Iron Accumulation with Age alters Metabolic Pattern and Circadian Clock gene expression through the reduction of AMP-modulated Histone Methylation

Iron accumulates with age in mammals, and its possible implications in altering metabolic responses are not fully understood. Here, we report that both high-iron diet and advanced age in mice consistently altered gene expression of many pathways, including those governing the oxidative stress response and the circadian clock. We used a metabolomic approach to reveal similarities between metabolic profiles and the daily oscillation of clock genes in old and iron-overloaded mouse livers. In addition, we show that phlebotomy decreased iron accumulation in old mice, partially restoring the metabolic patterns and amplitudes of the oscillatory expression of clock genes Per1 and Per2. We further identified that the transcriptional regulation of iron occurred through a reduction in AMP-modulated methylation of histone H3K9 in the Per1 and H3K4 in the Per2 promoters, respectively. Taken together, our results indicate that iron accumulation with age can affect metabolic patterns and the circadian clock. Iron accumulates with age in mammals, and its possible implications in altering metabolic responses are not fully understood. Here, we report that both high-iron diet and advanced age in mice consistently altered gene expression of many pathways, including those governing the oxidative stress response and the circadian clock. We used a metabolomic approach to reveal similarities between metabolic profiles and the daily oscillation of clock genes in old and iron-overloaded mouse livers. In addition, we show that phlebotomy decreased iron accumulation in old mice, partially restoring the metabolic patterns and amplitudes of the oscillatory expression of clock genes Per1 and Per2. We further identified that the transcriptional regulation of iron occurred through a reduction in AMP-modulated methylation of histone H3K9 in the Per1 and H3K4 in the Per2 promoters, respectively. Taken together, our results indicate that iron accumulation with age can affect metabolic patterns and the circadian clock. Iron is a vital trace element for most organisms, and it functions in numerous physiological processes. In mammalian cells, iron is an essential substrate for the biosynthesis of heme and sulfur–iron cluster proteins (1Madeddu C. Gramignano G. Astara G. Demontis R. Sanna E. Atzeni V. Macciò A. Pathogenesis and treatment options of cancer related anemia: Perspective for a targeted mechanism-based approach.Front. Physiol. 2018; 9: 1294Crossref PubMed Scopus (55) Google Scholar). Iron deficiency could lead to disorders including but not limited to anemia (2Stoltzfus R.J. Iron deficiency: Global prevalence and consequences.Food Nutr. 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We found that serum iron and Tf saturation in old mice were significantly higher, accompanied by lower unbound iron-binding capacity with total iron-binding capacity unchanged (Fig. 1A). Diaminobenzidine (DAB)-enhanced Prussian blue staining of livers from old mice showed more extensive stainable iron compared with that in young mice (Fig. 1B). Quantitative measurement of iron in the liver confirmed an increase of iron accumulation with age (Fig. 1C). The significant increases in iron accumulation with age were also observed in other organs, including the heart, kidney, brain, and spleen (Fig. S1). Moreover, excess iron supplement to young mice also led to elevated serum iron and Tf saturation (Fig. 1D), exhibiting a significantly higher accumulation of iron in livers (Fig. 1, E and F) and other peripheral organs (Fig. S2). These results indicated that iron was inevitably accumulated in the tissues with age because of the imbalance of iron intake and excretion. To analyze the physiological association between iron accumulation and aging, RNA-Seq was performed to analyze all poly A-containing transcripts in the livers of old mice and iron-overload mice. Using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, the significantly enriched pathways were identified. Mapping of annotated differentially expressed genes (DEGs) to KEGG pathways revealed 947 DEGs and 666 DEGs of 34 pathways in the livers of iron-overload group (Fig. 2A, left) and old group (Fig. 2A, right), respectively. The 34 KEGG pathways were disturbed in both groups, implying that the transcription changes in iron-overload group have some degree of similarity to those seen in old group. The 450 genes that are markedly altered in the old group overlapped with those in the iron-overload group (Fig. 2B, top). The Venn diagram revealed 88 upregulated genes and 182 downregulated genes that were commonly modulated in both groups (Fig. 2B, bottom). A heatmap of the 450 genes with significant regulatory functions was constructed (Fig. 2C). Pearson r analysis showed that there was a strong correlation between biological repeats in each group. On the DEGs of aging pathway, the top 20 altered pathways in upregulated and downregulated genes were shown in Figure 2D. Most of the changed genes related to metabolic regulation and oxidative stress were consistently changed in both groups. Real-time RT–PCR analysis confirmed that increased Maoa, Gpx3, Gadd45a, Plk2, Dapk2, and Cd36, and decreased Prdx5, Gpx1, Gstm2, Gstp1, Ulk2, and Gsto1 in old and iron-overload groups (Fig. 2E), and these target genes with meaningfully changed expression, were concerned with oxidative stress. Together, these results revealed a possibility that age-increased oxidative stress is partially contributed by the iron accumulation with age. To characterize the metabolic changes of iron-related aging, we acquired 1H NMR-based metabonomics analysis on old mice and iron-overload mice. As a metabolic profiling technique, metabonomics analysis allowed us to acquire the small-molecule metabolite profiles and analyze the differences in the metabolic profiles between groups. The orthogonal partial least squares discriminant analysis (OPLS-DA) score plots of control and old groups were distinct (Fig. 3A, left), and a clear separation was observed between control group and iron-overload groups as well (Fig. 3B, left). The corresponding loading plots displayed similar patterns (Fig. 3A, right; Fig. 3B, right). The identified metabolites from the NMR data of all groups were shown as a heatmap in Figures 3C, S3, and Table S1. The metabolite changes in iron-overload group were highly similar to that observed in old group. The identified metabolites data were subject to pathway enrichment analysis, and many pathways were consistently altered in both groups, including glutathione, porphyrin and chlorophyll metabolism, glyoxylate and dicarboxylate metabolism, pantothenate and CoA biosynthesis, and others (Fig. 3D). OPLS-DA models were validated by permutation tests repeated 200 times for confirming the reliability (Fig. 3, E and F). In the principal component analysis score plot, old group and iron-overload group were both well separated from control group, whereas the old group overlapped with iron-overload group (Fig. 3G), suggesting similar metabolic changes in both groups. The scree plot showed the variance of the first five principal components (Fig. 3H), and it demonstrated that principal component 1 and principal component 2 were the most important ones as shown in Figure 3G. Next, hematology and biochemistry parameters related to oxidative stress and liver physiology were assessed. Both old and iron-overload groups showed noticeable increments in serum levels of aspartate aminotransaminase and alanine aminotransferase, hepatic malondialdehyde, accompanied by reduced antioxidant enzyme activities of superoxide dismutase, catalase (CAT), and GSH levels (Fig. 3I). These results further confirmed that age-related oxidative stress associates with iron accumulations with age. We also noted that environment adaptation pathway was obviously altered by pathway enrichment analysis in old and iron-overload groups, and circadian entrainment was one of the significantly disturbed pathways (Fig. 4A). To further confirm the results from RNA-Seq, we performed real-time RT–PCR analysis for clock genes in the livers of old and iron-overload mice. The mesor, amplitude, and acrophase of each fitting curve are listed in Tables S2 and S3. Unexpectedly, the alteration of core clock genes displayed broad similarity in old and iron-overload livers (Fig. 4, B–M). Specifically, a reciprocal change of Per1 and Per2 was observed in both groups, with elevated Per1 expression and lowered Per2 expression in both groups. These results implied that iron overload could contribute to the age-dependent shift in circadian function. To demonstrate that age-related iron accumulation was a key regulator in metabolism and circadian function, phlebotomy was applied to reduce iron stores, which is one of the most widely used methods to decrease systemic iron levels. Expectedly, serum iron and Tf saturation were decreased after phlebotomy (Fig. 5, A–D). Liver iron content was also reduced (Fig. 5E). H&E staining revealed nearly no morphological changes of the livers in phlebotomy groups. Nevertheless, DAB-enhanced Prussian blue staining demonstrated a decrease of iron accumulation in the livers after phlebotomy (Fig. 5F). The activities of superoxide dismutase and CAT were elevated by phlebotomy as well as GSH levels, and malondialdehyde levels were decreased (Fig. 5G). The 1H NMR-based metabolome analysis confirmed that phlebotomy caused a significant recovery in the metabolic profiling. As shown in Figure 5,H and I, the metabolic profiles of young mice and old mice with phlebotomy were both well separated from that of old mice and close to each other. The corresponding loading plots and the alterations in identified metabolites also revealed high similarity between the metabolic profiles of young and old mice with phlebotomy (Fig. S6 and Table S4). The OPLS-DA models were validated with permutation tests (200 times) to confirm the reliability (Fig. 5J). The identified metabolites from the NMR data of all groups were shown as a heatmap in Figure 5K. Moreover, the mRNA expressions of antioxidant enzymes including SOD1, SOD2, Gpx1, and CAT were increased by phlebotomy in a dose-dependent manner (Fig. 5L). The oscillation amplitude of clock genes Per1 and Per2 was partially restored by phlebotomy (Fig. 5M). These results indicated that phlebotomy in old mice eliminated iron accumulation, restoring metabolic profiles and the clock function. To show that iron directly functioned on Per1 and Per2 transcription, a luciferase reporter assay was used to examine the effects of iron on Per1 and Per2 transcription in vitro. Contrary to the assumption, iron with different concentrations did not significantly affect Per1 and Per2 transcription in cultural cells (Fig. 6A). However, mice that received i.v. injection with iron showed an increase of Per1 expression and a decrease of Per2 in livers, respectively, and the effect was in a time-dependent and dose-dependent manner (Fig. 6B). The aforementioned observations could be explained by a blood signal that acts as a modulator of Per1 and Per2 expression during iron injection, and iron is not the direct regulator. We reasoned that the putative regulator when injected into mice should induce changes of Per1 and Per2 expression instead of iron. The putative regulator could either be a peptide or an organic molecule, and we initially chose to analyze the latter possibility. Plasma extracts obtained from mice with iron injection were analyzed with reverse-phase HPLC. The results revealed that iron injection caused an elevation of plasma AMP, and this effect was dose dependent (Fig. 6, G and H, left). An increased plasma AMP was also observed in old mice, and phlebotomy in old mice alleviated the elevation of plasma AMP (Fig. 6H, right). In isolated red blood cells (RBCs) and their lysates, iron significantly increased the total ROS levels (Fig. 6, C–E) with different levels. Iron-elevated ROS could induce hemolysis (Fig. 6F), which inevitably caused the release of intracellular nucleotides including AMP. To demonstrate that AMP is a regulator, we injected chemically synthesized AMP into mice to investigate whether clock gene expression was affected in the liver. Using quantitative RT–PCR analysis, we could detect an increased Per1 mRNA and a decreased Per2 mRNA by AMP (Fig. 6I). Thus, these studies showed that AMP is a potential modulator by which iron regulates the expression of circadian genes. The extracellular nucleotidase converts AMP to adenosine in the plasma. Therefore, the most likely intracellular action of AMP would be via the adenosine receptors and transporter pathways. We injected adenosine into mice, and it also induced reciprocal changes of Per1 and Per2 expression (Fig. 7, A and B). Then we used NIH3T3 cells to explore the regulation effects of AMP. Theophylline, a nonspecific adenosine receptor antagonist, did not affect the AMP-induced changes of Per1 and Per2 expression (Fig. 7, C and D). An adenosine transporter inhibitor dipyridamole significantly inhibited the effect of AMP on Per1 and Per2 transcription (Fig. 7, C and D), indicating that the intracellular action of AMP is likely mediated through the adenosine transporter pathways. Moreover, AMP resulted in a significant decrease in the SAM/SAH ratio, a known cellular methylation potential (Fig. 7E). To further investigate whether cellular methylation potential is involved in the regulation of Per1 and Per2, we used methylation activator SAM and methylation inhibitor cycloleucine to observe the effects on Per1 and Per2 transcription. While cycloleucine induced reciprocal changes of Per1 and Per2 transcription (Fig. 7F), the addition of SAM blocked the action of AMP (Fig. 7G). These results strongly suggest the axis of iron–ROS–AMP–methylation modulates clock gene expression. Then immunofluorescence analysis showed that AMP led to decreasing global H3K4me3 and H3K9me2 in a dose-dependent manner (Fig. 7,H and I), and the result was confirmed by Western blotting (Fig. 7J). Chromatin immunoprecipitation (ChIP) analysis revealed that AMP markedly decreased the abundance of H3K9me2 in the Per1 promoter and did not affect H3K4me3 abundance. On the contrary, AMP decreased H3K4me3 abundance in Per2 promoter with H3K9me2 abundance unchanged (Fig. 7, K and L). Similarly, we observed an elevated plasma level of AMP and a reciprocal change of histone methylation pattern in Per1 and Per2 promoters of old and iron-overload mice (Figs. S4 and S5). Together, our studies have identified that AMP is a signaling molecule that can regulate circadian function in response to disturbed iron homeostasis in old mice. 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