c/p\u003e \u003cp\u003eTo better understand the mechanisms that might be involved with the widespread TE expression changes, we curated a list comprising genes involved in the many layers of TE regulation. Surprisingly, of the 141 genes in the list, expression of only four was significantly changed by aging in SC, the most affected CNS region. Expression of all four SC genes was increased (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The four genes are involved in various aspects of histone, DNA, and RNA methylation, and thus chromatin accessibility. That increased expression of these genes did not prevent aging-related TE expression, potentially indicates a failed compensatory response to chromatin changes and TE derepression. There was little association between extent of TE derepression and TE regulation gene expression changes, with no changes in CTX or CB, but decreased expression of Uhrf1, a repressor of TEs [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], was seen in HIP, the least impacted CNS region for TE derepression.\u003c/p\u003e \u003cp\u003eH3K9me3 is important in the establishment and maintenance of heterochromatin, and TE repression. We investigated the impact of aging on expression of genes encoding proteins of the H3K9me3 proteome. As with the TE regulation gene set, the old SC was most affected, with five genes differentially expressed between Y and O (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Roles for most of the proteins encoded by H3K9me3 proteome DEGs are not known, but Loxl and Rela proteins have putative chromatin-silencing activities [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Given the degree to which TE expression was affected by aging, the relatively modest molecular signature for changes in TE regulation and H3K9me9 were somewhat surprising and indicate dysregulation at other levels.\u003c/p\u003e \u003cp\u003eIn this regard, and in stark contrast to the aforementioned TE regulation and H3K9me3 proteome genes, we found expression of KZfp genes to be more impacted by aging. There was good correlation between the numbers of differentially expressed TEs and KZfps at both middle (r\u0026thinsp;=\u0026thinsp;0.85) and old ages (r\u0026thinsp;=\u0026thinsp;0.99), consistent with an important role of KZfps in TE regulation. KZfps are organised in clusters across the mouse genome, with clusters characterised by KZfps with similar genome sequence specificity. Cluster members also tend to be transcribed together [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. However, when we examined cluster membership of aging-related KZfp DEGs, only\u0026thinsp;~\u0026thinsp;26% (27/102) of aging differences were related by cluster, although approximately half of old age CB DE KZfps were in a cluster with another DE KZfp. We also found some cluster members in the old SC with discordant expression changes which is suggestive of dysregulation of KZfp co-transcriptional regulation. While there has been intensive interest in targets of KZfps, characterisation of the mechanisms regulating their expression has received less focus. Accumulating evidence, in particular from the cancer field, however, indicates epigenetics, transcription factors, and non-coding RNAs, all play a role [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e]. Indeed, aging-related changes in histone modifications and DNA methylation have been widely reported [\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e]. Hypo- or hypermethylation of KZfp regulatory sequences would alter KZfp transcription and the overall lack of common KZfp DEGs across the CNS and between middle and old ages is consistent with the stochastic nature of aging-related DNA methylome and epigenetic changes in general [\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnlike TEs, we were not able to measure cytoplasmic mtDNA with our genomics approach, therefore it was not possible to determine cytoplasmic levels of this inflammatory trigger. However, Zbp1 protein was recently demonstrated to stabilise a form of stressed mtDNA and initiate cytoplasmic cGAS signalling [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and we found Zbp1 expression was increased in all four CNS regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ee). While this increase could be considered an indication of aging-related accumulation of cytoplasmic mtDNA, Zbp1 is not specific for mtDNA as it can also bind viral nuclei acids [\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e]. Further work is required to determine which molecular species (viral/bacterial DNA/RNA) trigger neuroinflammaging in the various cell types of the CNS.\u003c/p\u003e \u003cp\u003eIn summary, we have established different CNS regions are differentially impacted by neuroinflammation with normal aging. The neuroinflammaging is associated with a substantial number of differentially expressed TEs, with SC the most inflamed region having by far the largest number of these derepressed elements, a finding consistent with the notion these evolutionary, virally-derived sequences, are a significant underlying cause of neuroinflammaging. Dietary restriction interventions have universally been shown to improve neuroinflammation, including calorie restriction and the various forms of IF (for reviews see [\u003cspan additionalcitationids=\"CR120\" citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e]). However, with the exception of one report [\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e], such studies have not investigated the association of TE regulation with neuroinflammaging. Here, we have demonstrated IF has a profound effect on TE regulation.\u003c/p\u003e \u003cp\u003eWe first confirmed findings of previous studies demonstrating the effects of dietary interventions on various aspects of CNS inflammaging. Adult lifelong IF had a robust effect on microglia with both DAM and exhaustion phenotypes improved. IF downregulated expression of Tyrobp pathway signalling molecules Trem2 and Tyrobp, cholesterol-related genes Apoe and Ch25h, lysosomal genes Ctsz and Lyz2, and the negative regulator of phagocytosis Cd22, and pattern recognition receptors Clec7a and Ifi204. The reduced Cd22 expression is consistent with reports that blockade of Cd22 restores homeostatic function in aged microglia, including restoration of phagocytosis of myelin debris and amyloid peptides [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e, \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e]. The microglial exhaustion-related gene, Cd72, is traditionally associated with B cells, but has recently been shown to be expressed by microglia and involved in inflammation and phagocytosis [\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e]. Taken together, the reduction in expression of DAM and microglial exhaustion genes, our data suggests that IF attenuates aging pro-inflammatory microglial phenotypes. In turn, we found reductions in inflammasome genes, particularly Nlrp1 and Casp4. While Cgas expression was unaffected by IF, we found a mild reduction in Sting1 expression and a non-significant reduction in Il1b expression. Long term IF is directly or indirectly modifying mechanisms that are driving these microglial phenotypes. One possible explanation may be the induction of autophagy and mitophagy by IF [\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e], which limits the continual build-up of waste products over the lifespan. Overall, IF improves neuroinflammaging in SC consistent with that demonstrated in other CNS regions.\u003c/p\u003e \u003cp\u003eAs we were particularly interested in potential drivers of aging-related sterile CNS inflammation, we wanted to determine whether IF could mitigate or reduce these inflammatory triggers. To do this we made use of gene expression profiles derived from models of cytoplasmic accumulation of non-self, nucleic acids, specifically, the Tfam\u003csup\u003e+/\u0026minus;\u003c/sup\u003e (bacterially derived) mtDNA and (virally derived) LINE1 derepression models [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. We found aging resulted in increased expression of many genes associated with these models across the CNS, especially at old age (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Genes of the cGAS-STING pathway that is activated following non-self, nucleic acid detection, were also upregulated as expected (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). Note, many upregulated genes associated with these models are, unsurprisingly, detectors of non-self, nucleic acids (Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e, and Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Supplementary Material Online; Aim2, Ifit1, Ifit2, Ifit3, Dhx58, Ifi204, Zbp1, cGAS). IF was effective at reducing the expression of many of these Tfam\u003csup\u003e+/\u0026minus;\u003c/sup\u003e and LINE1 derepression related genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e17\u003c/span\u003e), indicating a decreased burden of non-self, nucleic acids.\u003c/p\u003e \u003cp\u003eWe found expression of the viral RNA-sensing gene, Zc3hav1, was significantly increased at middle and old ages in all four CNS regions, the only non-self, nucleic acid (DNA and/or RNA) sensor to do so (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e). Zc3hav1 encodes a zinc finger CCCH domain-containing protein that binds to specific RNA viruses, targeting them for degradation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Recently, Zc3hav1 was shown to have a role in cGAS-STING activation and therefore is also involved anti-DNA virus activity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. It also improves NLRP3 oligomerisation, a critical step in the activation of the cytoplasmic NLRP3 inflammasome that plays important roles in innate immunity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Significantly, in the context of the present work, Zc3hav1 also inhibits LINE1 and Alu transposition [\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e]. Although Zc3hav1 expression in SC was not impacted by IF, given its prolonged, CNS-wide involvement, ability to detect multiple viral DNA and RNA sequences, and various antiviral activities [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], Zc3hav1 may be a therapeutic target for neuroinflammaging and warrants further investigation.\u003c/p\u003e \u003cp\u003eAn important issue is identification of the proximate triggers of neuroinflammaging. Non-self, nucleic acids are considered likely triggers of sterile neuroinflammation, albeit not the only ones. However, due to the number of different TEs and the difficulty in determining the intracellular localisation of non-self, nucleic acids (for example, mtDNA residing in nucleoids within mitochondria would not be a trigger), we used an alternate method for quantifying non-self, nucleic acid triggers. By quantifying expression of non-self, DNA/RNA sensors as surrogates for nucleic acids themselves, we could assess inflammation triggers and showed the SC and CB had by far the greatest trigger load (see Supplementary Table S6, Supplementary Material Online). We curated a 33-gene non-self, DNA/RNA sensor list and, based on the number of significantly changed sensor genes in each region at both middle and old age, the SC and CB had by far the largest number of sensor DEGs, followed by HIP, with CTX being relatively unaffected. Notably, expression of all sensor DEGs was increased with aging in all four CNS regions, with CB having the largest inflammation trigger load at middle age. Furthermore, based on the nucleic acids these sensors detect, there does not appear to be a single, dominating trigger, with sensors for both single- and double-stranded RNA and DNA all being upregulated. Remarkably, the majority of sensor DEGs (14/24) that were upregulated at old age in SC, were universally downregulated with IF. Why IF did not reduce expression of all upregulated sensors requires further evaluation. Nonetheless, IF has a profound effect on non-self, DNA/RNA sensor expression, presumably reflecting decreases in the presence of these nucleic acid species in the aged CNS, a finding consistent with the overall beneficial effects of this dietary intervention.\u003c/p\u003e \u003cp\u003eRelatively little is known about the effects of IF, or other dietary interventions for that matter, on TE expression. We found the number of TEs that were differentially expressed when comparing IF with age-matched ad libitum-fed controls, was markedly higher than for aging alone, which seems counterintuitive in the context of the anti-inflammation effects associated with IF. Intriguingly, however, there was a flip in the ratio of numbers of TEs with increased to decreased expression, with aging associated with ~\u0026thinsp;2.5\u0026ndash;3-fold greater number of TEs with increased expression relative to those with decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e18\u003c/span\u003e), whereas IF saw ratios of ~\u0026thinsp;1.2\u0026ndash;1.7 in favour of decreased expression. Exactly how this results in reduction in inflammation remains to be determined, but a role for TE transcripts themselves may be at play. Indeed, methylation of chromatin-associated TE RNAs has been shown to regulate heterochromatin [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Whatever the mechanism, the aforementioned decreased expression of non-self, nucleic acid sensors indicates the trigger load has dropped with IF. It is also thought that TE expression needs to be balanced for somatic cell survival [\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e, \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the markedly changed TE expression profile induced by IF we probed for TE regulation and H3k9me3 proteome gene expression changes. Unlike with aging, where expression of only a few genes in these sets was changed (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), IF resulted in markedly more changes (Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). For TE regulation there were over six-times as many DEGs as there were for aging alone, and expression of ~\u0026thinsp;92% (22/24) of these was elevated. Similarly, for H3K9me3 proteome genes, IF was associated with a\u0026thinsp;~\u0026thinsp;3-fold increase in DEGs, with expression of ~\u0026thinsp;79% (11/14) of these being elevated. Considering just the broad increases in expression in TE regulation and H3K9me3 related genes, one interpretation is there was a substantial increase in chromatin remodelling, presumably resulting in re-heterochromatisation from the relatively de-heterochromatised state of the aging CNS. Importantly, IF impacted multiple levels of TE regulation including histone, DNA, and RNA methylation, RNA interference, chromatin remodelling, TE pre- and post-transcriptional activities such as nuclear RNA retention (to prevent cytoplasmic translation) and export via nuclear exosomes for lysosomal degradation, transcriptional repressor protein recruitment, and histone deacetylation. The IF-associated DEGs for the H3K9me3 proteome largely have uncharacterised roles for this heterochromatin structure but many have RNA binding properties. IF has also been previously described to alter epigenetic mechanisms, including in H3K9me3 in cerebellum [\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe final layer of TE regulation we investigated was the KZfps. Compared to aging alone, IF was associated with more KZfp DEGs (63 v 40 aging alone at old age) in SC with most of these showing decreased expression compared to age-matched ad libitum-fed controls. Surprisingly few (6/63) Zfp DEGs were also DEGs with aging alone. Since the beneficial effects of IF on TE regulation involves many regulatory layers, it may be difficult to target pharmacologically.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eDue to availability issues, and also to allow comparison with the most comprehensive report to date using this IF model [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], only male mice were used in the present work. Studies will need to be replicated in female mice to identify any sex-related differences. Use of an inbred mouse strain may also be considered a limitation in the context of mimicking human aging. However, even with an isogenic strain we observed marked differences in overt characteristics such as body weights, degree of thoracic spine kyphosis, neoplasms, and fur greying, including between cage mates. Aging is stochastic by nature and using inbred mice could in fact be considered an experimental advantage, as it allows assessment of behavioural, cellular and molecular variabilities caused by aging per se and not genetic variability.\u003c/p\u003e \u003cp\u003eOur bulk RNA sequencing (RNAseq) analyses used RNA extracted from CNS region tissue homogenates and, therefore, cell type resolution is lost. With the exception of genes known to be expressed in specific cell types, it is generally not possible to ascribe aging and IF related expression changes to particular types of cells using this type of bulk RNAseq approach. Importantly, recent single-cell RNAseq (scRNAseq) reports have demonstrated that most aging-related (non-TE) DEGs are cell-type- and CNS-region-type specific, further underscoring the need to carry out cell-type-specific gene expression analyses in different CNS regions [\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e, \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e]. An important consideration, however, is that sc/snRNAseq (single nucleus) technologies typically use short, single-end, 3' derived reads that compromises mapping accuracy for TEs as they are highly repetitive. Further developments are needed to improve sensitivity and accuracy of scTE mapping and expression quantification [\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e] before this approach can be broadly implemented.\u003c/p\u003e \u003cp\u003eThe present genomics study was undertaken to improve our understanding of potential drivers of CNS neuroinflammaging. Deep RNAseq confers a sensitive and robust, transcriptome-wide, discovery approach that is ideal for initial investigations of poorly understood mechanisms. Molecular signatures have been identified that hopefully will provide the neurobiology of aging field bases to build on using complementary and confirmatory protein, epigenetic, and chromatin-based approaches. Ideally, process(es) will be identified that can be therapeutically targeted to obviate the need to comply with a challenging, long-term, dietary intervention.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe have demonstrated, based on transcriptomics indices along with gene ontology analyses, that SC is an aging hotspot region of the CNS, both in terms of the impacts of aging on gene expression as well as the degree of inflammation. The aged SC appears to suffer from substantial TE derepression, and it is likely increased TE expression is a significant causal trigger to the neuroinflammation, although aging-related cytoplasmic release of mtDNA cannot be ruled out as an additional inflammatory trigger. Encouragingly, we identify IF as a potential mitigator of neuroinflammaging, as this dietary intervention profoundly influenced the TE expression profile and reduced most inflammatory markers. We propose IF resulted in a rebalancing of the transposonome. Future work will hopefully lead to a more mechanistic understanding of how IF achieves its beneficial effects and the identification of processes that could be targeted by a more widely acceptable intervention.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eAnimals\u003c/h2\u003e \u003cp\u003eHealthy C57BL/6 male mice were used for all experiments. Animals were maintained under standard housing conditions, on a 12-hour light-dark cycle, with food (Specialty Feeds SF00-100) and water available ad libitum. ADF animals had no food access every second day from ~\u0026thinsp;8 weeks of age for their lifespan. All mice were euthanized with 1mL i.p. Lethabarb (325mg/ml) before tissue collection. All animal work was undertaken in strict accordance with the University of Newcastle Animal Ethics Committee, and New South Wales and Australian animal research guidelines. Young mice were 3\u0026ndash;4 months old, middle-aged were 12\u0026ndash;14 months old, and old mice were 24 months old.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTissue Dissection and Cryosectioning\u003c/h2\u003e \u003cp\u003eAll phosphate buffered saline (PBS) used was diethyl pyrocarbonate (DEPC) treated and autoclaved before use to inactivate RNases. Mice were transcardially perfused on ice with 50mL ice-cold PBS, and brains and spinal cords were dissected out, frozen in isopentane on dry ice and stored at -80\u0026deg;C for later use. Tissues were then mounted in optimum cutting temperature (O.C.T.) compound and cryosectioned at 100\u0026micro;m thickness. Cryosections were thaw mounted on RNase-free glass microscope slides and stored at -80\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTissue preparation for bulk RNA sequencing\u003c/h2\u003e \u003cp\u003eCNS regions were dissected out from brain sections using a clean scalpel blade and collected in a tube containing RNAlater (ThermoFisher). RNA was extracted using the miRNeasy Micro Kit (Qiagen) following manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003cp\u003eAll RNA samples were DNAse treated in solution using DNase I (Invitrogen) as per manufacturer instructions. Briefly, RNA was DNase treated for 15 mins with DNase I and the DNase subsequently inactivated by addition of 25mM EDTA and heating to 65\u0026deg;C for 10 minutes. DNA-free, RNA samples were sent to the Australian Genome Research Facility (AGRF, Melbourne) for sequencing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRNA sequencing\u003c/h2\u003e \u003cp\u003eTotal RNA was rRNA depleted, fragmented and first and second (dUTP) cDNA strands synthesised. Adaptors were ligated and the first strand underwent 13 cycles of PCR amplification. cDNA was sequenced using TruSeq PE Cluster Kit v3 reagents and the NovaSeq 6000 system (Illumina) with between ~\u0026thinsp;74\u0026ndash;121\u0026nbsp;million (CTX), ~\u0026thinsp;92\u0026ndash;123\u0026nbsp;million (HIP), ~\u0026thinsp;40\u0026ndash;108\u0026nbsp;million (CB), ~\u0026thinsp;79\u0026ndash;146\u0026nbsp;million (SC), 150 bp, paired end reads, per sample. For each region, all samples were run on a single lane.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics\u003c/h2\u003e \u003cp\u003eRead files were first subject to QC using the \u003cem\u003eFASTQC\u003c/em\u003e tool [\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e]. Adaptors were removed using the \u003cem\u003eCutadapt\u003c/em\u003e tool [\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e]. Forward and reverse reads files were aligned using the \u003cem\u003eSTAR\u003c/em\u003e aligner [\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e]. Aligned files were assessed to determine DEGs using \u003cem\u003eCuffdiff\u003c/em\u003e [\u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e], \u003cem\u003eHTSeq-count\u003c/em\u003e [\u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e] followed by \u003cem\u003eDeseq2\u003c/em\u003e [\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e] or \u003cem\u003eedgeR\u003c/em\u003e [\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e]. DEG lists were compiled using an FDR cut-off of \u0026lt;\u0026thinsp;0.05, and requiring significance in 2 of 3 DEG analysis programs. DEG lists were analysed for Gene Ontology (GO) using \u003cem\u003eMetascape\u003c/em\u003e [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. DEG lists were compared with the inflammation-related gene lists in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Supplementary Material Online. Enrichment of gene sets was determined using a hypergeometric overlap calculator (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://systems.crump.ucla.edu/hypergeometric/\u003c/span\u003e\u003cspan address=\"https://systems.crump.ucla.edu/hypergeometric/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). p-values were corrected for multiple comparisons.\u003c/p\u003e \u003cp\u003eFor expression of transposable elements, read files were aligned using \u003cem\u003eSTAR\u003c/em\u003e with specific parameters as suggested by \u003cem\u003eTEtranscripts\u003c/em\u003e [\u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e]. We performed testing of differential expression for transposable elements using \u003cem\u003eTelescope\u003c/em\u003e with an FDR cut-off of \u0026lt;\u0026thinsp;0.05 [\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eImages\u003c/h2\u003e \u003cp\u003eVolcano plots, heatmaps, and bar graphs were generated with \u003cem\u003eGraphpad Prism\u003c/em\u003e. For heatmaps of the counts per million (CPM) for each gene was calculated. Gene counts for each sample were divided by either the average CPM of the region (CTX, HIP, CB, SC heatmaps), or the average CPM of the young (IF heatmaps). For bar graphs, gene counts for each sample were divided by the young average CPM of the region. Venn diagrams were generated using an online tool available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinformatics.psb.ugent.be/webtools/Venn/\u003c/span\u003e\u003cspan address=\"https://bioinformatics.psb.ugent.be/webtools/Venn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Gene Ontology cluster heatmaps were generated with \u003cem\u003eMetascape\u003c/em\u003e [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Graphical methods generated with \u003cem\u003eBioRender.com\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll animal work was undertaken in strict accordance with the University of Newcastle Animal Ethics Committee (Approvals: A-2021-137, A-2016-625, A-2015-526) and New South Wales and Australian animal research guidelines.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.C., E.C., and D.S. conceived the study. M.C. was involved in the planning and performing of all experiments and analyses, generated all figures, tables, and supplements, and wrote the manuscript. E.C. reviewed and edited the manuscript, and performed tissue and RNA extractions. D.S. was involved in the planning and performing of all experiments and analyses, generated tables, and wrote the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to acknowledge the assistance of Mr Aaron Scott for IT support in relation to RNA sequencing analyses. We would like to acknowledge the University of Newcastle for funding supporting the aging mouse colony.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eSequence data that support the findings of this study have been deposited in the Sequence Read Archive (SRA) with the primary accession code PRJNA1110971.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G (2023). Hallmarks of aging: An expanding universe. Cell, 186:243\u0026ndash;278.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSierra A, Gottfried-Blackmore AC, McEwen BS, Bulloch K (2007). Microglia derived from aging mice exhibit an altered inflammatory profile. 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PLoS Comput Biol, 15:e1006453.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 6 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"genome-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Genome Medicine](https://genomemedicine.biomedcentral.com/)","snPcode":"13073","submissionUrl":"https://submission.springernature.com/new-submission/13073/3","title":"Genome Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6165725/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6165725/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA hallmark of CNS aging is sterile, chronic, low-grade neuroinflammation. Understanding how the aging CNS develops chronic inflammation is necessary to achieve extended healthspan. Characterisation of neuroinflammatory molecular triggers remains limited. Interventions that reduce neuroinflammation and extend health and lifespan could be useful in this regard. One such intervention is intermittent fasting (IF), but how IF impacts CNS inflammation is insufficiently understood. To address this, we performed deep RNA-sequencing on young, middle-aged, and old, mouse CNS regions. Additionally, we sequenced spinal cord in animals subject to adult lifelong IF.\u003c/p\u003e \u003cp\u003eWe found most differentially expressed genes (DEGs) at middle age were CNS region specific (~\u0026thinsp;50\u0026ndash;84%), whilst this effect weakened (~\u0026thinsp;18\u0026ndash;72%) in old age, suggesting emergence of a more general global aging profile. DEGs from all regions were enriched for inflammatory and immune ontologies. Surprisingly, SC was the most aging- and neuroinflammation-impacted region at both middle and old ages, with by far the highest number of DEGs, the largest net increase in expression of transposable elements (TEs), the greatest enrichment of immune-related ontologies, and generally larger increases in inflammatory gene expression. Overall, with normal aging we found upregulation of sensors of non-self, DNA/RNA, activation of specific inflammasomes, and upregulation of cGAS-STING1 and interferon response genes, across the CNS.\u003c/p\u003e \u003cp\u003eWhilst IF animals still developed an inflammatory profile with aging in SC, average immune gene expression was lower by ~\u0026thinsp;50% compared to age-matched controls. IF-specific DEGs were apparent, suggesting IF also acts on separate, potentially targetable, pathways to those impacted by normal aging. Expression of disease associated microglia, phagocytic exhaustion, sensors of non-self, DNA/RNA, STING1, and inflammasome genes were all decreased with IF. Significantly, the TE profile was reversed with a net expression decrease. In summary, we find SC is a CNS aging hotspot, and that IF attenuates neuroinflammaging potentially by rebalancing the transposonome.\u003c/p\u003e","manuscriptTitle":"Intermittent fasting attenuates CNS inflammaging - rebalancing the transposonome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-12 11:38:24","doi":"10.21203/rs.3.rs-6165725/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"genome-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Genome Medicine](https://genomemedicine.biomedcentral.com/)","snPcode":"13073","submissionUrl":"https://submission.springernature.com/new-submission/13073/3","title":"Genome Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c103dc6e-6550-4d71-a891-7b34e5b04783","owner":[],"postedDate":"March 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2025-05-16T14:38:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-12 11:38:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6165725","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6165725","identity":"rs-6165725","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}