EVALUATION OF THE CELL-FREE MITOCHONDRIAL DNA QUANTITY IN PLASMA AS AN AGING BIOMARKER CANDIDATE

Master’s Thesis Tapio Nevalainen Institute of Biomedical Technology University of Tampere 2012

ACKNOWLEDGEMENTS This Master’s Thesis was carried out in the Department of Microbiology and Immunology, School of Medicine at the University of Tampere, Finland. First of all, I would like to express my greatest gratitude to group leader Mikko Hurme for giving me this great opportunity and for all the guidance and patience as what comes to finishing this thesis. I would also like to thank my other supervisor, Juulia Jylhävä for her outstanding expertise and guidance throughout this project. I also want to thank other group members, Saara Marttila and Taru Kuparinen for all the help. Special thanks belong to Sinikka Repo-Koskinen for all the assistance in laboratory.

Lastly, I am deeply grateful for my family and friends for all the support they have provided during my studies.

Tampere, June 2012 Tapio Nevalainen

PRO GRADU -TUTKIELMA Paikka: Tekijä: Otsikko: Sivumäärä: Ohjaajat: Tarkastajat: Aika:

TAMPEREEN YLIOPISTO Biolääketieteellisen teknologian yksikkö (IBT) NEVALAINEN, TAPIO LAURI JUHANI Plasman soluvapaa mitokondriaalinen DNA kandidaatti ikääntymisen biomarkkeriksi. 62 Professori Mikko Hurme ja FM Juulia Jylhävä Professorit Markku Kulomaa ja Mikko Hurme Kesäkuu 2012

Tiivistelmä Tutkimuksen tausta ja tavoitteet: Saman lajin yksilöiden ikääntymisnopeuksien välillä esiintyy huomattavaa vaihtelua, erityisesti vanhuksien keskuudessa. Tämän vuoksi kronologista ikää pidetään epäsopivana mittarina arvioimaan yksilön toiminnallista kykyä ja sen sijaan ehdotetaankin käytettäväksi biologista ikää. Jotta biologinen ikä voitaisiin määrittää luotettavasti, tarvitaan kuitenkin mitattavissa olevia ikäsidonnaisia fysiologisia paremetreja, ikääntymisen biomarkkereita. Aiemmin on osoitettu, että soluvapaan DNA:n pitoisuus plasmassa on kohonnut vanhoilla yksilöillä. Tämän tutkimuksen tarkoituksena oli analysoida soluvapaata DNA:ta tarkemmin kvantifioimalla sen osakomponentit, nukleaarinen ja mitokondriaalinen DNA, erikseen. Materiaalit ja menetelmät: Näytteinä käytettiin plasmaa ja perifeerisen veren mononukleaarisoluja, jotka oli kerätty Vitality 90+ - tutkimukseen osallistuvilta 90vuotiailta yksilöiltä. Kontrolliryhmä koostui nuorista, terveistä laboratoriotyöntekijöistä. Soluvapaa DNA eristettiin plasmanäytteistä ja totaali DNA perifeerisen veren mononukleaarisoluista. Kunkin näytteen nukleaarisen ja mitokondriaalisen DNA:n kopioluvut määritettiin erikseen kvantitatiivisella PCR:llä käyttäen spesifisiä alukkeita. Mitokondriaalisen DNA:n kopioluvut suhteutettiin nukleaarisen DNA:n kopiolukuihin ja niitä verrattiin ryhmien sisällä ja välillä. Tulokset: Vanhusten ja kontrollien mitokondriaalisen DNA:n kopioluvuissa plasmassa ei havaittu merkittävää eroa. Sen sijaan nukleaarisen DNA:n tasot vanhuksilla olivat selvästi matalammat. Johtopäätökset: Ensisilmäyksellä vaikuttaa, että mitokondriaalisen DNA:n määrä plasmassa on riittämätön parametri käytettäväksi ikääntymisen biomarkkerina. On kuitenkin huomionarvoista, että tässä tutkimuksessa tehdyt mittaukset tehtiin kahdelle ääripäissä oleville kohderyhmille, eikä lineaarista kronologisen iän korrelaatiota voi siten osoittaa. Lisätutkimukset ovat tarpeellisia, jotta voidaan osoittaa, miten soluvapaan mitokondriaalisen DNA:n pitoisuus plasmassa käyttäytyy erilaisten vanhenemisen fenotyyppien kanssa.

MASTER’S THESIS Place: Author: Title: Pages: Supervisors: Reviewers: Date:

UNIVERSITY OF TAMPERE Institute of Biomedical Technology (IBT) NEVALAINEN, TAPIO LAURI JUHANI Evaluation of the cell-free mitochondrial DNA quantity in plasma as an aging biomarker candidate. 62 Professor Mikko Hurme and Juulia Jylhävä, MSc Professors Markku Kulomaa and Mikko Hurme June 2012

Abstract Background and aims: Individuals of the same species may have large variations in their ageing rates, especially notable at higher ages. Therefore, it is proposed that chronological age is not suitable for measuring ones functionality, but instead the concept of biological age should be used. To define individuals biological age the biomarkers of aging have to be first established. In a previous study, the cell-free DNA concentration in plasma was reported to be higher in older individuals, making it potential aging biomarker candidate. The aim of this study was to further analyze the cell-free DNA. This was carried out by quantifying nuclear and mitochondrial DNA, the two fractions of cell-free DNA, separately. Materials and methods: The plasma and PBMC samples were collected from the participants of the Vitality 90+ study who were of 90 years of age. Control group consisted of healthy young laboratory personnel. The cell-free DNA was extracted from plasma samples as well as total DNA of the PBMC. The nuclear and mitochondrial DNA copy numbers of all samples were determined separately with quantitative PCR by using specific primers. The obtained mitochondrial DNA copy numbers were scaled to nuclear DNA copies and compared within and between the groups. Results: No significant difference in plasma mitochondrial DNA quantity was observed between nonagenarians and controls. In contrast, the plasma of nonagenarians contained significantly higher levels of nuclear DNA. Conclusions: The mitochondrial DNA quantity in plasma seems to be inadequate parameter to be used as an aging biomarker. However, because only two distinct groups were compared: the young and the very old individuals, the obtained data does not address the issue of a linear correlation between chronological age and mtDNA quantity in plasma. Additional studies are also needed to demonstrate whether there are differences in plasma mtDNA quantity between the successfully and unsuccessfully aged individuals.

TABLE OF CONTENTS

1. Introduction ................................................................................................................. 8 2. Review of the literature.............................................................................................. 10 2.1 Essence of aging .................................................................................................. 10 2.1.1 Successful aging vs. unsuccessful aging ............................................................ 11 2.1.2 Aging and longevity.......................................................................................... 11 2.1.3 Biological age and biomarkers of aging............................................................. 14 2.2 Cell-free DNA ..................................................................................................... 16 2.2.2 History of cell-free DNA in circulation ............................................................. 17 2.2.3 Cell-free DNA in circulation ............................................................................. 17 2.2.4 Origin of cell-free DNA .................................................................................... 18 2.2.5 Cell-free DNA kinetics ..................................................................................... 20 2.2.6 Implications of cell-free DNA ........................................................................... 21 2.2.7 Applications ...................................................................................................... 22 2.2.8 Cell-free mitochondrial DNA and aging process ............................................... 23 2.2.8.1 Overview of the mitochondrial DNA.............................................................. 23 2.2.8.2 Mitochondrial DNA damage .......................................................................... 24 3. Aims of the study ...................................................................................................... 26 4. Materials and methods ............................................................................................... 27 4.1 The study population and the samples .................................................................. 27 4.1.1 Collection and the processing of the blood samples ........................................... 27 4.2 DNA extraction ................................................................................................... 28 4.2.1 Extraction of the cell-free DNA from plasma samples ....................................... 29 4.2.2 Extraction of the DNA from the PBMC:s .......................................................... 30 4.3 Determining the concentration of the DNA in plasma and PBMC eluates ............ 30 4.4 DNA quantification by qPCR............................................................................... 31 4.4.1 Quantification of mitochondrial DNA from plasma ........................................... 32 4.4.2 Quantification of genomic DNA from plasma ................................................... 33 4.4.3 Quantification of DNA content in the PBMC:s.................................................. 34 4.5 Copy number processing and statistical analysis .................................................. 35 5 Results........................................................................................................................ 36 5.1 DNA concentrations in plasma show no difference between nonagenarians and controls...................................................................................................................... 36

5.2 qPCR ................................................................................................................... 37 5.2.1 The nonagenarians have elevated genomic DNA levels ..................................... 37 5.2.2 There is no difference in the plasma mitochondrial DNA copy numbers between the nonagenarians and controls .................................................................... 40 5.2.3 The mitochondrial DNA to genomic DNA - ratio is reduced in the PBMC:s of the nonagenarians ...................................................................................................... 42 6. Discussion ................................................................................................................. 43 6.1 The study population ........................................................................................... 43 6.2 Extraction of cell-free DNA from plasma samples ............................................... 44 6.3 Quantitative PCR ................................................................................................. 45 6.3.1 Cell-free DNA in plasma................................................................................... 46 6.3.2 DNA content in PBMCs.................................................................................... 47 6.3.3 Comparison of the mtDNA/nDNA ratio in plasma and PBMCs......................... 48 7. Conclusions ............................................................................................................... 50 8. References ................................................................................................................. 52

Abbreviations cf-DNA

cell-free DNA

DMSO

dimethyl sulfoxide

PBS

phosphate-buffered saline

PBMC

peripheral blood mononuclear cell

ROS

reactive oxygen species

FBS

fetal bovine serum

FAM

6-carboxy fluorescein

IQR

interquartile range

RT-PCR

real-time polymerase chain reaction

NTC

no template control

NET

neutrophil extracellular trap

1. Introduction Ageing is one of the most enigmatic mysteries still to baffle the modern scientists. Why do we age and why lifespans between species differ so much? Even the individuals of the same species do not age at equal speed (Harman 2003). Viewing aging from an evolutionary aspect, it just does not make sense; why would something as sophisticated as life is today, which has taken billions of years to evolve (Spiegel & Turner 2012), be incapable of holding itself together more than just few decades. Actually, this deliberation is futile as long as observing it from human point of view, since we tend to think inside the box. Humans, just like any other life forms, are evolved for one reason; the reproduction. In the light of evolution, we have no other purpose but to produce offspring after which we can slowly decay away from using valuable resources that the young ones need to fulfill their part of the destiny (Gavrilov & Gavrilova 2002). That is how nature works and we have to deal with it.

The statement above might sound harsh as it is intentionally generalized. It is probably true for less evolved species, but not necessarily in the case of humans. As a consequence of our self-awareness and intelligence we have partly get past the selecting pressure of evolution by being able to set up safe environments. (Gavrilov & Gavrilova 2002) This distinguishes us from wild animals and it has greatly affected our yet increasing median age which is actually about to become a problem.

The age structure of the human population is about to undergo a major shift in the near future. Today around 11 % of the population is of age 60 or older median age being 28 years and it has been estimated that these numbers will be 22 % and 38 years by the year 2050 (Figure 1) (United Nations 2009). In fact, the fastest growing parts of population are those aged 80 years or over owing to even developing medical knowledge even though the maximum lifespan is not likely to increase. As the people get older, several age-related health issues appear, some of which are resulting from the actual aging process whereas others, the age-related diseases, are the real problem showing high incidence amongst older individuals (Campisi et al., 2009). This will have an enormous impact in economic system, especially in health care services as they will have to deal more and more with older people and giving the optimal treatment for each individual becomes extremely important (United Nations 2009). 8

Figure 1. A graph depicting the proportion of 60 years or older in world population. The number of people in this group is predicted to double in the next 40 years (Data modified from United Nations, 1998).

The problem described above has been recognized for some time and the possible solutions have been investigated ever since. Funds currently spent on ageing research has been criticized to be insufficient and it is believed that the greater investments would pay themselves back as the health care costs might undergo significant reductions if breakthrough is made (Harrison & Bronson 2003). To overcome this huge incoming health burden, anti-aging interventions have been proposed (Yu 1999). Ideally this would be a system that could predict the onset of age-related diseases on individual level well before the symptoms occur and allow interventions to be made (Golden et al., 2009). This idea has aroused numerous studies trying to discover the biomarkers of aging. With the aid of these biomarkers the individuals having greater risk for developing age-associated diseases could be identified (Simm et al., 2008). Additionally, successful aging biomarkers would most probably also give hints how the actual aging mechanism works.

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2. Review of the literature 2.1 Essence of aging Even though the exact molecular cause ultimately behind the aging process remains unknown, the physiological manifestations observed as a result are well defined in human species (Baker & Sprott 1988). Aging is characterized by several physiological alterations in different body systems including general decrease of function in cardiovascular, pulmonary, endocrine, renal, immune and nervous systems (Aalami et al., 2003). In addition, many specific changes such as progressive loss of muscle mass (Combaret et al., 2009) and loss of hearing and vision are well-known. These alterations and changes are considered normal as a natural part of aging process, so called primary aging. However, some of these changes are also risk factors, being precursors for aging-related diseases like diabetes, osteoporosis and Alzheimer disease (Rowe & Kahn 1987, Aalami et al., 2003). The age-associated diseases resulting, not from the actual aging process, but increased sensitivity towards them are termed secondary ageing. Together, these changes lead to decrease of function in tissues and ultimately to functional impairment (Anstey et al., 1999).

The very essence of aging process has undergone a major shift in the past decades as previously it was thought as a chain of events initiating from the actions of a single gene or a decline in one essential body system. Nowadays, the current, widelyaccepted consensus is that aging is extremely complex process with several affecting factors on multiple levels (Weinert & Timiras 2003), and it is likely that the outcome is the result of complicated interplay between genetic, epigenetic and environmental components (Capri et al., 2006). However, as discussed below, single genes may play important roles in aging process and have great effects in individuals’ lifespan.

Given the complex nature of the aging process, there are lots of different aging theories explaining the reason for getting old. Single theories such as free radical theory (Harman 2003), mitochondrial theory (Wiesner et al., 2006) and immunologic theory (Franceschi et al., 2000) all focus on one aspect of the aging process. Therefore, it cannot be stated that one theory is right or wrong, but they are rather complementary entities (Weinert & Timiras 2003). In conclusion, aging can be thought as the sum of several different kinds of deleterious changes taking place in 10

cells and tissues as the individual gets older. Together these alterations increase the occurrence of age-associated diseases and risk of death (Harman 2003). Although well characterized, the aging process is far from being understood. What makes it so difficult to study, is the vast complexity and variety of the sub-processes that may or may not be interconnected. In addition, the adverse effects observed as a part of natural aging process are difficult to separate from those of the age-related diseases (Buffenstein 2008).

2.1.1 Successful aging vs. unsuccessful aging Successful aging is a relatively new multidimensional concept including aspects from sociology, psychology, and physiology (Phelan & Larson 2002). The essence of term has been widely discussed for 4 decades and it does not have one standardized definition yet due to its complicated nature of every branch of science having own point of views (Phelan & Larson 2002). When trying to define successful aging and focusing on physiological point of view, it is necessary to make some specifications. Firstly, it is important to make difference between pathological aging, which includes age-associated diseases, and aging as a normal process. Secondly, as there appears to be heterogeneity among the normal agers, this category is still divided into usual and successful (Rowe & Kahn 1987). Individuals termed usual agers exhibit normal nonpathological changes characteristic for aging process such as loss of hearing and vision, and alterations in blood pressure, bone density and pulmonary function (Rowe & Kahn 1987). Successfully aging individuals by contrast, suffer only little of these changes when compared to average (Schulz & Heckhausen 1996). Based on these classifications the successful aging can be defined as having low probability of disease and disease-related disability and high cognitive and physiological functionality (Rowe & Kahn 1997). 2.1.2 Aging and longevity Gazing across the vast spectrum of different species, it appears that the humans and the animals under our protection are one of a few actually facing the aging issue. Most of the species live in environments where they have to deal with the predators, limited food supply and diseases and due to this the old individuals living in their natural environment are rare (Hayflick 2000). However, the human species has succeeded to 11

overcome much of the pressure that environment puts on us in terms of highly developed technology; In the absence of the predators and having plenty of food supplies to begin with, somewhat high ages are reached (Hayflick 2000). Adding the developed medicine and other welfare services, extremities of the human life span can be reached more readily. The life expectancy is defined as the total number of years that individual lives on average. Life span, on the other hand, is the maximum number of years that an individual of the human species can live. The life span of humans has remained approximately the same for the last 100,000 years whereas the life expectancy has increased notably in the last 100 years (Hayflick 2000).

Longevity is no doubt the most challenging trait to study in a living organism. Not only because it takes ones lifespan to evaluate but also because it is affected by so many factors. Every life-form has a set of biochemical mechanisms that have evolved just to improve longevity. Also diseases and environment have an effect, modifying the outcome (Cournil & Kirkwood 2001). As ultimate examples are the mitochondrial DNA haplogroups, in which a few single nucleotide polymorphism have been reported to associate with longevity (Niemi et al., 2005). Many animal studies have been performed where genetic components of aging has been attempted to discover. These longevity studies have focused on using easily manipulatable species, such as C. Elegans and D. Melanogaster, which having considerably short lifespans allow the testing to be done in reasonable timeframe (Van Zant & de Haan 1999).

Studies in yeast, Saccharomyces cerevisiae, have revealed that the overexpression of Sir2, a histone deacetylase, extends its life span (Kaeberlein et al., 1999). In addition, alterations in the expression of oncogene RAS, increase the longevity of the yeast (Chen et al., 1990). These two examples demonstrate that it is likely, that longevity is at least at some degree specified at gene regulatory level (Table 1). This is further supported by additional experiments with different animal models. Lin et al. (2001) have shown that the Caenorhabditis elegans having mutations in insulin-like growth factor (IGF) -1 pathway may live twice as long as the wild-type worm also retaining the youthful phenotype. It is noteworthy that IGF-1 pathway is known to regulate life spans in flies and mammals as well, making it potential universal longevity regulator (Lin et al., 2001). The Chico gene in flies encodes substrate for insulin receptor and its’ knockout leads to increase in life span (Clancy et al., 2001). The overexpression 12

of superoxide dismutase (SOD) in Drosophila melanogaster resulted in prolonged lifespan with decreased oxidative damage in proteins and relatively better physical performance in older age (Orr & Sohal 1994). The studies involving the neutralization of reactive oxygen species clearly indicate that those are related to aging process. However, similar results has not been obtained in the case of mammals so far. The mutation in p66shc, a cytoplasmic signal transducer, has been reported to increase the life span of the mice by 30 % with improved resistance to environmental stress. It is suggested that the increased viability followed by this mutation is due to improved resistance to oxidative stress (Migliaccio et al., 1999). Trifunovic et al. (2004) have demonstrated with mice that induced mutation that renders mitochondrial DNA polymerase gamma error-prone, gives rise to individuals with highly mutated mitochondrial DNA. The observed phenotype of these mice is astounding; premature aging with all typical characteristics of the aging individual and reduced life span (Trifunovic et al., 2004). The role of tumor suppressor p53 is well known in many critical cellular processes such as cell cycle, apoptosis, DNA repair and cellular senescence (Ghosh et al., 2004). Tyner et al. (2002) produced a p53 mutant mouse that possessed increased p53 activity. These mice were reported to have increased resistance against tumours but on the other hand, they showed characteristics of premature ageing. This is good example of antagonistic pleiotropy theory of aging, where specific gene is thought to confer positive advantage at early stage of the life, mainly during the reproducing period and negative effects later on (Ungewitter & Scrable 2009).

In humans, the studying of longevity genes is much more challenging as the genetic manipulation is out of question. Instead, the studies focus on large populations and comparison of the groups between which there are natural variation. Until now, some potential longevity genes have been discovered. The apolipoprotein E (apoE) is an example of a gene that is present in three common variants. It has been reported that one specific variant, apoE4, is associated with decreased longevity which has brought forth an idea that these variants may play a role in successful aging phenomenon (Smith 2000). Similar results have been obtained from the SIRT3 gene (Rose et al., 2003), which is the homolog of yeast Sir2 and the YTHDF2 gene (Cardelli et al., 2006). Additionally, mtDNA haplogroups are associated with longevity as the J 13

haplogroup is most frequent amongst centenarians (de Benedictis et al., 1999). (Table 1) Table 1. The examples of longevity genes associated with different species. All genes are discussed in the text.

Longevity genes

S. cerevisiae [1]

C. elegans [2]

Sir2 RAS

IGF1/insulinpathway Age1/Daf-16

D. melanogaster [3] M. musculus [4] SOD Chico

IGF1/insulin pathway p66shc POLG p53

H. sapiens [5] APOE SIRT3 IGF1R YTHDF2 mtDNA variants

2.1.3 Biological age and biomarkers of aging Traditionally the age of an individual, being the time from the birth to the present time in years, is used as rough indicator to predict the onset of certain age-related diseases and also as a predictor of life-expectancy (Burgin 2004). However, this method is not really rational as there appears to be significant heterogeneity amongst the aging rates of the individuals. Individuals of the same age can sometimes look stunningly different which also most probably reflects the functional state of the organ systems (Jackson et al., 2003). The difference in aging rates is not that prominent in younger ages but as the people aged over 70 years are concerned, the group is extremely heterogenic; there are extremely fit and highly functional individuals amongst the nonagenarians, whereas some 20 years younger individuals have already diminished functional capacity even in the absence of any detectable disease (Goffaux et al., 2005, Baker & Sprott 1988). This phenomenon has been recognized for decades ago and the term biological age has been proposed to better predict the actual functionality of the individual rather than the conventional chronological age (Goffaux et al., 2005).

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To measure the biological age, the concept of biomarkers of aging has been established. However, discovering the biomarkers of aging turned out to be extremely challenging as there is no single accepted definition for the aging giving the rather poor theoretical understanding to begin with. During the first aging biomarker studies, the results seemed to indicate that chronological age appears to be as good predictor of the functionality and biological age than any physiological or biochemical parameter whether analyzed alone or in combination. (Costa & McCrae 1980) However this did not shut down the emerging studies, but rather led to the more precise adjustments of definitions and validations for biomarkers (Baker & Sprott 1988). Due to the assumed complexity of the aging process, it is estimated that it is the panel of biomarkers rather than any single one that will be the successful predictor of the biological age (Sprott 2010).

When it comes to biomarkers of aging, an important point to consider is a difference between aging process and disease. Pessimistic view would be that aging is simply the sum of damage resulting from all the diseases so far rendering the concept of biomarker of aging meaningless. Another way to look at it is that aging is one underlying process of its own and the diseases are separate entities partly masking the essence of aging. (Sprott 2010) Clearly, the existence of the progerias, the genetic conditions of individuals exhibiting the features of accelerated aging suggest that aging is not just the due to the damage accumulation from diseases but rather a separate process (Fossell 2003). Due to the intertwined relationship between aging and disease, it is essential that the potential parameter of interest measures the changes in actual aging process but not the changes in diseases (Baker & Sprott 1988). This is all based on a fundamental that aging is not a disease and it will not cause a death in any circumstances (Hayflick 1998).

Specific criteria have been established for the potential aging biomarkers. The guidelines proposed by the American Federation for Aging Research state that biomarker of aging should be able to predict the rate of aging by giving information where the individual is at his or hers lifespan and doing this better than chronological age (Johnson 2006). Secondly, the biomarker of aging should be related to basic aging process instead of the disease as these two may or may not be connected. Finally,

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obtaining the biomarker should be minimally invasive allowing the retesting and it should also be able to work in both humans and laboratory animals (Johnson 2006).

An extra challenge to biomarker research is brought by the possibility that a predictor value of single aging biomarker may change as a function of the age, meaning that successful biomarker at younger ages may become unsuccessful later on. When applying this to the whole picture of panel of several biomarkers things get really complicated (Sprott et al., 2010).

The ultimate motivation behind the biomarker research is that their successful identification would allow the interventions to be made. According to Baker and Sprott (1988) there are three kinds of possible interventions. Two of these are more general and they would increase the maximum lifespan and mean lifespan. The third one is special and it follows from the multifactorial nature of aging process. It would allow the interventions targeted specifically to single sub-components of aging such as bone loss, sarcopenia, immune system dysfunction and hence leading to better functionality. (Baker & Sprott 1988) In fact, there are already several of this kind of interventions in use although their scientific foundation is controversial. Oxidative stress is known to cause damage in cellular level and it is proposed as one of the main factors that drive aging process forward. Due to these, caloric restriction and antioxidant supplements are recommended. Biomarker research aims for the same kind of results. Although aging is not considered as a disease, it is sometimes referred as a process that could be curable or slowed. It has been proposed that with medical intervention, increasing the maximum human life span could be possible (de Grey 2003).

2.2 Cell-free DNA The term cell-free DNA includes all endogenous DNA found in the extracellular environments inside the body. This DNA entity consists of the components of nuclear and mitochondrial DNA and they may appear associated with different kind of subcellular or molecular particles (Mittra et al., 2012). Cf-DNA has already been successfully established as a disease biomarker in a variety of pathologies (Gahan &

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Swaminathan 2008) and just recently, it has been suggested that cf-DNA may have potential to be the aging biomarker as well (Jylhävä et al., 2011). The cf-DNA has been described to be present in various body fluids, including blood, urine, saliva, feces, synovial fluid, cerebrospinal fluid and peritoneal fluid (Wagner 2012). This section introduces the cf-DNA as a whole. In the end of the chapter the mitochondrial cf-DNA is discussed briefly. 2.2.2 History of cell-free DNA in circulation The presence of extracellular DNA in circulation has been known since 1948, first reported by Mandel and Métais, even before the double-helical structure of DNA was shown by Watson and Crick (Lo 2001). However, this finding was ahead of its time as the molecular biology methods were not successful back then, unable to make more specific research. In following decades, high levels of cell-free DNA in circulation were demonstrated in certain autoimmune diseases as well as in cancer patients (Lo 2001). In 1990s, it was proven that majority of cell-free DNA in the blood of cancer patients was originating from cancer cells, given the tumor-specific alterations, awakening the global interest for potential cancer biomarker (Lo 2001). Also the fetal cell-free DNA in maternal circulation has been discovered, enabling certain test to be permitted (Gahan & Swaminathan 2008). The majority of the studies considering the cell-free DNA usually focus on nuclear DNA as it is the one that can be used in clinical applications. The cell-free mitochondrial DNA has been paid less attention and it tends to focus on aging studies. 2.2.3 Cell-free DNA in circulation The estimates of the cell-free DNA levels in healthy individuals vary widely as it is highly dependent on used extraction and quantification methods. In addition, the levels measured from serum and plasma are sometimes reported somewhat interchangeably, although the values are totally different. Cell-free DNA values in blood up to 100 ng/ml in healthy individuals are considered normal. There has been criticism for the use of serum in clinical applications, since it has been shown to contaminate easily from the DNA originating from the blood cells if not isolated immediately after blood withdrawal (Gormally 2007). According to Lee et al. (2001) the cell-free DNA levels in serum are 20-fold higher when dealing with recently 17

drawn samples. With increasing time between drawing and isolation, the values can rise to 100-fold from the original due to the contamination of DNA from the blood cells. Therefore, the plasma as a source of cell-free DNA quantification has been recommended. (Lee et al., 2001) 2.2.4 Origin of cell-free DNA The origin of cell-free DNA is obvious in the cases like cancers as the tumor-specific alterations can be evidently seen (Lo 2001). However, as the blood of healthy individuals also contains detectable amounts of cf-DNA, it raises a question concerning the source of this non-pathologically appearing component (Anker & Stroun, 2000). Majority of the circulating, nuclear cell-free DNA shows the characteristics of the apoptotic DNA fragmentation as indicated by the lengths in multiples of nucleosomal DNA (Bischoff et al., 2002). Indeed, apoptosis following from the normal turnover of cells is regarded as one of the main sources of the cfDNA resulting in estimated 7-97 % of the total circulating DNA (Tamkovich et al., 2008) (Figure 2). Additionally, the increased cf-DNA concentrations in the plasma of cancer patients are thought to be mainly due to the apoptosis of neoplastic cells as evident by increased amount of DNA of nucleosomal length when compared to healthy individuals (Giacona et al., 1998, Fournie et al., 1995). However, the problem in the apoptosis theory is that if the DNA originating form apoptotic cells is embedded in apoptotic vesicles following the phagocytosis and degradation by macrophages as a part of their normal fate (Nagata 2000), how such a large concentrations could be accounted for in the circulation. This has been explained to be due to the abundance of resulting apoptotic bodies that overwhelm the body’s capacity to clear them (Tamkovich 2008). Necrosis may be the main source of extracellular DNA in the cases of trauma or some other acute tissue injury (Lam et al., 2003).

Other sources have also been considered; erythrocyte maturation and the formation of the platelets from megakaryocytes are both processes that result in the degradation of the DNA within these postcellular structures rendering them anuclear (Tamkovich 2008). There are about 25-30 × 1012 circulating erythrocytes and 0,8-2,4 × 1012 platelets in the blood that have daily turnover rates of 1 % and 15 % respectively. This 18

DNA is normally phagocyted by macrophages but some of it is able to escape this pre-determined fate and end up circulating in the apoptotic bodies similar to those originating from apoptotic cells (Tamkovich 2008) (Figure 2).

In addition to passive release of DNA from the cells as described above, cells are also able deliver DNA into the circulation actively. This has been shown in cell culture experiments having elevated concentrations of DNA in the media without notable amount of apoptotic or necrotic cells (Gahan 2006). These experiments have been performed with the cells from frog heart, lymphocytes and chick embryo fibroblasts. An intriguing aspect of this phenomenon is that the released DNA appears to follow some kind of homeostatic mechanism. In these experiments the cells released specific amount of DNA into the medium reaching certain concentration. When incubated in fresh medium, cells released DNA in a way that same concentration was obtained again and again (Gahan 2006). In addition, this DNA was not of apoptotic origin as it was released by the living cells indicated by the absence of cell death markers. There are not so many studies proving that the same process actually takes place in vivo, but it is reasonable to assume so. Experiment made by Fournie et al. (1974) demonstrated that injection of bacterial lipopolysachharides in mice gave rise to increased cf-DNA concentrations. Lipopolysachharides have stimulating effect on lymphocytes accelerating their DNA release (Stroun et al., 1972). Active release of the DNA into the circulation has also been demonstrated as a part of innate immune system functioning (Wartha et al., 2007). Neutrophils are able to secrete molecular structures that contain protein and DNA. Interestingly DNA seems to play structural role in these so called Neutrophil extracellular traps (NETs). NETs are able entrap the pathogens and kill them by antimicrobial agents that it contains (Medina 2009).

In conclusion, whether it is a healthy individual or the one with a pathological condition giving rise to elevated concentration of extracellular DNA, the circulating cell-free DNA in the blood originates mostly from the cells dying due to apoptosis. The formation of platelets and the enucleation of erythrocytes have probably less significant effect at least in healthy individuals. Finally, active release of the DNA into the circulation remains still somewhat enigmatic as the function and mechanisms for these processes are not well-known.

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Figure 2. Possible sources of cell-free DNA. A) Apoptosis is considered to be the most significant source contributing to the total pool of cell-free DNA. Active secretion (B) and erythrocyte maturation (D) also have an effect in cell-free DNA levels. Necrosis (C) is thought to be minor contributor of cellfree DNA concentrations in plasma. Figure modified from Schwarzenbach et al. (2011) and Nagata (2005).

2.2.5 Cell-free DNA kinetics Free nucleic acids in the circulation are normally degraded quickly due to several deoxyribonucleases present in the blood. Enzymes of this class, DNase I, DNase II and phosphodiesterase I, act on exogenous DNA of bacteria and viruses in order to hamper the growth of these microbes as well as clear the endogenous DNA leaked into circulation from apoptotic and necrotic cells. (Cherepanova et al., 2006). In addition to the traditional blood deoxyribonucleases mentioned above, there are probably several less active proteins that participate in the degradation of circulating DNA some of which are known such as lactoferrin and immunoglobulins. However, despite the abundance of several DNA degrading enzymes in circulation, DNase I, owing to its high concentration and activity, is regarded as the main enzyme that determines the DNA nuclease activity of the blood. (Cherepanova et al., 2006)

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Given the fact that free nucleic acids are degraded rapidly in the blood, it is not likely that cell-free DNA would exist in circulation in its’ totally free form. Therefore, considering the relatively high nucleic acid concentrations that can be measured even from healthy individuals, it is obvious that there has to be some kind of shielding effect that protects the circulating DNA from degradation (Lichtenstein et al., 2005). Indeed, there is evidence for several different circulating particles that are associated with DNA. Common opinion is, that majority of the circulating DNA is originating from apoptotic cells as seen from the ladder pattern characteristic to DNA degraded in apoptosis. This implies that circulating DNA is protected from blood nucleases as they are inside of apoptotic bodies or microparticles released from apoptotic cells. (Distler et al., 2005) Rapid clearance of cell-free DNA from blood can be seen in fetal DNA in case when fetal DNA becomes invisible after the birth. (Bischoff et al., 2005)

2.2.6 Implications of cell-free DNA Since the time of its discovery, the presence of cell-free DNA in circulation has risen questions for does it have some specific functions or is it just background noise following from other processes. Probably the most of the circulating cell-free DNA is resulting from the natural turn-over of the cells and serve no biological function. (Peters & Pretorius 2011) However, as described above there is an evidence for the active release of DNA performing certain tasks.

The performed in vitro experiments, demonstrating the active release of DNA into the surrounding medium, have stirred up a lot of speculation. This seemingly strange behavior of the cells is not likely to be without a reason as the synthesis of the DNA requires large amounts of energy and cellular reactions tend to aim at maximizing the energy economy. (Alberts et al., 2002) It is known from the experiments that the plants and prokaryotes are able to release DNA outside and this DNA can move around and occasionally enter other cells leading to the transcription of these sequences (Gahan 2003). Adams and McIntosh (1985) have demonstrated with labeling experiments that the released DNA is taken up by the cells. Adams et al. (1997) demonstrated later using the different tumor cell lines that the taken up DNA is able to alter the DNA synthesis in these cells. Therefore, a role as messenger molecule between the cells or organs has been proposed for the cf-DNA. 21

It is generally accepted that mitochondria are of prokaryotic origin and indeed mitochondrial genome possesses several characteristics to bacterial DNA. In addition to the lack of introns and histones, the mitochondrial DNA contains CpG motifs that are unmethylated just like their procaryotic ancestors (Collins et al., 2004). The CpG sites in human nuclear DNA, however, show a very high methylation status (Jabbari & Bernardi 2004) being the main reason how innate immune system distinguishes between self-DNA and exogenous bacterial DNA (Krieg, 2000). If accessed, the neutrophils can identify mitochondrial DNA as foreign leading to activation of inflammation response through Toll-like receptor (Zhang et al., 2010, Oka et al., 2012). demonstrated that the mitochondrial DNA escaping the phagocytosis in cells under haemodynamic stress is able to induce myocarditis and dilated cardiomyopathy through the inflammation response. Chronic inflammation is a hallmark of aging and has been linked to aging-related diseases such as diabetes, atherosclerosis, sarcopenia, Alzheimer disease and cancer (Licastro et al., 2005). The DNA suffered from oxidative damage as indicated by the presence of 8-oxodG bases is also inflammatogenic (Collins et al., 2004).

2.2.7 Applications As the levels of cf-DNA in circulation are not constant but rather the consequence of ongoing release and degradation they are very sensitive to fluctuations (Fleischhacker & Schmidt 2007). This has been utilized and the quantitative and qualitative changes in cf-DNA concentration allow the diagnosis of certain diseases and conditions.

After the discovery that DNA originating from fetuses is present circulating in maternal plasma, it was quickly realized to have diagnostical potential. As a diagnostic tool, it is particularly tempting as usually prenatal testing implies invasive procedures while obtaining the cell-free DNA is rather un-invasive (Lo 2006). The fetal DNA is used routinely to find out to sex and Rh status of fetus in some countries of the Europe (Wagner 2012). However, the ethical issues have to be considered as the sex determination test is easy to carry out and can be purchased from the internet. This might be a problem in countries where other gender is preferred over another. The cell-free DNA is also used clinically as a general indicator of pregnancy. The 22

elevated DNA concentration may be a sign of pathological pregnancy such as preeclampsia, invasive placenta, fetal aneuploidy or pre-term labor (Wagner 2012, Lo 2006). Fetal cell-free DNA is used successfully to diagnose some neurological disorders and fetal chromosomal aneuploidies, but these applications are not yet validated (Tong & Lo 2006).

Cell-free DNA can also be used in cancer diagnostics as the circulating DNA also contains nucleic acids originating from the tumor cells. Tumors generally contain typical genetic and epigenetic alterations making the characterization of the tumor possible without the need of biopsy (Tamkovich et al., 2007).

2.2.8 Cell-free mitochondrial DNA and aging process Mitochondria are the primary energy suppliers of the cells providing them the means to stay functional even under highly stressful conditions (Meissner 2007). If, for some reason, mitochondria become dysfunctional it will usually lead to the cell death (Hiona & Leeuwenburgh 2008). Therefore, maintaining the integrity of the mitochondrial genome is highly important in the overall functionality of the individual. It is suggested in the mitochondrial theory of aging that damage to mitochondrial DNA leads to the accumulation of impaired mitochondria, which would be the essential contributor of the aging process (Kowald 1999). In this section, the mitochondrial DNA is briefly introduced as well as its possible role in aging process which is also part of the reasoning for performing this study.

Most of the cell-free DNA studies focus on the quantification of nuclear DNA fraction whereas that of cf –mtDNA has had less attention (Ellinger et al., 2008). The cfmtDNA is present in plasma as free form as well as bound to different particles (Chiu et al., 2003). 2.2.8.1 Overview of the mitochondrial DNA Mitochondrial DNA is circular, double stranded DNA molecule of 16569 base pairs located in the mitochondrial matrix (Wiesner et al., 2006). It is present in several thousands of copies depending on cell type with the energy-demanding cells like myocytes and neurons on the top (Miller et al., 2003). Mitochondrial DNA encodes 23

thirty-seven genes of which 24 code for ribosomal RNAs and tRNAs, the components of translational machinery, while the rest 13 are coding for the subunits of oxidative phosphorylation complexes (Wiesner et al., 2006). Due to small number of genes present, the maintenance, replication and transcription of mitochondrial DNA is carried out by the proteins encoded by the nuclear DNA (Escames et al., 2011). The mitochondrial DNA does not contain any intergenic sequences other than the short Dloop which acts as a regulator in the replication and transcription events. Unlike in the genomic DNA, the mitochondrial genes do not contain any introns and the genome is transcribed as a polycistronic product modified post-trascriptionally (Rose et al., 2002, Alexeyev et al., 2004). The replication of mitochondrial DNA is carried out by nuclear encoded DNA polymerase gamma (Taanman 1999).

2.2.8.2 Mitochondrial DNA damage and its’ role in aging It is suggested that mitochondrial impairment may be a major contributor of the aging process (Hiona & Leeuwenburgh 2008). The generation of the reactive oxygen species as a by-product of the respiratory chain is believed to introduce mutations into nearby mitochondrial DNA, which, depending on the severity of the damage may decrease the efficiency of the oxidative phosphorylation or totally impair it (Kujoth et al., 2007). However, some studies have shown that mitochondrial DNA damage is not dependent on ROS, but induced by some other mechanism (Trifunovic et al. 2005). Mutator mice experiment has been conducted to test this hypothesis. In mutator mice, the mitochondrial DNA polymerase gamma has been altered so that deficiency in 3’5’ proofreading activity renders it error-prone leading to the accumulation of somatic mtDNA mutations (Trifunovic et al., 2004). These mutator mice showed premature aging phenotype and reduced lifespan but instead of increased ROS production, they had increase in the markers of apoptosis which could implicate that mitochondrial DNA mutations contribute to the aging process via apoptosis (Khrapko and Vijg 2008).

An idea that mitochondrial DNA mutations contribute to the aging process is from the observation that some mtDNA mutations cause diseases resembling aging (Khrapko & Vijg 2008). Wild-type and mutant mitochondria are present in cells as heteroplasmic and mutant mitochondria in a cell must first reach a threshold level by 24

clonally expanding before it can cause adverse effects (Khrapko & Vijg 2008). Sarcopenia, the gradual loss of muscle mass is typical age-related condition not considered as a disease. It has been reported that mitochondrial DNA deletions are present in the muscles of older individuals implicating that mutated mitochondria may have an effect in sarcopenia (Khrapko & Vijg 2008).

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3. Aims of the study The cell-free DNA quantity in plasma has been reported to be elevated in older individuals, thus making it potential aging biomarker candidate (Jylhävä et al. 2011). The aim of this study was to analyze the cell-free DNA further by quantifying the mitochondrial and nuclear DNA, two components of cell-free DNA, separately from the plasma samples. The mitochondrial DNA mutations have been suggested to be essential players in the aging process and it has been reported that this effect may be due to the apoptosis, driven by the non-functional mitochondria. Therefore, it would be reasonable to hypothesize that the plasma of the nonagenarians contains more mitochondrial DNA than that of the control group. The ultimate objective of the study was to find out whether the mitochondrial DNA quantity in plasma can be used as a non-invasive aging biomarker. Additionally, the parameters obtained in this study, are used in the Vitality 90+ study to make correlations to different phenotypes of senescence.

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4. Materials and methods 4.1 The study population and the samples The samples used in this study were collected previously as a part of the bigger multidisciplinary project called the Vitality 90+ study which focuses on the aging of the oldest old. The Vitality 90+ study involves the individuals aged 90 years and older living in the city of Tampere, Finland and includes the collection of the data in the forms of mailed surveys, face-to-face interviews, performance tests and blood samples. The samples obtained for this study included the plasma and the PBMC:s. The Vitality 90+ study has the approvals of the Research Licence Committee and Ethical Committee of the Department of Health and Social Services in the city of Tampere. The study population consisted of 163 individuals being participants of the Vitality 90+ study and 35 young adults as a control group. All the 163 individuals representing the nonagenarian group were born in year 1920 assuring certain level of homogeneity in terms of the age. 116 of the total of 163 individuals were women and 47 men. The control group of the study consisted of healthy laboratory personnel who volunteered to contribute to the study. The individuals of the control group were aged 19-30 years and there were 25 women and 10 men giving the same ratio as that of the nonagenarian group. Additional criteria for the control group were that the individuals had no chronic illnesses and no infections or vaccinations in the previous two weeks. The nonagenarians of this study were recruited as reported earlier by Goebeler et al. (2003). 4.1.1 Collection and the processing of the blood samples Prior to this study, as a part of Vitality 90+ study, blood samples were collected from the individuals in summer 2010. The blood draw was done by trained medical students into EDTA-containing tubes. The total of 27 mL blood was collected of which the plasma and the PBMC:s were separated. The plasma was separated by centrifuging the blood first at 400 x g at +4°C for 15 min and after the separation an additional 1000 x g at +4°C for 15 min. After the separation plasma was stored in 70°C.

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The separation of the PBMC:s was done with Ficoll-Paque density gradient (FicollPaque™ Premium, cat.no. 17-5442-03, GE Healthcare Bio-Sciences AB, Uppsala Sweden). 25 mL + 8 mL of PBS was added to the blood cells left after the removal of plasma after which the mixture was pipetted over the Ficoll-gradient of 17 mL. The Ficoll-gradient containing the cells were centrifuged at 400 x g at RT for 35 min. The PBMC-layers were collected and PBS was added following the centrifugation at 400 x g at +20°C for 12 min. Supernatant was removed and the pellet was resuspensed to 8 mL PBS and centrifuged at 200 x g at 20°C for 10 min. Again, the supernatant was removed and pellet resuspensed for 4 mL of PBS and centrifuged at 200 x g at +20°C for 10 min. After the removal of supernatant and resuspension to 2 mL of PBS, the PBMC:s were counted.

The washed cells were divided into aliquots of 0,5 million cells. The freezing medium was prepared to include 50 % RPMI, 25 % FBS and 25 % DMSO. 500 µl FBS and 500 µl freezing medium was added to each 0,5 million cells. The cells were put in cryotubes and stored in -70°C overnight, after which they were moved to liquid nitrogen for long-time storage.

4.2 DNA extraction

Using the plasma samples of voluntarily laboratory personnel, two different DNA extraction kits were evaluated; The NucleoSpin® Plasma XS kit with high sensitivity protocol by Macherey-Nagel and QIAamp DNA Blood Mini kit by QIAGEN. Both kits are suitable for extraction of cell-free DNA and the procedures are based on silica-membrane purification. Additionally, both kits involve the removal of contaminants and inhibitors to a high degree, allowing the down-stream applications such as PCR to be performed reliably. The NucleoSpin® Plasma XS protocol is available in Macherey-Nagel Nucleospin® Plasma XS user manual (February 2010, Rev. 02) and QIAamp protocol in QIAGEN QIAamp® DNA Mini and Blood Mini Handbook (April 2010, third edition). The DNA yields with these two kits were compared and QIAGENs QIAamp kit was chosen to be used for the actual samples based on the higher yields.

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4.2.1 Extraction of the cell-free DNA from plasma samples The cell-free DNA was extracted from plasma samples using QIAGEN QIAamp DNA Blood Mini kit with DNA purification from blood or body fluids protocol. This kit has a variety of protocols allowing the purification of genomic, mitochondrial and viral DNA from whole blood, plasma, serum, buffy coat, bone marrow, lymphocytes or cultured cells. The purified DNA is free of protein, nucleases and other contaminants, making it suitable for PCR right away. The purified DNA is stated to be up to 50 kb in size with 20-30 kb fragments predominating. The purification procedure follows the four basic steps of nucleic acid extraction; lysis, binding, washing and elution.

The plasma samples were stored in -70°C as mentioned above. 200 µl of thawed sample was applied to 20 µl of QIAGEN Protease. 200 µL of lysis buffer AL was added to the mixture of protease and sample following the 15 seconds of pulsevortexing. After thoroughly homogenized, the mixture was incubated in heating block at 56°C for 10 minutes. After the incubation period, the tube was swiftly microcentrifuged in order to remove the drops from the inside surface of the lid. 200 µl of 100 % ethanol was added to the mix following the 15 seconds of pulse-vortexing and brief centrifugation. Next, the mixture was transferred to QIAamp Mini spin column placed in a 2 ml collection tube which was centrifuged at 6000 x g for 1 minute. The column was placed in a clean 2 ml collection tube and 500 µL of wash buffer AW1 was applied to the sample following a centrifugation at 6000 x g at RT for 1 minute. The washing of the column was still performed by placing it in a clean 2 ml collection tube with 500 µL of wash buffer AW2 following a centrifugation at 20000 x g at RT for 3 minutes. To get rid of the ethanol left-overs, the column was moved to clean collection tube and centrifuged at 20000 x g at RT for 1 minute. Finally, the collection tube was applied to sterile microcentrifuge tube and 60 µL of elution buffer AE was added. Additionally, the sample was incubated in heating block at 65°C for 10 minutes. Elution was performed by centrifuging at 6000 x g at RT for 1 minute. Elution volume was adjusted from 200 µL to 60 µL in order to obtain more concentrated DNA. In Addition, final incubation period before elution was modification of the original procedure to still increase the concentration of DNA. After elution, the extracted DNA was stored in -20°C. 29

4.2.2 Extraction of the DNA from the PBMC:s The PBMC:s freezed prior to this study were now thawed and washed to get rid of the freezing media containing DMSO, the potential inhibitor of PCR. Prior to thawing, the thawing medium was prepared to include 90 % RPMI and 10 % FBS and warmed up at the +37°C in heating water bath. The cryotubes were taken out of the liquid nitrogen and placed in a water bath. Just prior to total thawing the cryotubes were sterilized and moved to laminar hood. The content of the cryotubes was pipetted in 15 mL tubes and 1 mL of thawing media was added to the emptied cryotube to wash up and detach the remaining cells and finally added to originally pipetted fraction. Additional 7 mL of thawing media was added over the cells and centrifugation at 250 x g at RT for 10 minutes was performed. The supernatant was removed and 10 mL of fresh, warm thawing media was added resuspensing the pellet. The cells were centrifuged again at 250 x g at RT for 10 minutes following the removal of the supernatant and resuspension to 200 µL of PBS.

Immediately after the washing of the PBMC:s the DNA extraction was performed. The extraction was carried out similar way and with same protocol as in the case of plasma DNA with the QIAGEN QIAamp DNA Mini kit which is basically the same as the one used with the plasma samples. As a difference compared to plasma DNA extraction, all the centrifugation steps were carried out at the full speed to avoid the clogging of the membrane as instructed in the manual. The DNA was eluted to 60 µL of elution buffer AE and stored at -20°C.

4.3 Determining the concentration of the DNA in plasma and PBMC eluates The concentration of the DNA in obtained plasma eluates was determined by using the Qubit® fluorometric quantification with dsDNA High Sensitivity assay which is highly selective for double-stranded DNA and accurate for sample concentrations from 10 pg/µL to 100 ng/µL. 3 µL of sample was used for each reaction and they were performed as duplicates, of which the mean value was used. The measurements were done according to the standard protocol of dsDNA HS assay provided by Invitrogen Molecular Probes 32851 (Revised:1- October-2010). 30

4.4 DNA quantification by qPCR The quantification of mitochondrial and nuclear DNA in plasma and PBMC samples was carried out by using quantitative real-time PCR. The quantification of plasma samples was performed for all the 163 nonagenarian and 35 control plasma samples. The PBMC samples from some individuals were unavailable and due to too low DNA yields in some samples they were not included for the quantification. The TaQMan® probe-based assay was used in both, the quantification of mitochondrial and nuclear DNA. The principle of the probe is described in figure 3.

Figure 3. The principle of the TaQMan-probe based PCR assay. 1. A Fluorescent reporter dye (R) and quencher (Q) are attached to 5’ and 3’ ends of the probe. When the probe is intact, the quencher renders the reporter as non-fluorescent. 2. While Extending the primers, the DNA polymerase cleaves the reporter dye as it meets the probe. 3. As the reporter is no longer in the vicinity of the quencher, it is able to emit fluorescent signal which can be detected on the detectors of quantitative PCR instrument.

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4.4.1 Quantification of mitochondrial DNA from plasma Special attention was addressed to mitochondrial DNA primers since as a result of the duplication of mitochondrial genome, many pseudogenes are present in the nuclear DNA resulting in possible undesired amplification products. Therefore, instead of using the primers specific for mitochondrial genes such as ND1 and ND6 that are commonly used, we utilized the predesigned primers constructed by Malik et al. (2011). They used BLAST to determine the duplication status of mitochondrial DNA in respect to nuclear genome and were successful in obtaining the mitochondrial DNA specific sequences. The primers amplify the region of mitochondrial DNA containing 64 base pairs and hybridization probe specific for this sequence, also described by Malik et al. (2011), was used. The hybridization probe contained FAM as a reporter dye and a non-fluorescent quencher dye. The probe and the primers were ordered from Applied Biosystems (Espoo, Finland). The primer and probe sequences for mitochondrial DNA amplification are depicted in table 3. The primers were delivered dry and purified with desalting. The dissolving was carried out to sterilized water to give the concentration of 10 µM. The probe was delivered in liquid state dissolved in TE buffer with the molarity of 100 pmol/µL and purified with HPLC. The probe was diluted to give the concentration of 2,5 µM.

Table 3. The primers for mitochondrial DNA.

Primer

Sequence

Forward 5'-CTT CTG GCC ACA GCA CTT AAA C-3' Reverse 5'-GCT GGT GTT AGG GTT CTT TGT TTT-3’ Probe 5'-ATC TCT GCC AAA CCC C-3'

The optimal amount of DNA in the PCR reaction for plasma samples was adjusted by testing the different volumes of test sample eluates. Based on the linear range determination of the reaction, 2 µL of DNA eluate from each sample was used in PCR reaction. The setup of the reaction components for the quantification of mitochondrial DNA in plasma eluates is depicted in table 4. With the given volumes, the concentration of the primers and probe was 1 µM and 250 nM, respectively. The 32

reactions were carried out in 96 well plates. The reactions were carried out in 96 well plates. All samples were run as duplicates and NTC was included for each plate. The human mitochondrial DNA standard was prepared as a reference to the samples. The certified human mitochondrial Standard Reference Material ® 2392-I was ordered from NIST, USA. The standard included mitochondrial copy numbers of 1E4, 1E5, 1E6 and 1E7 copy numbers. All controls, standards and samples were run in duplicate. If the standard deviation of the Ct-values of the duplicates differed more than one unit, the sample was re-tested. The PCR was carried out by using Applied Biosystems 7900HT instrument and the program is as follows. DNase and RNase free water was provided by Sigma Aldrich (St. Louis, MO, USA). The Master Mix (TaqMan® Universal PCR Mix, no AmpErase® UNG, manufactured by Roche, Branchburg, NJ, USA) was ordered from Applied Biosystems.

Table 4. PCR reaction setup for mitochondrial DNA in plasma eluates.

Reaction component TaqMan Universal PCR Master Mix (2X) Forward primer Reverse primer TaqMan probe DNA RNase-free water Total

Volume/Well (µL) 10 2 2 2 2 2 20

The thermal cycling conditions were as follows: 2 minutes at 50°C for the activation of UNG followed by 10 minutes at 95°C for the activation of the enzyme. After activation steps 40 cycles were performed with a denaturation step of 15 seconds at 95°C followed by annealing/extension of 1 minute at 60°C. The obtained data was analyzed in SDS software (ABI).

4.4.2 Quantification of genomic DNA from plasma For the quantification of genomic DNA, the TaqMan® RNase P detection kit by Life Technologies™ was used. This kit includes single ready-to-use reaction mix which contains pre-designed primers and FAM-labeled hybridization probe for the nuclear RNase P gene. According to the manufacturer, the primers and probe are designed in 33

terms of Primer Express guidelines. The human genomic control DNA was also provided with the kit from which the genomic standard was prepared. The standards were diluted to contain 9, 90, 900 and 9000 copies of genomic DNA. The PCR was carried out on a 96-well plate, which contained NTC, human genomic DNA standard and samples all in duplicates. All reaction components were pipetted separately. The plate was covered with adhesive cover and centrifuged briefly for 1 minute at 1000 rpm. The reaction setup is shown in table 5.

Table 5. The reaction setup for the nuclear DNA quantification from the plasma samples.

Reaction component Volume/Well (µL) TaqMan Universal PCR Master Mix (2X) 10 20x RNase P Primerprobe mix 1 RNase-free water 7 Sample eluate 2 Total 20

4.4.3 Quantification of DNA content in the PBMC:s Quantification of mitochondrial and nuclear DNA from PBMCs was done with the same protocol that was used in plasma quantification. Instead of using the fixed volume of the DNA as in the plasma quantification, the 50 ng of DNA was used in each reaction. Due to this, increasing the reaction volume up to the 30 µl was obligatory. The mitochondrial DNA primers and probe were same as previously described (table 3) and reaction setup is depicted in table 6.

Table 6. The reaction setup for the mitochondrial DNA quantification from the PBMC:s.

Reaction component TaqMan Universal PCR Master Mix (2X) Forward primer Reverse primer TaqMan probe DNA RNase-free water Total

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Volume/Well (µL) 15 3 3 3 x 6-x 30

RNase P detection kit was used for the quantitation of nuclear DNA in a similar way as in the plasma samples. The PCR reaction setup for the quantification of the nuclear DNA is shown in table 7.

Table 7. The reaction setup for the nuclear DNA quantification from the PBMC:s.

Reaction component Volume/Well (µL) TaqMan Universal PCR Master Mix (2X) 15 20x RNase P Primerprobe mix 1,5 RNase-free water 13,5-x DNA x Total 30

4.5 Copy number processing and statistical analysis The results were obtained by SDS software (Applied Biosystems) version 2.2.2 installed in the computer operating the ABI 7900. The standards were checked and the duplicate samples having standard deviation larger than 1 in Ct-values were re-tested. The statistical analysis of the results was carried out in SPSS® statistics software version 19 by IBM®. The obtained data were analyzed by nonparametric test of independent samples with significance level of 0,05.

.

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5 Results

The median and interquartile range was calculated for the concentrations of plasma and PBMC DNA eluates inside the nonagenarian and control group. Additionally, both groups were analyzed separately by dividing them as men and women. Same protocol was applied when dealing with nuclear and mitochondrial DNA copy numbers. The differences between the groups were analyzed by independent-samples Mann-Whitney U test. In addition, the mitochondrial to nuclear DNA ratio was calculated for each group and were compared pairwise.

5.1 DNA concentrations in plasma show no difference between nonagenarians and controls Of the 163 analyzed plasma samples, the concentration of total extracted DNA was first determined by fluorometric quantification to find out whether there are differences between the two groups. The cell-free DNA appeared to be present equally in the plasma of nonagenarians and controls (median =77.6 ng/ml, IQR=65.096.9 and median=79.9 ng/ml, IQR=65.0-97.5, respectively) Additionally, there were no significant differences between the concentration of men and women when tested separately inside the nonagenarian and control groups (p=0.988 and p=0.812, respectively). Male nonagenarians had a median of 77,3 ng/ml, IQR being 60.8-105.0, while female nonagenarians had a median of 78.7 ng/ml, IQR being 66.7-96.1. The male controls had a median of 78.9 ng/ml, IQR being 54.0-123.5 , while female controls had median of 79.9 ng/ml, IQR being 68.4-93.1.

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Figure 4. Total DNA concentration in extracted plasma samples. There were no differences between nonagenarians and controls.

5.2 qPCR In general, the qPCR showed good reproducibility as the Ct-values of the duplicate samples were highly similar. Standard deviation greater than 1 within the Ct-values of duplicates were considered inappropriate and were re-tested. All genomic DNA samples worked out well and had extremely low standard deviations. Mitochondrial DNA samples, on the other hand, had more variation and around 10 samples were retested. The copy numbers were scaled to 1 ml for better comparability to previous results.

5.2.1 The nonagenarians have elevated genomic DNA levels Quantitative PCR was performed to find out whether there were differences in quantities of DNA in plasma compared to younger individuals. The levels of genomic 37

cell-free DNA turned out to be significantly higher in the plasma of nonagenarians (p=0.001, nonagenarians; median=2626, IQR=1357-3029; controls; mean=1442, IQR=688-2040) (figure 5). Copy numbers were scaled to 1 mL for better comparability with previous results.

Figure 5. Genomic cell-free DNA copy numbers in plasma. The nonagenarians have significantly higher levels of genomic DNA than younger control group.

In addition, a comparison was done between genders inside both groups. There were no statistically significant difference in genomic DNA of nonagenarian male and female subgroups (p=0.368, medians=1865 and 2183 respectively). Instead, the copy numbers of controls were significantly different between men and women (p=0.04, medians=2006 and 930, respectively).

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Figure 6. Genomic cell-free DNA copy numbers in plasma of nonagenarians. There is no difference in the genomic DNA copy numbers between the male and female nonagenarians.

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Figure 7. Genomic cell-free DNA copy numbers in plasma of controls. The females of the control group have significantly lower genomic DNA copy numbers than male controls.

5.2.2 There is no difference in the plasma mitochondrial DNA copy numbers between the nonagenarians and controls There were no differences between the plasma mitochondrial levels of nonagenarians and controls (p=0.549). Additionally, when divided into subgroups by gender, no difference was observed (p=0.930 and p=0.635, respectively). The ratio of mitochondrial DNA copies to genomic DNA copies in plasma was calculated (figure 8). The nonagenarians have smaller ratio due to bigger amount of genomic DNA present in plasma (p=0.01).

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Figure 8. Ratio of plasma copy numbers of mitochondrial and nuclear DNA. The nonagenarians have smaller mtDNA/gDNA ration due to the higher amount of genomic DNA.

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5.2.3 The mitochondrial DNA to genomic DNA - ratio is reduced in the PBMC:s of the nonagenarians The mtDNA to gDNA ratio of the PBMC:s was notabl,y, although not significantly, lower in nonagenarians (p=0.084). The mtDNA and nuclear DNA levels were both smaller in nonagenarian group driving the ratio towards the observed values (Nonagenarians; median=12862, controls; median=16568). (Figure 9)

Figure 9. Ratio of PBMC copy numbers of mitochondrial and nuclear DNA. The nonagenarians appear to have smaller mtDNA to nDNA ratio in PBMC:s. The difference is notable although not significant.

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6. Discussion The amounts of the publications involving cell-free DNA quantification are increasing. However, the major drawback of these studies is that there is no standardized protocol involving the methodology and the presentation of the results (Fleischhacker 2011). Different DNA extraction kits are used alongside with different PCR primers and the results are presented different way in virtually every article. This makes the comparison of the results between different studies extremely difficult and in some cases, impossible. As the clinical significance of the cell-free DNA has come up, it should be of uttermost importance to develop at least somewhat analogical logic to overcome these issues.

6.1 The study population Selecting the representative sample group out of the population is prerequisite for reliable results in every study. Therefore, the study population was chosen to reflect the actual proportions of women and men amongst their own age group. It has been reported that after the age of 60, women begin to outnumber men in a way that there are three women for every two men at the age of 80 and six women for every man amongst centenarians (Vaupel 2010). The 71 % share of the women in this study population is consistent with this trend and therefore reasonably chosen. In addition to the observation that women tend to live longer, those few men that survive to very old ages are actually healthier and do better than the women of the same age (Vaupel, 2010). These strange curiosities are known collectively as so called male-female health-survival paradox (Oksuzyan, 2008).

Additionally, as there is variation in the normal cf-DNA concentrations between individuals, number of samples has to be considerably high to make up these natural fluctuations (Beiter et al., 2011). The total number of obtained nonagenarian samples is relatively high considering that the population which is suitable for this study is small.

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6.2 Extraction of cell-free DNA from plasma samples Although absolute quantification of the cell-free DNA was not the aim of this study, it is appropriate to bring it up here as it is relevant when discussing the copy numbers of genomic and mitochondrial DNA.

Traditionally DNA extraction is carried out by different kind of alcohol precipitations, which tend to be troublesome and susceptible to mistakes. Nowadays, however, many companies provide a fast and simple-to-use DNA extraction kits that are based on the silica membrane technology, involving the binding and elution of the DNA with different kind of buffers. In the beginning of this study, two extraction kits that were used for the same purpose before were evaluated. The DNA yields obtained by Nucleospin® Plasma XS kit and QIAamp DNA Blood Mini kit were compared and QIAamp was chosen due to its better yield.

As the cell-free DNA in plasma is highly fragmented and present in small quantities there is an issue considering different extraction methods. It has been reported that the extraction done with different kits from the same samples gives results of high variety (Fleischhacker 2011). Concentrations ranging from few ng/mL to hundreds of ng/mL have been extracted from human plasma, leading to confusion when comparing the results, as in addition to variation rising from different patients, also preanalytical conditions and extraction methods contribute to these differences (Fleischhacker 2007).

The measured cell-free DNA concentrations in plasma eluates (nonagenarians: median=78 ng/mL, controls: median=80 ng/mL) were in line with the previously reported results and also with the estimates of 1-100 ng/mL provided by the kit manufacturer. Although no reference study was found with same extraction kit and quantification method. Rhodes et al. (2006) reported the cell-free DNA plasma concentrations in healthy individuals with a median of 74 ng/mL extracted with High Pure PCR Template Preparation kit (Roche, Lewes, UK) and quantified with qPCR. Regardless of the different methodology, the DNA extraction in this study can be considered successful. 44

It has been previously reported that the cell-free DNA concentration is higher in older individuals when measured directly from plasma samples. (Jylhävä et al. 2011) The fact that extracted plasma DNA in this study shows no significant difference may be due to the limitations of the extraction kit. According to the kit manual, the QIAamp DNA kit extracts mainly high molecular weight fragments thus possibly excluding the very small fragments. The increased apoptotic activity due to different physiological reasons has been proposed to be the cause of increased cell-free DNA concentration in plasma of older individuals. Therefore, if there actually was more cell-free DNA in plasma of nonagenarians resulting from apoptosis, some of it may be lost in extraction procedure due to its’ small size.

6.3 Quantitative PCR Many studies involving the quantification of mitochondrial DNA by qPCR uses primers for mitochondrial genes ND1 and ND6 or some others. However It is obvious that using this kind of genes in quantification may lead in undesirable amplification products. On the other hand, this would not have had big effect in this study because copy numbers were compared only relatively.

The RNase P gene is well characterized and is known to be present in 2 copies in diploid cells. Therefore, it is a reasonable choice for the quantification of genomic DNA and was also used in this study. As for the mitochondrial genome, it is highly duplicated during the evolution and many of its’ genes also reside in nuclear genome as pseudogenes. It is very common in studies to use the primers specific for mitochondrial genes, which are repetitive and also present in nuclear genome (Malik et al., 2011). According to www.genecards.org, 40 pseudogenes for mitochondrial ND1 and 21 pseudogenes for ND6 gene, common genes used for the amplification of mitochondrial DNA, are present in nuclear genome. As this obviously leads to undesirable amplification products, their use is not justifiable. The mtDNA primers used in this study, engineered by Malik et al, were designed with an effort by identifying unique region in the mitochondrial genome using BLAST.

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Malik et al. (2011) also reported of the issue called mitochondrial DNA dilution bias in which certain dilutions do not contain the desirable mtDNA copy number although they should. To overcome this obstacle the copy number must be multiplied by given coefficient. The correction in this study was done according to the study performed by Malik et al. (2011).

6.3.1 Cell-free DNA in plasma According to the results, the nonagenarians have more cell-free nuclear DNA circulating in plasma than their younger counterparts (p=0.001). Additionally, the men of the control group had significantly higher genomic DNA levels than the women (p=0.04). In other words, the nuclear DNA levels stay intact in the plasma of men their whole life, whereas there is a rise of these levels in women. Zachariah et al. (2008) reported nuclear DNA levels in plasma of healthy individuals of approximately 1700 genomic equivalents per mL extracted with automated MagNa Pure LC DNA extraction kit and quantified with real-time multiplex quantitative PCR. These are in consistency with the results obtained in this study regardless of the different methodology.

Zhong et al. (2007) reported significantly elevated cf-DNA concentrations in women over 60 years when compared to younger counterparts. Additionally, they did not observe such a difference in men. Zhong et al. (2007) did not really discuss this through but proposed the possibility of statistical artifact as an explanation. However, as the same phenomenon repeated again in a study by Jylhävä et al. (2011) and now in this study, the observed effect is most likely real. Estrogen is known to affect in immune system functioning by affecting the inflammatory mediator production by macrophages and thus providing women somewhat stronger immune system functioning (Capellino et al., 2006). It is also known that macrophages are responsible for clearing the cellular debris produced by apoptotic cells and this is diminished in older age due to general immune system dysfunction (Aprahamian et al., 2008). Now, as the women go through menopause and the production of estrogen falls, their immune system might hypothetically undergo relatively larger rundown during the aging process than that of men. Additionally, as the immune system is responsible for clearance of the cf-DNA this could explain the observed differences. 46

However, there were no differences in cell-free mitochondrial quantity between nonagenarians and controls implying that increased apoptosis may not be the responsible factor. In order to fully interpret these results, the origin and form of mitochondrial DNA should be better known as the mitochondrial DNA is known to be actively secreted and have a role in immune defenses (Keshari et al., 2012). 6.3.2 DNA content in PBMCs The obtained copy numbers for the genomic DNA indicated that the genomic standard was reasonable and the extraction of the DNA from the cells was successful. When the copy numbers of nonagenarians and controls were compared, it appeared that nonagenarians have less genomic DNA in PBMCs than controls. The mitochondrial DNA behaved the same way having lower copy numbers in nonagenarian group. This result, however, may be misleading, as the nonagenarian cells were much harder to extract whereas those of the control group handled easily which may have affected to different cell yields. Therefore it is more useful to compare the ratios of mitochondrial and nuclear DNA in each group as the exact mitochondrial copy number count per cell is obtained. There were notable differences between the mtDNA/nDNA ratio, although not significant (p=0.084). The ratio obtained for the nonagenarians (mean=12862) was notably smaller, than that of the controls (mean=16568) indicating that the mitochondrial DNA levels are probably relatively lower in the PBMC:s of the older individuals as the nuclear DNA content is somewhat the same. Indeed, Barazzoni et al. (2000) has reported that there are varying mitochondrial DNA copy number reductions in the cells of different tissues as a result of aging, thus supporting this finding.

Typical human cell contains approximately 1000-5000 mitochondrial DNA molecules, although depending on many factors (Moraes 2001). High energy using cells like neurons and muscle cells may contain even more. Approximately 1000 copies have been reported to be present in peripheral blood mononuclear cells (Mendoza et al., 2004). In the light of these numbers it is obvious that although fixed, the mitochondrial standard still has issues concerning the dilution bias as discussed above. Nevertheless, even though the obtained mtDNA copy numbers are obviously

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not absolute, they are comparable between each other and hence will not affect any correlations.

6.3.3 Comparison of the mtDNA/nDNA ratio in plasma and PBMCs Comparison of the obtained ratios was of interest as the apoptosis of the cells from varying tissues has been proposed to be major source of the cf-DNA (Tamkovich 2008). According to the results the mtDNA/nDNA ratio in plasma is much higher than that of PBMC:s. This suggests that if cf-DNA is of apoptotic origin, the plasma levels of mitochondrial DNA are too high to be originated from the cells of low mtDNA copy content such as PBMCs. However, if scaled up from these results, the mtDNA/nDNA ratio in the cells of high energy demand, possessing greater number of mitochondria, would somewhat match the ratio observed in the plasma. This suggests that cells like neurons and myocytes undergoing apoptosis could be the source of cfDNA. The degeneration of neurons is described in several age-associated pathologies but also as a part of normal aging (Anglade et al., 1997). Additionally, sarcopenia, the age related progressive loss of muscle mass due to apoptosis is generally accepted part of the normal aging process (Marzetti and Leeuwenburgh 2006).

The contribution of the other possible sources is difficult to estimate in the light of this study. Considering the total cf-DNA concentrations in the plasma, which are practically the same (p=0.964) between the nonagenarians and controls, and the somewhat differing copy numbers of genomic and mitochondrial DNA, it is tempting to think of some kind of a mechanism that keeps the total cf-DNA concentration constant allowing the changes in subcomponents (mtDNA, nDNA). Indeed this homeostatic system considering cf-DNA has been described (Gahan, 2006).

Most studies involving the quantification of cf-DNA face an issue that measured concentrations are usually obtained from individuals having some disease that has already progressed. The study layout of this kind allows only two kinds of conclusions to be made; whether the cf-DNA concentrations are ascended or not, in a particular condition. To better understand the implication of cf-DNA in reported conditions, it would be necessary to obtain the information at the onset of the disease as well, at the stage where symptoms are still absent. The studies of this kind are 48

obviously difficult to set up, as there is no way to predict the time when specific condition is about to break out. However, alternative way to bypass this obstacle has been discovered. It has been demonstrated that body’s immune response after physical exercise is comparable to that of infection (Pedersen et al. 2000). Based on this, Beiter et al. (2011) carried out an experiment where individuals’ cf-DNA levels were measured at different time points; before, during and after the exercise. This kind of innovative study setup is effective for modeling the events taking place in actual clinical conditions and more of these are needed to obtain the information related to the origin and behavior of the cf-DNA.

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7. Conclusions The rising number of very old individuals within the human populations is worldwide phenomenon. It has been estimated that the percentage of 60 years or older will double in the next 50 years, which will definitely have an effect in the medical care services and overall economy. Not only is it the issue of money but also improving the quality of life of these individuals has to be considered. As the aging is characterized by several age-related issues that worsen the quality of life, it is proposed that aging biomarkers could be used to predict which individuals are on a risk group to develop these complications and they could be treated well before the symptoms occur, hence improving their quality of life.

This has initiated a global interest to find the biomarker of aging, a measurable parameter that would predict the life-expectancy of an individual accurately and allow interventions to be made. Some aging biomarkers have been discovered that work in mice, but the problem tends to be that they succeed in predictions only in the level of populations. In other words, they won’t apply well on individuals.

The aim of this study was to evaluate the potential of the quantity of different components of cell-free DNA as an aging biomarker. Clearly, cell-free DNA holds value as a disease biomarker but when it comes to predicting aging process, it is not that simple. There are differences in the components of cf-DNA between older and young, but the results are not unambiguous. More studies are needed focusing on the origin and possible functions of cf-DNA, which might reveal some light for what components to measure. There was no observed difference between the mtDNA quantities of nonagenarians and controls, suggesting that cf mtDNA plasma quantity is not successful aging biomarker as such. However, because only two distinct groups were compared: the young and the very old individuals, the obtained data does not address the issue of a linear correlation between chronological age and mtDNA quantity in plasma. Additional studies are also needed to demonstrate whether there are differences between the successfully and unsuccessfully aged individuals.

Additionally, as the aging is multifactorial process, a single biomarker is not likely to be covering the whole system. Cf-DNA might be the predictor of one subprocess of 50

the aging, but for the predicting of whole package, it will probably need a battery of biomarkers with varying weightings. Cf-DNA could be one of those. Nevertheless, successful or not, the aging biomarker research in general makes a huge contribution to the aging science community, by providing useful data concerning the actual aging process and giving rise to new theories. In a few years, when the results of the large scale European Mark-Age project (www.mark-age.eu) are published, the cell-free DNA concentration in serum with dozens of other parameters will be evaluated as a potential biomarkers of aging.

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