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Systematic age‐, organ‐, and diet‐associated ionome remodeling and the development of ionomic aging clocks

aging biomarkers of aging calorie restriction chemical elements ionome

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#1 Engadin

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Posted 27 April 2020 - 11:18 PM


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O P E N   A C C E S S   S O U R C E :   Aging Cell

 

 

 

 

 

 

Abstract
 
Aging involves coordinated yet distinct changes in organs and systems throughout life, including changes in essential trace elements. However, how aging affects tissue element composition (ionome) and how these changes lead to dysfunction and disease remain unclear. Here, we quantified changes in the ionome across eight organs and 16 age groups of mice. This global profiling revealed novel interactions between elements at the level of tissue, age, and diet, and allowed us to achieve a broader, organismal view of the aging process. We found that while the entire ionome steadily transitions along the young‐to‐old trajectory, individual organs are characterized by distinct element changes. The ionome of mice on calorie restriction (CR) moved along a similar but shifted trajectory, pointing that at the organismal level this dietary regimen changes metabolism in order to slow down aging. However, in some tissues CR mimicked a younger state of control mice. Even though some elements changed with age differently in different tissues, in general aging was characterized by the reduced levels of elements as well as their increased variance. The dataset we prepared also allowed to develop organ‐specific, ionome‐based markers of aging that could help monitor the rate of aging. In some tissues, these markers reported the lifespan‐extending effect of CR. These aging biomarkers have the potential to become an accessible tool to test the age‐modulating effects of interventions.
 
 
 
1 INTRODUCTION
 
Aging is associated with systemic deleterious changes at all levels, from molecular to organismal, leading to a gradual decline in function. During aging, organisms accumulate diverse forms of damage and other deleterious changes, ranging from the consequences of errors in intrinsic biological processes to the effects of extrinsic factors coming from diet and environment (Gladyshev, 2016; Lopez‐Otin, Blasco, Partridge, Serrano, & Kroemer, 2013). These intrinsic and extrinsic risk factors interfere with normal functions, causing alterations to the genome, epigenome, proteome, transcriptome, and metabolome (Booth & Brunet, 2016).
 
Age‐related changes include changes in the levels of chemical elements (Meplan, 2011). Human body is composed of about 60 elements, about one third of which have known biological functions (Chellan & Sadler, 2015). Some of these elements are macroelements, that is, elements characterized by high abundance. They form the basic structures of molecules and tissues. For example, Na and K contribute to the electric potential produced by nerve cells, which is key to all neuroactivities (Chellan & Sadler, 2015). Ca is not only a key component of bones and teeth, but also regulates contractions of cardiac muscle cells through its cation form (Vaughan‐Jones, 1986). Mg plays a role in regulating cell cycle, specifically in the replication, transcription, and translation (Walker, 1986). S is a component of two amino acids and numerous cofactors (Kessler, 2006). In addition to the key role P plays in nucleic acids, it is a major component of the bones.
 
Some elements in the body are known as trace elements. Despite their lower levels, these elements (many of which are transition metals) play important roles in vital biological processes. For example, Mn is well known as a critical component of Mn‐superoxide dismutase and several other proteins (Avila, Puntel, & Aschner, 2013). Fe acts through hundreds of Fe‐containing proteins, such as the famous heme‐containing protein acting as the carrier of oxygen in the body. Zn is also a component of hundreds of proteins and is central to the regulation of development and wound healing, as well as the immune system and reproductive functions (Oteiza & Mackenzie, 2005; Stefanidou, Maravelias, Dona, & Spiliopoulou, 2006). Cu acts as a cofactor of redox enzymes and contributes to a set of biological processes such as cardiovascular development (Bost et al., 2016). Co is actively involved in neuroprotection and hematopoietic systems through the function of vitamin B12. Mo is a key part of the MoCo cofactor, a critical contributor to neurological functions (Hänsch & Mendel, 2009). Finally, as a functional component of selenoproteins, Se plays important roles in redox control, thyroid function, and other biological processes (Labunskyy, Hatfield, & Gladyshev, 2014). In addition to these essential elements, there are elements in the body that do not have known biological functions, such as As and Cd. Although As has been utilized for treating human diseases such as leukemia, its use is related to its toxic properties (Shen et al., 1997).
 
The balanced element composition in the body is closely related to its health status, whereas an altered homeostasis of elements may lead to diseases, some of which resemble functional decline related to aging. For example, a shift in the balance of elements, especially metals, may predispose to neurodegenerative diseases. Overexpression of amyloid beta, a protein implicated in the Alzheimer's disease, leads to Mn accumulation inside the brain, and excess Fe and Cu also accelerate the progression of this disease. Parkinson's disease, in addition, was reported to be related to altered Mn homeostasis and elevated Fe levels, and the Down Syndrome was associated with altered Zn levels (Fraga, 2005; Gaeta & Hider, 2005). Furthermore, changes in Cu were linked with diabetes and cardiovascular diseases, both of which are age‐related diseases (Uriu‐Adams & Keen, 2005). On the other hand, the effect of aging on the composition of elements is not well understood.
 
In the current study, we sought to better understand aging by analyzing changes in element composition throughout lifespan. We quantified the ionome (19 element isotopes) of eight organs of mice aged 3–35 months old. We observed that the distribution of elements reflects their organ of origin and that age‐related changes in this distribution are gradual. We also found clear differences between control mice and mice subjected to calorie restriction (CR) at multiple levels. In addition, we explored the possibility of building organ‐specific biomarkers of aging based on organ ionomes and developed a series of clocks that could track the aging process and report the effect of CR. These findings offer a better understanding of aging from the perspective of element composition and provide a convenient tool that may be applied to assess the biological age of animals.
 
 
 
2 RESULTS
 
2.1 Organ ionomes are stable throughout life
 
We prepared a large‐scale dataset of ionome profiles of C57BL/6 mice representing eight organs (brain, lung, heart, testis, liver, muscle, pancreas, and kidney) and 16 age groups ranging from young adults (3 months old) to very old mice (35 months old), with up to 4–5 biological replicates per age group (Figure 1a). After filtering, the resulting dataset included 1,271 tissue measurements. We assayed 20 chemical elements, including 10 elements that were assessed by two different isotopes, by using inductively coupled plasma‐mass spectrometry (ICP‐MS) and could reliably quantify 19 isotopes representing 13 elements (Na, Mg, Ca, P, S, K, Mn, Fe, Co, Cu, Zn, Se, and Mo) across the entire mouse lifespan. Additional elements (As, Cd) were also quantified and analyzed, but were not detected in all tissues. Principal component analysis (PCA) revealed distinct sample clustering by the first three principal components (Figure 1b). The samples clustered by organ of origin, indicating high stability of element composition of organs throughout adult life. This observation is consistent with a previous ionomic analysis of young adult males across different species of mammals (Ma et al., 2015). Interestingly, liver samples exhibited a wider distribution, implying a higher diversity in the ionome structure of this organ. We examined these outliers in more detail and found that almost all of them belonged to the tissues of mice aged 30–35 months, suggesting that the liver is more prone to the disrupted elemental composition with age than other tissues examined.
 
 
acel13119-fig-0001-m.jpg
 
 
Figure 1. Overview of mouse organ ionomes
(a) Age distribution of mouse samples and schematic of analyses in the study. Sixteen age groups (3–36 months old) of mice on a standard diet and four age groups (10–27 months old) of mice on calorie restriction were analyzed. All mice were C57BL/6 animals. (b) Principal component analysis of samples. Organ origin is shown with different colors. Replicates are presented as individual points. © Heatmap view of samples and elements. Each row represents one element or isotope. Each column represents one particular biological sample. Elements with contents lower than noise in certain tissues (e.g., Cd and As) are shown in Figure S2. Clustering was performed using complete‐linkage method with Euclidean distance measure. The same color scheme of organ of origin (shown on the right) is used for panels (b) and ©.
 
 
 
 
Hierarchical clustering also resulted in the samples being grouped by their organ of origin (Figure 1c). Different isotopes of the same element showed very tight clustering, as they are similarly utilized by an organism. The two analyzed Se isotopes, Se78 and Se82, showed a slight variation in the lung, although the basis of this effect is unclear. We examined similarity in element profiles across organs and found that Na and Ca clustered together, whereas Mg and K clustered with S and P. Also, most transition metals clustered together, except for Mo and Co, which were closer to Se. They showed higher levels in the kidney and lower in the brain and heart. Ca and Mg were elevated in the muscle, which agrees with their reported distribution in the body (Jahnen‐Dechent & Ketteler, 2012).
 
 
2.2 Interactions among elements
 
To examine associations among elements across lifespan, we calculated their Spearman's correlation coefficients for tissues and age groups (Figure 2a). As expected, isotopes of the same element strongly correlated, exhibiting coefficients above 0.9. We previously found that Fe, Co, Mn, and Mo form a cluster across 26 species of mammals (Ma et al., 2015). In contrast, the macroelements Mg, S, P, and K formed a cluster across the mouse lifespan (Figure 2a). This observation may be related to the high levels of these elements in mammalian tissues. In addition, K and Mg were highly correlated to Ca, consistent with their involvement in common regulatory functions.
 
 
acel13119-fig-0002-m.jpg
 
 
Figure 2. Features of elemental composition across organs
(a) Correlation coefficient matrix of elements across eight organs. Coefficients >0.4 or <−0.4 are highlighted in color. (b) Principal component analysis of elements. The 19 isotopes are projected on the first two PCs. © Macroelement composition of different organs. Comparison between the scaled value of element content is performed for eight organs. (d) Trace element composition of different organs. Comparison between the scaled value of element content is performed for eight organs.

 

 

 

The trace elements Mn, Cu, Zn, and Mo formed another cluster. We also observed a cluster of Ca and Na. This is consistent with the pervasive sodium–calcium exchange in biology. Excitable cells (mostly neurons) use a sodium–calcium exchanger to export the Ca ion when taking in the Na ion, preventing Ca accumulation. Principal component analysis offered further information on the correlation matrix of elements (Figure 2b). Interestingly, Mn, Ca, Cu, and Zn clustered in the middle of the plot. These elements are in the same period on the periodic table, and they all are characterized by biologically functional divalent states, even though they have different ionic radii, suggesting that the chemical properties of elements may influence their utilization.
 
We further focused on the distribution of elements across tissues (Figure 2c,d; Figure S3). Liver, kidney, and testis showed the highest levels of Se. Kidney exhibited a large range of element levels, and surprisingly, it also had the lowest Zn and Fe values. We also found that testis had high levels of Zn, supporting its a role in sperm function (Fallah, Mohammad‐Hasani, & Colagar, 2018).
 
 
2.3 Age‐associated ionome remodeling
 
We sought to determine how the element levels change across tissues with age (Figure 3a). Most elements (eight out of 13) had a negative correlation with age in all or almost all organs analyzed, consistent with an acquired systemic deficiency of these elements (Zn, Cu, Se, Mn, Mg, P, S, K) in old age. The remaining five elements showed contrasting patterns in different tissues or were increased with age; for example, we observed elevated Ca in kidney and muscle, Fe in pancreas and testis, and Mo in brain and testis. We also examined how the variability in element levels changes with age by analyzing the coefficient of variation (Figure 3b). We observed a pattern, wherein most elements in most tissues were characterized by increased variation with age. This is consistent with disrupted homeostasis of tissues with age. Notable exceptions were the lung and testis.
 
 
acel13119-fig-0003-m.jpg
 
 
Figure 3. Changes in element levels with age
(a) Spearman's correlation coefficient between age and element levels. (b) Spearman's correlation coefficient between age and element coefficient of variation. © Distinct age‐related changes in iron levels in the testis and lung. A complete representation of element changes is in Figure S4. (d) Age‐related increase in calcium levels in the testis. Calcium increases sharply in the oldest ages. (e) Principal component analysis of samples based on age and diet. Transition across age is indicated with increased color intensity: Blue circles indicate animals on a standard diet, and red triangles indicate animals on a calorie restriction (CR) diet. The figure shows percent variation explained by the first three components.
 
 
 
We examined in more detail the elements that exhibited contrasting age‐related trends (Figure 3c). The Fe levels in the testis as well their variability increased with age, whereas this element showed a totally different pattern in the lung. The main source of Fe in the lung is the serum, so a decrease in Fe may be the sign of a reduced blood flow in this organ. A similar pattern was observed in the testis. This is consistent with oxidative damage in response to high Fe (Turner & Lysiak, 2008). In addition to the elements steadily increasing with age, we noticed elevated Ca in the brain and testis specifically in the very old mice (Figure 3d). In the testis, this dramatic increase was only observed in the oldest mice (34–35 months old) and it also varied dramatically within these age groups. The data suggest that massive calcification may occur at very advanced ages.
 
Because the elements such as Ca showed sharp increases in some tissues in the oldest ages, we were interested to determine whether age‐associated changes in element levels occur gradually or are subject to sudden shifts at a certain age. To examine this question, we carried out PCA of samples grouped by their age. Interestingly, one of the principal components was defined by mouse age (Figure 3e), showing that the entire ionome moved along a particular trajectory, with gradual changes in element levels with age (Figure S6). Strikingly, CR mice showed a trajectory in the same direction, which was shifted along another principal component. We interpret these data as that, rather than slowing down the aging process, at the organismal level CR remodels metabolism, so that the organism ages along a different (shifted) trajectory that is still going in the same direction.
 
 
2.4 Organ‐ and element‐specific effects of caloric restriction
 
Calorie restriction is one of best characterized longevity interventions in mice. Similar to the analysis of the organismal ionome (Figure 3e), PCA within individual organs (lung, heart, pancreas, muscle, and kidney) revealed an aging trajectory (Figure 4a; Figures S5 and S6). Interestingly, the trajectories of CR and control samples were aligned in the muscle and kidney. For these two organs, CR mice corresponded to younger control mice, suggesting that in these organs the CR effect may resemble a shift to younger state in terms of element composition.
 
 
 
 
 
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Also tagged with one or more of these keywords: aging, biomarkers of aging, calorie restriction, chemical elements, ionome

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