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Keywords:

  • biomarker;
  • cf-DNA;
  • frailty;
  • inflammaging;
  • nonagenarians;
  • self-DNA

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Results
  5. Discussion
  6. Experimental procedures
  7. Acknowledgments
  8. Conflict of interest
  9. Author contributions
  10. References
  11. Supporting Information

Plasma cell-free DNA (cf-DNA) has recently emerged as a potential biomarker of aging, reflecting systemic inflammation, and cell death. In addition, it has been suggested that cf-DNA could promote autoinflammation. Because the total cf-DNA pool comprises different cf-DNA species, we quantified the plasma levels of gene-coding cf-DNA, Alu repeat cf-DNA, mitochondrial DNA (mtDNA) copy number, and the amounts of unmethylated and total cf-DNAs. We identified the relationships between these cf-DNA species and age-associated inflammation, immunosenescence, and frailty. Additionally, we determined the cf-DNA species-specific transcriptomic signatures in blood mononuclear cells to elucidate the age-linked leukocyte responses to cf-DNA. The study population consisted of n = 144 nonagenarian participants of the Vitality 90+ Study and n = 30 young controls. In the nonagenarians, higher levels of total and unmethylated cf-DNAs were associated with systemic inflammation and increased frailty. The mtDNA copy number was also directly correlated with increased frailty but not with inflammation. None of the cf-DNA species were associated with immunosenescence. The transcriptomic pathway analysis revealed that higher levels of total and unmethylated cf-DNAs were associated with immunoinflammatory activation in the nonagenarians but not in the young controls. The plasma mtDNA appeared to be inert in terms of inflammatory activation in both the nonagenarians and young controls. These data demonstrate that the plasma levels of total and unmethylated cf-DNA and the mtDNA copy number could serve as biomarkers of frailty. In addition, we suggest that circulating self-DNA, assessed as total or unmethylated cf-DNA, might aggravate immunoinflammatory reactivity in very old individuals.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Results
  5. Discussion
  6. Experimental procedures
  7. Acknowledgments
  8. Conflict of interest
  9. Author contributions
  10. References
  11. Supporting Information

Following tissue damage or conditions involving cellular stress and death, cells release cell-free DNA (cf-DNA) into the circulation. The concentration of plasma cf-DNA typically reflects the magnitude of damage and inflammation, and it has been shown that cf-DNA levels have predictive value in certain acute conditions (Butt & Swaminathan, 2008). We (Jylhava et al., 2012) and others (Fournie et al., 1993) have introduced cf-DNA to the field of aging biomarkers, suggesting that it serves as a novel biomarker of age-associated low-grade inflammation (inflammaging) and age-associated systemic decline. However, methods for assessing the levels of plasma cf-DNA have typically relied on the PCR-based quantification of a given coding sequence, even though the total cf-DNA pool is composed of different cf-DNA molecules in terms of quantity and quality. Specifically, the total cf-DNA pool comprises nuclear protein-coding, noncoding and repeat sequences as well as mitochondrial DNA (mtDNA). According to a high-throughput sequencing analysis performed in healthy individuals (Beck et al., 2009), the plasma cf-DNA sequence representation mirrors that of the genome; however, an overrepresentation of Alu repeat sequences in cf-DNA has been demonstrated, suggesting that repeat sequences are actively secreted in living cells (Stroun et al., 2001). In addition, exceptionally high concentrations of mtDNA have been detected in the plasma of trauma patients (Zhang et al., 2010), and variation in the cf-DNA methylation status is known to accompany a variety of conditions ranging from cancer to neurological and psychiatric diseases (Levenson & Melnikov, 2012). However, all cf-DNA species can be captured using a single dsDNA-intercalating dye-based measurement that quantifies the total cf-DNA irrespective of the sequence and fragment length. Therefore, in this study, we quantified the plasma levels of gene-coding cf-DNA (RNAse P gene), Alu repeat cf-DNA, and mtDNA copy number and determined the amount of unmethylated cf-DNA relative to the total cf-DNA and analyzed these cf-DNA species separately to elucidate their role in age-associated inflammation and frailty.

The age-associated remodeling of the immunoinflammatory compartment typically manifests as a chronic inflammatory state, increased reactivity to self-antigens, including DNA (Agrawal et al., 2009), and a decline in the adaptive immune branch (Franceschi et al., 2000). Other body systems, such as those at the musculoskeletal and cognitive axes, also undergo a notable age-associated deterioration (Hunt et al., 2010). The generalized age-associated increased vulnerability to environmental and endogenous stressors can be defined as frailty, a condition in which the individual is at the limit of his/her physiological reserves in more than one homeostatic system (Flicker, 2008). Although inflammaging and frailty are often associated with chronic multimorbidity, they are not equivalent to it, but are indicative of systemic senescence and disability (Hubbard & Woodhouse, 2010; Hunt et al., 2010). In keeping with this, chronic disability and the traditional markers of inflammaging, such as C-reactive protein (CRP) and interleukin-6 (IL-6), are stronger mortality predictors than multimorbidity (Marengoni et al., 2009). Longitudinal studies have also reported that elevated levels of CRP, IL-6, and tumor necrosis factor alpha (TNF-α) are independently associated with disability, frailty, and muscle strength decline, suggesting that inflammation may be causally related to skeletal muscle catabolism and tissue homeostasis (Roubenoff, 2003; Hubbard & Woodhouse, 2010; Li et al., 2011). The potential value using cf-DNA as a biomarker can be attributed to the fact that virtually every tissue and cell type can release cf-DNA following death or injury. Consequently, any immune-competent cell capable of endocytosing cf-DNA can evoke a response to it, which depending on the motifs in the cf-DNA can be immunostimulatory, suppressive, or inert (Ishii & Akira, 2005; Pisetsky, 2007). In effect, accumulating evidence has demonstrated that endogenous DNA can serve as a danger-associated molecular pattern (DAMP) capable of triggering host immune responses (Ishii & Akira, 2005). Potent immunostimulatory motifs in mammalian DNA include the unmethylated deoxycytidyl-deoxyguanosine (CpG) stretches that resemble those found in bacterial DNA and in CpG oligodeoxynucleotide vaccine adjuvants (Ishii & Akira, 2005; Pisetsky, 2007). The role of unmethylated DNA acting as a key DAMP has been demonstrated recently in vitro by Agrawal et al. (2010) who observed that the level of global DNA methylation decreases with age, concomitantly with its increasing immunogenicity (Agrawal et al., 2010).

The activity of endogenous DNA can also be determined by the components that are complexed with it. The factors known to render self-DNA immunostimulatory include the high mobility group box protein 1 (HMGB1) (Pisetsky, 2007; Urbonaviciute et al., 2008), cathelicidin antimicrobial peptide (CAMP) (Lande et al., 2007), and the antichromatin antibodies (Leadbetter et al., 2002; Boule et al., 2004). The DNA originating from necrotic cells, undigested apoptotic cells, or from macrophages that have digested the contents of apoptotic cells is first encountered by the cell membrane and/or endosomal receptors that include the receptor for advanced glycation end products and the Toll-like receptor 9 (Ishii & Akira, 2005; Pisetsky, 2007). In addition, chromatin-immunocomplexes can also be internalized via the dendritic cell (DC) Fcγ receptors, or the B-cell receptor and can be accompanied by immunostimulatory activity in the recipient cells (Leadbetter et al., 2002; Boule et al., 2004; Avalos et al., 2010).

In this study, we had two aims. First, we determined the associations between the cf-DNA species (total cf-DNA, unmethylated cf-DNA, RNAse P-coding cf-DNA, Alu repeat cf-DNA, and mtDNA copy number) with immunoinflammatory parameters and frailty. Second, following our observations in the previous Vitality 90+ Study (Jylhava et al., 2012) in which we demonstrated that the total cf-DNA level predicted a 4-year all-cause mortality and reflected the systemic low-grade inflammation, we investigated whether these associations could be linked with leukocyte immunoinflammatory responses, and if so, which of the cf-DNA species were responsible. To this end, we analyzed the cf-DNA species-specific genome-wide transcriptomic signatures in peripheral blood mononuclear cells (PBMCs), cells that are in constant contact with cf-DNA and involved in its turnover. The analyses were performed separately in the nonagenarians and young controls to delineate the age-associated characteristics of the cf-DNA and to elucidate its role a biomarker of aging.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Results
  5. Discussion
  6. Experimental procedures
  7. Acknowledgments
  8. Conflict of interest
  9. Author contributions
  10. References
  11. Supporting Information

The characteristics and distribution of the study variables are presented in Table 1. In both the nonagenarians and young controls, a strong intercorrelation was observed between the total cf-DNA level and the unmethylated cf-DNA level and between the levels of RNase P-coding cf-DNA and Alu repeat cf-DNA (Table 2). The mtDNA copy number did not correlate with any of the other cf-DNA species (Table 2) or the immunoinflammatory markers. A direct correlation between the levels of the proinflammatory mediators CRP and IL-6 and the total cf-DNA and unmethylated cf-DNA was observed only in the nonagenarians but not in the young controls (Table 2).

Table 1. Characteristics of the study population. Statistically significant differences in the study variables between the nonagenarians and young controls are shown in bold
 Nonagenarians (n = 144)Young controls (n = 30)P for differencee
MedianIQRMedianIQR
  1. BMI, body mass index; CD, cluster of differentiation; CRP, C-reactive protein; GE, genomic equivalent; IL, interleukin; IQR, interquartile range; kyn, kynurenine; MMSE, Mini-Mental State Examination; NA, not available; trp, tryptophan.

  2. a

    Percentage of total lymphocytes.

  3. b

    Percentage of CD4+ cells.

  4. c

    Percentage of CD8+ cells.

  5. d

    Percentage of live-gated cells.

  6. e

    Mann–Whitney U-test.

Total cf-DNA (μg ml−1)0.8840.788–9860.8200.662–0.901 0.002
Unmethylated cf-DNA (μg ml−1)0.7010.618–0.8110.6430.503–0.732 0.004
mtDNA (copy number ml−1)3.79E82.73E8–5.07E84.05E82.63E8–5.41E80.818
RNase P cf-DNA (GE)13.389.02–19.987.254.56–13.70 <0.001
Alu repeat cf-DNA (GE)71.0551.01–101.0941.8529.25–62.04 <0.001
CRP level (ng ml−1)2.150.82–4.271.500.58–2.82 0.041
IL-6 level (pg ml−1)3.972.41–6.194.111.78–6.340.546
IL-10 level (pg ml−1)1.510.98–2.532.181.08–3.140.154
CD4+ cells (%)a63.5051.95–73.2359.7555.95–62.030.055
CD8+ cells (%)a28.0020.18–39.9331.8527.18–35.150.218
CD4+ CD28− cells (%)b9.604.18–18.250.250.10–1.58 <0.001
CD8+ CD28− cells (%)c68.4051.58–77.0316.9512.65–29.10 <0.001
CD14+ cells (%)d8.305.68–11.932.651.80–3.23 <0.001
BMI (kg m−2)26.1222.81–29.49NA  
Handgrip (kg)2016–24NA  
Chair stand test time (s)1512–23NA  
MMSE score2522–27NA  
Barthel index9590–100NA  
Frailty score (percentage of individuals with each score, min 0, max 5) 0 = 25.0 NA  
1 = 32.6   
2 = 19.4   
3 = 18.8   
4 = 3.5   
5 = 0.7   
Table 2. Spearman correlations between the different cf-DNA species and study variables in the nonagenarians and young controls. Statistically significant correlations are shown in bold
 Nonagenarians (n = 144)Young controls (n = 30)
Total cf-DNAUnmethylated cf-DNAmtDNA copy numberRNase P cf-DNAAlu cf-DNATotal cf-DNAUnmethylated cf-DNAmtDNA copy numberRNase P cf-DNAAlu cf-DNA
  1. BMI, body mass index; CD, cluster of differentiation; CRP, C-reactive protein; GE, genomic equivalent; IL, interleukin; MMSE, Mini-Mental State Examination; NA, not available.

  2. a

    Percentage of total lymphocytes.

  3. b

    Percentage of CD4+ cells.

  4. c

    Percentage of CD8+ cells.

  5. d

    Percentage of live-gated cells.

Unmethylated cf-DNA r 0.9530.954
P <0.001<0.001
mtDNA copy number r 0.1260.131−0.023−0.053
P 0.1320.1250.9040.782
RNase P cf-DNA r 0.1730.1720.0960.7390.7310.013
P 0.0380.0430.252<0.001<0.0010.945
Alu cf-DNA r 0.1170.1130.0830.8210.6400.6520.0820.078
P 0.1640.1860.323<0.001<0.001<0.0010.665<0.001
CRP level r 0.3110.3250.0590.1130.0250.057−0.0160.3040.061−0.002
P <0.001<0.0010.4820.1760.7670.7630.9310.1030.7480.993
IL-6 level r 0.2370.1920.145−0.060−0.0420.093−0.0310.1500.0390.029
P 0.0040.0240.0840.4760.6190.6260.8690.4300.8360.878
IL-10 level r 0.1260.0430.0610.1100.1040.007−0.1180.245−0.101−0.008
P 0.1320.6190.4710.1880.2160.9710.5360.1920.5950.965
CD4+ cellsa r −0.054−0.023−0.0280.0370.065−0.161−0.1760.240−0.0020.057
P 0.5440.7950.7480.6740.4640.3950.3540.2010.9900.764
CD8+ cellsa r 0.0120.0010.005−0.049−0.0790.2910.356−0.1920.3270.304
P 0.8940.9910.9540.5810.3760.1180.0540.3100.0780.103
CD4+ CD28− cellsb r 0.0720.013−0.022−0.082−0.1040.0880.134−0.304−0.147−0.183
P 0.4140.8860.8020.3550.2390.6640.4810.1010.4380.332
CD8+ CD28− cellsc r −0.082−0.115−0.109−0.084−0.048−0.076−0.011−0.2150.1740.174
P 0.3550.1990.2150.3400.5890.6890.9530.2550.3580.357
CD14+ cellsd r 0.0650.0470.0640.0770.1180.1330.128−0.224−0.030−0.024
P 0.4640.5990.4720.3840.1830.4820.4990.2340.8750.898
BMI r 0.026−0.019−0.2090.011−0.005NANANANANA
P 0.7640.8290.0130.8960.954
Handgrip strength r −0.123−0.185−0.188−0.059−0.072NANANANANA
P 0.1530.0340.0290.4790.409
Chair stand test time r 0.2190.1650.2620.1150.046NANANANANA
P 0.0220.0910.0060.2350.636
MMSE score r −0.201−0.233−0.109−0.0950.022NANANANANA
P 0.0160.0060.1940.2590.798
Barthel index r −0.230−0.222−0.036−0.033−0.002NANANANANA
P 0.0060.0090.6710.6980.981
Frailty score r 0.2580.2900.1820.0980.047NANANANANA
P 0.0020.0010.0290.2420.574

In the nonagenarians, the amounts of total cf-DNA, unmethylated cf-DNA, and mitochondrial cf-DNA displayed a range of associations with the markers and indices of functional performance and frailty (Table 2) but not with the markers of immunosenescence (proportions of CD4+ CD28− and CD8+ CD28− cells) or the proportions of CD3+, CD4+, and CD8+ lymphocytes or monocytes (CD14+ cells) (Table 2). Similarly in the young controls, correlations were not observed between the plasma levels of the cf-DNA species and the leukocyte proportions (Table 2).

In the bioinformatics analysis, we assessed the transcriptomic signatures for the total cf-DNA level and the unmethylated cf-DNA level because of their correlation with the inflammatory markers. The transcriptomic signatures for the mtDNA copy number were assessed because of previous findings demonstrating an mtDNA-induced inflammatory response (Zhang et al., 2010). For the pathway analysis (IPA), we included the transcripts (assessed using the Chipster correlation tool) exhibiting expression levels in the PBMCs that correlated with the concentrations of total cf-DNA, unmethylated cf-DNA, and mitochondrial cf-DNA. The top 250 transcripts exhibiting expression levels that correlated with the plasma levels of total cf-DNA, unmethylated cf-DNA, and mtDNA copy number in the nonagenarians and young controls are presented in Tables S1, S2, and S3, respectively. The 10 most statistically significant canonical pathways harbored by the transcripts that exhibited expression levels correlating with the levels of total cf-DNA, unmethylated cf-DNA, and mtDNA copy number are presented in Tables 3, 4, and 5, respectively. In addition, all the statistically significant canonical pathways harbored by these transcripts are presented in Supporting Tables 4, 5, and 6 (S4, S5, and S6). Although only seven significant canonical pathways were identified for the total cf-DNA level–correlated transcripts in the young controls and 10 significant canonical pathways were identified for the mtDNA copy number–correlated transcripts in the nonagenarians, to maintain uniformity, these pathways are presented as supporting Tables S4b and S6a.

Table 3. The top 10 IPA canonical pathways harbored by the 250 transcripts that exhibited expression levels correlating with the plasma total cf-DNA concentration in the nonagenarians (a) and young controls (b)Thumbnail image of
Table 4. The top 10 IPA canonical pathways harbored by the 250 transcripts that exhibited expression levels correlating with the plasma unmethylated cf-DNA concentration in the nonagenarians (a) and young controls (b)Thumbnail image of
Table 5. The top 10 IPA canonical pathways harbored by the 250 transcripts that exhibited expression levels correlating with the plasma mtDNA copy number in the nonagenarians (a) and young controls (b)Thumbnail image of

In the bioinformatics analysis, notable differences were observed for the correlated transcripts and the resultant canonical pathways across the different cf-DNA species between the nonagenarians and the young controls (Tables S1–S6). In the nonagenarians, a notable overlap was observed in the transcripts with expression levels that correlated with the levels of total cf-DNA and unmethylated cf-DNA (Tables S1a and S2a). This overlap was also observed in the corresponding canonical pathways of which the majority was involved in immunoinflammatory responses or cytoskeleton/integrin-associated signaling (Tables 3a and 4a, S4a and S5a). In contrast, in the young controls, the correlated transcripts (Tables S1b and S2b) and the resulting canonical pathways for the total cf-DNA level and unmethylated cf-DNA level were different; the pathways for the total cf-DNA level–correlated transcripts largely represented cellular turnover-related processes, whereas the pathways for the unmethylated cf-DNA level were largely involved in immune signaling. Interestingly, however, most of these immune-related transcript expression levels were inversely correlated with the amount of unmethylated cf-DNA (Tables 3b and 4b, S3b and S4b). In both the nonagenarians and young controls, the canonical pathways for the mtDNA copy number–correlated transcripts covered cellular signaling, metabolism, and hormone signaling pathways (Tables 5a, 5b, S6a and S6b). In addition, three integrin/cytoskeleton-related pathways (Paxillin Signaling, Integrin Signaling, and ILK Signaling) were identified in the nonagenarians (Tables 5a, 5b, S6a, and S6b). However, pathways did not demonstrate overt immunoinflammatory activation associated with the mtDNA quantity (S6a and S6b).

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Results
  5. Discussion
  6. Experimental procedures
  7. Acknowledgments
  8. Conflict of interest
  9. Author contributions
  10. References
  11. Supporting Information

In this study, the concentrations of the total cf-DNA, unmethylated cf-DNA, RNase P-coding cf-DNA, and Alu repeat cf-DNA were significantly elevated in the nonagenarians compared with the young controls, suggesting that the age-accompanied increase in cellular senescence and death rate is manifested as elevated plasma cf-DNA. Moreover, the observation that higher concentrations of total cf-DNA and unmethylated cf-DNA were directly associated with inflammaging indicates that the plasma levels of these cf-DNA species are associated with central processes of aging, strengthening their role as aging biomarkers. However, we did not observe correlations between the cf-DNA levels and the markers of immunosenescence, indicating that the immune-related associations of cf-DNA are not extended to this niche of adaptive immunity. The finding that the plasma mtDNA copy numbers did not differ between the elderly and the young could reflect the aging-associated depletion of cellular mtDNA (Welle et al., 2003), manifesting as a decreased amount of mtDNA in relation to total cf-DNA in plasma. However, conflicting results have been reported with regard to the depletion of cellular mtDNA with age (Miller et al., 2003), which necessitates further research to elucidate the basis of our finding.

The observation that the levels of total cf-DNA, unmethylated cf-DNA, and mtDNA copy number were directly correlated with the frailty score leads us to speculate that the tissues that are most prominently affected by frailty could be among the primary sources of these cf-DNA species. However, as the total and unmethylated cf-DNA levels were observed to better reflected the ‘generalized' frailty, including cognitive functions, while the mtDNA quantity was restricted to reflecting the physical aspect of frailty, we hypothesize that these cf-DNA species exhibit a degree of specificity with regard to their originating tissue. Specifically, catabolism-related cellular senescence and death in brain, skeletal muscle, and nonmuscle lean tissue could be responsible for the elevation in total and unmethylated cf-DNA levels, whereas the plasma mtDNA level could increase following the depletion of mitochondria in sarcopenic skeletal muscle. Similar to our findings, Swarup et al. (2011) have suggested that the elevated cf-DNA levels observed in subjects with Friedreich's ataxia and spinocerebellar ataxia are due to muscular and neuronal degeneration in the patients. However, it is also possible that frailty is associated with changes in cellular DNA methylation status in several other cell types as well, all of which can contribute to the pool of unmethylated plasma cf-DNA. For example, Bellizzi et al. (2012) have demonstrated that a lower global DNA methylation status in buffy coat cells is associated with frailty in 65- to 85-year-old individuals but not in ultranonagenarians (Bellizzi et al., 2012).

We next asked whether the total cf-DNA, unmethylated cf-DNA, and mtDNA plasma levels were associated with differential PBMC responses in the nonagenarians and young controls. In the nonagenarians, immunoinflammatory activity dominated the pathways of total cf-DNA and unmethylated cf-DNA levels. Extensive involvement of the integrin signaling and cytoskeleton/actin-remodeling pathways (i.e., FAK Signaling, ILK Signaling, Signaling by Rho Family GTPases, RhoGDI Signaling, Actin Nucleation by ARP-WASP Complex, Regulation of Actin-based Motility by Rho, Paxillin Signaling, and Rac Signaling) was also observed in these data sets. Remodeling of the cytoskeleton through actin and integrin signaling pathways plays a key role in leukocyte effector functions, such as activation, migration, and phagocytosis (Fenteany & Glogauer, 2004). In addition, convergence of the immunoreceptor and integrin-ligation signaling pathways has been demonstrated downstream of the T-cell, B-cell, and Fc receptors (Abram & Lowell, 2007). As the T- and B-cell Receptor Signaling pathways and Fcγ Receptor-mediated Phagocytosis in Macrophages and Monocytes were also identified in these data sets, we propose that conditions associated with high total and unmethylated cf-DNA levels could involve broad activation of the immune system. The identified mTOR Signaling pathway may also merge into this network because of its role in controlling antigen-presenting cell activation and T-cell and B-cell receptor pathways (Powell et al., 2012). However, the extent to which cf-DNA contributes to the immune system activation and autoinflammation warrants further research.

In the EIF2 Signaling pathway, the downregulated expression of several ribosomal proteins was associated with increased levels of total and unmethylated cf-DNA in the nonagenarians. Various stress conditions are known to downmodulate EIF2-regulated global translation accompanied by the selective translation of proteins required for coping with the stressors, mitigating cellular injury, or inducing apoptosis (Wek et al., 2006). Because our data sets also identified several stress-related pathways (p53 Signaling, HMGB1 Signaling, mTOR Signaling, NRF2-mediated Oxidative Stress Response, and Production of Nitric Oxide and Reactive Oxygen Species in Macrophages) and an apoptotic pathway (Myc Mediated Apoptosis Signaling), it appears that fluctuation in the plasma levels of total and unmethylated cf-DNA is inherently linked with various cellular stress conditions in the old individuals. Therefore, we hypothesize that high levels of total and unmethylated cf-DNA or the processes associated with elevation of these cf-DNA species could be among the stressors that contribute to attenuated EIF2-regulated global translation.

In the young controls, the pathways identified for the total cf-DNA level were mainly involved in cellular metabolism and cell cycle regulation. Because the EIF2 Signaling pathway emerged without any stress-related pathways in this data set, it is likely that the total cf-DNA level in young individuals fluctuates with cellular turnover and is not associated with stress or immune responses. In contrast, the pathways for the unmethylated cf-DNA level included various immunoinflammatory pathways that exhibited a tendency toward downregulation with increasing concentrations of unmethylated cf-DNA. Thus, it appears that a somewhat reversed situation prevails in the PBMC responses associated with the unmethylated cf-DNA level in the nonagenarians and the young individuals. Tentatively, these observations suggest that cf-DNA might acquire its proinflammatory properties with aging or in the ‘inflammation-primed’ milieu. Indeed, Agrawal et al. (2009) reported that DCs from aged subjects exhibited increased reactivity to human DNA compared with DCs from young subjects. The same group also demonstrated that the age-associated decrease in DNA methylation status led to the enhanced immunogenicity of the DNA when delivered into DCs (Agrawal et al., 2010). Furthermore, Atamaniuk et al. (2012) reported that cf-DNA or plasma from end-stage renal disease patients exhibiting continuous innate immunity activation is capable of inducing IL-6 production in monocytes (Atamaniuk et al., 2012). Other inherent properties of self-DNA may also contribute to its immunomodulatory capacity. Antagonizing effects of suppressive elements in mammalian DNA have been demonstrated against CpG-driven immune activation; the telomeric sequences (TTAGGG) and their ability to form G-tetrads have been identified as the key suppressive motifs in self-DNA (Gursel et al., 2003). Telomere shortening occurs with aging; however, the relevance of this phenomenon in the potential cf-DNA–related immunomodulation cannot be addressed using our data.

In both the nonagenarians and young controls, the pathways identified for the plasma mtDNA copy number included Protein Ubiquitination Pathway and the Estrogen Receptor Signaling pathway, which have been identified as regulators of mitochondrial homeostasis (Neutzner et al., 2008) and biogenesis (Klinge, 2008), respectively. Likewise, the pathways Melatonin Signaling, Glucocorticoid Receptor Signaling, and Androgen Signaling pathways identified in the young controls might reflect a more global external control of these signaling pathways on mitochondrial turnover, which could manifest as fluctuation in the plasma mtDNA quantity. Nevertheless, the lack of immunoinflammatory engagement in the pathways suggests that mtDNA is not immunostimulatory under physiological concentrations. Therefore, the potential mtDNA-mediated inflammatory activation may involve other cell types or conditions, such as neutrophils, which have recently been demonstrated to be activated by mtDNA released following trauma (Zhang et al., 2010).

Overall, the results of the transcriptomic analysis provide information that higher levels of total and unmethylated cf-DNA are associated with different PBMC activation states and responses in vivo in very old and young individuals—a finding that could be of relevance with regard to biomarker discovery and the mechanisms of immune aging. However, a weakness of this study is that is does not reveal the cell type–specific responses associated with the cf-DNA levels. Therefore, for example, potential DC-restricted DNA-sensing pathways might have been masked due to the responses of more abundant cell types. In addition, a clear limitation of the study is that the median IL-6 level appeared unusually high in the healthy young controls. Possible explanations include the possibility that some individuals were recently engaged in strenuous physical exercise before the blood collection or were about to develop an infection. The strengths of this study include a well-characterized and relatively large cohort of very elderly individuals and a multidirected approach to characterize the role of cf-DNA in immune aging and frailty.

In conclusion, we suggest that the total circulating cf-DNA and its unmethylated content serve as indicators of inflammaging and frailty, whereas the plasma mtDNA concentration serves as a marker of the physical aspect of frailty. Our results also suggest that increasing concentrations of total and unmethylated cf-DNAs might potentiate autoinflammation in very old individuals. However, further investigations will be required to elucidate the basis of these findings.

Experimental procedures

  1. Top of page
  2. Summary
  3. Introduction
  4. Results
  5. Discussion
  6. Experimental procedures
  7. Acknowledgments
  8. Conflict of interest
  9. Author contributions
  10. References
  11. Supporting Information

Study population

The study population consisted of n = 144 nonagenarians (101 women and 43 men) participating in the Vitality 90+ Study, which is an ongoing prospective study involving individuals aged 90 years and older, living in the city of Tampere, Finland. The individuals in the current study were born in 1920 and were recruited and characterized as in the previous Vitality 90+ Study cohorts (Goebeler et al., 2003). A home-visiting trained medical student performed the blood tests, physiological measurements, interviews, and performance tests. The data concerning the recent infections in the subjects were elicited using a questionnaire, and if the subject reported suffering from any infectious disease during the last 2 weeks, the subject was excluded from the analyses in this study. Written informed consent was obtained from each participant and the study protocol followed the guidelines of the Declaration of Helsinki. The Ethics Committee of the Pirkanmaa Hospital District and the Ethics Committee of the Tampere Health Center approved the study protocol. The control subjects (n = 30, 21 women and 9 men aged between 19 and 30 years) consisted of healthy laboratory personnel who had no diagnosed chronic diseases and had not suffered from any infectious diseases within the previous 2 weeks.

Biochemical measurements

The EDTA blood was collected and the plasma was separated by centrifugation for 15 min at 700 g followed by transfer to new tubes and centrifugation for 15 min at 1000 g. The plasma was stored at −70 °C. The total cf-DNA was measured directly in plasma using a Quant-iT™ DNA high-sensitivity assay kit and a Qubit® fluorometer (Invitrogen, Carlsbad, CA, USA) in accordance with the manufacturer's instructions. The samples were analyzed in duplicate, and the mean of the two measurements was used as the final value. For the mean cf-DNA level of 0.593 μg ml−1, the intra- and interday variation coefficients for the Quant-iT™ DNA high-sensitivity assay were 1.9% and 4.7%, respectively, and were 2.3% and 5.2%, respectively, for the mean cf-DNA level of 1.007 μg ml−1. The amount of unmethylated cf-DNA was determined by quantifying the 5-methyl-2-deoxy cytidine using the DNA Methylation EIA Kit (Cayman Chemical Company, Ann Arbor, MI, USA, Cat. no. 589324) and subtracting the resulting value from the total cf-DNA concentration. For the quantitative PCR (qPCR)-based assessment of the genomic equivalents (GEs) of the RNase P-coding cf-DNA and the Alu repeat cf-DNA and the mtDNA copy number, the QIAamp DNA Blood Mini kit (Qiagen, Hilden, Germany) was used to extract the plasma cf-DNA from 200 μl of plasma. The amount of input cf-DNA for each of the qPCR assays was 2 μl, which was determined by assessing the linear range for each of the qPCR assays and testing the reactions for inhibition. The GE quantity of the single-copy gene, RNase P, was determined using the RNase P detection reagents (Applied Biosystems, Foster City, CA, USA, part no. 4316831). The TaqMan qPCR protocol to quantify the Alu repeat cf-DNA was adapted from a previously described protocol (Stroun et al., 2001). The primer sequences used were Alu F 5′-GGAGGCTGAGGCAGGAGAA-3′ and Alu R 5′-ATCTCGGCTCACTGCAACCT-3′, and the probe sequence was 5′-(FAM)CGCCTCCCGGGTTCAAGCG-3′. The standard curve for the RNase P and Alu repeat detection assays was constructed using Human Genomic control DNA (Applied Biosystems, part no. 4312660). The mtDNA copy number was determined with TaqMan qPCR using the following primers: hmitoF 5′-CTTCTGGCCACAGCACTTAAAC-3′ and hmitoR 5′-GCTGGTGTTAGGGTTCTTTGTTTT-3′, and a probe 5′-(FAM)ATCTCTGCCAAACCCC-3′ (described in (Malik et al., 2011)). The standard curve for the assessment of the mtDNA copy number was constructed using a purified mitochondrial genome (Standard Reference Material no 2392-I, National Institute of Standards and Technology, Gaithersburg, MD, USA). The qPCR assays were run with the ABI PRISM® 7900 HT Sequence Detection System with 40 cycles of amplification under standard cycling conditions (2 min at 50 °C, 10 min at 95 °C, 15 s at 95 °C, and 1 min at 60 °C.

The plasma CRP concentration was measured using the Human CRP Immunoassay (Quantikine ELISA, R&D Systems, Minneapolis, MN, USA). The plasma IL-6 concentration was measured using the PeliKine human IL-6 ELISA kit (Sanquin Reagents, Amsterdam, the Netherlands). The plasma IL-10 concentration was determined with the PeliKine Compact™ human IL-10 ELISA kit (Sanquin Reagents).

RNA extraction and whole-genome transcriptomic analysis

The blood samples were subjected directly to leukocyte separation using Ficoll-Paque density gradients (Ficoll-Paque™ Premium, GE Healthcare Bio-Sciences AB, Uppsala, Sweden). The PBMC layer was collected, and the cells were suspended in 150 μl of RNAlater solution (Ambion Inc., Austin, TX, USA) and stored at −70 °C until analyzed. The RNA was extracted using the miRNeasy Mini Kit (Qiagen) with on-column DNase digestion (AppliChem GmbH, Darmstadt, Germany). The concentration and quality of the RNA was assessed using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). The Illumina TotalPrep RNA amplification kit (Ambion Inc.) was used to amplify 330 ng RNA for hybridization on the HumanHT-12 v4 Expression BeadChip (Cat no. BD-103-0204; Illumina, San Diego, CA, USA) at the Core Facility of the Department of Biotechnology, University of Tartu, Estonia. The quality of the biotinylated complementary RNA (cRNA) products was determined with Agilent 2100 Bioanalyzer (Agilent Technologies Inc., Santa Clara, CA, USA). The chips were scanned using Beadscan (Illumina Inc., CA, USA).

The bioinformatic microarray data analysis was performed using the Chipster v2.0 software (http://chipster.csc.fi/) (Kallio et al., 2011). The quality of the data was confirmed using the box blot, density blot, and principal component analyses. For the data preprocessing and normalization, the lumi pipeline was used, and the Array_Address_ID was used as the probe identifier. The background correction was performed using the bgAdjust.affy package, and the data were log2-transformed to normality. To filter out the bad-quality data, the preprocessed data were filtered by expression (fluorescence intensity), that is, the probes with an expression value of <5 or >100 were removed from the analysis. To identify the transcripts exhibiting expression levels correlating with the concentrations of total cf-DNA, unmethylated cf-DNA, and plasma mtDNA copy number, the concentrations of the cf-DNA species were log-transformed and correlated with Pearson's rho using Chipster's correlate with phenodata tool, which assigns each transcript a correlation coefficient (from −1 to +1, without a p-value). The microarray expression correlation analyses were performed out separately for the nonagenarians and the control subjects, and from each of the analysis, the 250 transcripts that were best correlated were transferred to the Ingenuity Pathway Analysis (IPA, http://www.ingenuity.com). The IPA canonical pathway analysis identified the pathways from the IPA library of canonical pathways that were most significant to each of the data set containing the 250 correlated transcripts. The significance of the association between the data set and the canonical pathway was measured in 2 ways. (i) The ratio of the number of molecules from the data set that map to the pathway divided by the total number of molecules in the given pathway. (ii) Fisher's exact test was used to calculate a p-value determining the probability that the association between the transcripts in the data set and the canonical pathway is explained by chance alone. A significance level of p < 0.05 was considered statistically significant. The correlation coefficients of the transcripts were identified in the results as follows: the transcripts with expression levels correlated directly with the levels of total cf-DNA, unmethylated cf-DNA, or mtDNA copy number are identified in red, and the transcripts with expression levels correlated indirectly with levels of total cf-DNA, unmethylated cf-DNA, or mtDNA copy number are identified in green. The resulting canonical pathways were filtered to exclude disease-specific pathways and pathways not relevant to PBMCs.

Flow cytometry

The flow cytometric analysis of the different leukocyte subtype distributions has been described in detail (Marttila et al., 2011). Briefly, the PBMCs were separated by Ficoll-Paque density gradient (Ficoll-Paque™ Premium, GE Healthcare Bio-Sciences AB) and stored in liquid nitrogen until analysis using flow cytometry (BD FACSCanto II, the results were analyzed using the BD FACS Diva, version 6.1.3, BD Biosciences, Franklin Lakes, NJ, USA). The antibodies used to label the cells were FITC-CD14 (cat. no. 11-0149), PerCP-Cy5.5-CD3 (45-0037), APC-CD28 (17-0289) (eBioscience, San Diego, CA, USA), PE-Cy™7-CD4 (cat. no. 557852), and APC-Cy™7-CD8 (557834) (BD Biosciences).

Physiological measurements and the assessment of functional performance and frailty

The frailty score for the nonagenarians was assessed based on the criteria outlined by Fried et al. (Fried et al., 2001). The points for calculating the frailty score (min 0, max 5) for each individual were assessed as follows: if the individual met the criteria/threshold value in any of the five assessment steps, he/she was awarded one point in each criterion, and the points were summed to yield the frailty score. The criteria yielding the frailty points were as follows: (i) Mini-Mental State Examination (MMSE) score ≤ 22; (ii) weight loss of ≥ 10% of body weight in the previous 2 years or BMI < 18.5 kg m−2; (iii) self-reported fatigue (the individual reported that he/she felt fatigued ‘often’ in a questionnaire in which the options were feeling tired ‘often,’ ‘sometimes,’ or ‘never’); (iv) low hand grip strength [the maximum hand grip strength of the hand primarily used was in the lowest sex- and BMI-specific quartile, in accordance with Fried et al. (Fried et al., 2001)]; and (v) low moving capability (the individual was not able to walk independently on a level surface or on stairs, as assessed by the Barthel index points for ‘mobility’ and ‘stairs’, that is, the summed score of these two values was <25).

The methods for the assessment of MMSE, chair stand test, handgrip strength, and Barthel index have been previously described (Jylha et al., 2007; Tiainen et al., 2010).

Statistical analyses

The differences in the distributions of the study variables between the nonagenarians and young controls were analyzed using the Mann–Whitney U-test or Student's t-test where appropriate. The correlations between the cf-DNA species and the biochemical and physiological variables were analyzed using Spearman's rho. The statistical analyses were performed using the IBM SPSS Statistics version 19 (IBM Corp., Sommers, New York, USA).

Acknowledgments

  1. Top of page
  2. Summary
  3. Introduction
  4. Results
  5. Discussion
  6. Experimental procedures
  7. Acknowledgments
  8. Conflict of interest
  9. Author contributions
  10. References
  11. Supporting Information

This study was supported financially by The Academy of Finland (M.H.) and The Competitive Research Funding of the Tampere University Hospital (M.H. grant 9M0179). The authors would like to thank Ms. Sinikka Repo-Koskinen, Ms. Katri Välimaa, and Ms. Sanna Tuomisaari for their skillful technical assistance.

Author contributions

  1. Top of page
  2. Summary
  3. Introduction
  4. Results
  5. Discussion
  6. Experimental procedures
  7. Acknowledgments
  8. Conflict of interest
  9. Author contributions
  10. References
  11. Supporting Information

J.J. collected the samples, designed the experiments and performed most of the experiments, analyzed the data, and wrote the manuscript. T.N. performed some of the experiments. S.M. collected the samples and performed some of the experiments. M.J. was responsible for the Vitality 90+ Study design and cohort recruitment. A.H. was responsible for the Vitality 90+ Study design and cohort recruitment. M.H. designed the experiments and provided the facilities and reagents.

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  3. Introduction
  4. Results
  5. Discussion
  6. Experimental procedures
  7. Acknowledgments
  8. Conflict of interest
  9. Author contributions
  10. References
  11. Supporting Information
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Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Results
  5. Discussion
  6. Experimental procedures
  7. Acknowledgments
  8. Conflict of interest
  9. Author contributions
  10. References
  11. Supporting Information
FilenameFormatSizeDescription
acel12058-sup-0001-Supporting Experimental.docWord document27KData S1 Experimental procedures.
acel12058-sup-0002-TableS1-S6.docWord document2247K

Table S1 The 250 transcripts with expression levels correlating with plasma total cf-DNA level in the nonagenarians (a) and young controls (b).

Table S2 The 250 transcripts with expression levels correlating with plasma unmethylated cf-DNA level in the nonagenarians (a) and young controls (b).

Table S3 The 250 transcripts with expression levels correlating with plasma mtDNA copy number in the nonagenarians (a) and young controls (b).

Table S4 The significant IPA canonical pathways harbored by the transcripts that exhibited expression levels correlating with the plasma total cf-DNA level in the nonagenarians (a) and young controls (b).

Table S5 The significant IPA canonical pathways harbored by the transcripts that exhibited expression levels correlating with the plasma unmethylated cf-DNA level in the nonagenarians (a) and young controls (b).

Table S6 The significant IPA canonical pathways harbored by the transcripts that exhibited expression levels correlating with the plasma mtDNA copy number in the nonagenarians (a) and young controls (b).

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