Peculiarities of Working with Ancient DNA Текст научной статьи по специальности «Биологические науки»

PECULIARITIES OF WORKING WITH ANCIENT DNA

L.R. Gabidullina1*, M.A. Dzhaubermezov1'2, N.V. Ekomasova1'2' Z.R. Sufyanova1, E.K. Khusnutdinova1'2

1 Federal State Educational Institution of Higher Education «Ufa University of Science and Technology», 32 Zaki Validi St., Ufa, 450076, Russia;

2 Institute of Biochemistry and Genetics, Ufa Federal Research Center of the Russian Academy of Sciences, 71 prospect Oktyabrya, Ufa, 450054, Russia.

* Corresponding author: [email protected]

Abstract. This year Nobel Prize in Physiology or Medicine awarded to Swedish paleogeneticist Svante Paabo demonstrated the significance of ancient genomes investigation, shedding bright light to the aDNA science. As an ancient DNA study has evolved from the 1980s to the present days it has experienced peak of inflated expectations, dramatic fall to trough of disillusionment and slope of enlightenment with exponential data accumulation after the successful introduction of NGS approach. Today ancient DNA study is a rapidly developing and facilitated by advanced technologies science that challenges our assumptions about the past by analyzing over a million-year-old ancient specimens. Here we provide an overview of peculiar properties of ancient DNA analysis of human and microbes genomes.

Keywords: human populations, ancient DNA, paleogenetics, postmortem damage, contamination, ancient bacterial DNA, ancient pathogens.

List of Abbreviations

14C - radiocarbon

A / T / G / C - adenine / thymine / guanine / cytosine

aDNA - ancient DNA bp - base pairs

C to T - cytosine to thymine replacement DNA - deoxyribonucleic acid IntCal20 - International Calibration 2020 kyr - thousand years Ma - million years

MRCA - most recent common ancestor mtDNA - mitochondrial DNA NGS - next-generation sequencing UDG - uracil-DNA glycosylase PCR - polymerase chain reaction rRNA - ribosomal ribonucleic acid

Introduction

Research on aDNA or paleogenetics is defined as the study of degraded DNA fragments that preserved in subfossil material, ranged from hundreds or hundreds of thousands of years old (Paabo et al., 2004). The number of published papers on paleogenetics has increased exponentially in recent years, and the application of sophisticated new methodologies provided remarkable results in short time.

There are several fields for aDNA research applying. Firstly, in origin study it has revolutionized our understanding of evolutionary history especially of humans. Discovery of previously unknown hominins, analyze of homo group representative's interbreeding and their contribution to the modern human gene pool, including the Denisovans and the Neanderthals (Krause et al., 2010; Paabo, 2015). Clarifying the migration routes of human from Africa and peopling around the world in conjunction with transition processes from a hunter-gatherer lifestyle to pastoral herding, agricultural farming and domestication, that in turn shed light on the spread of animal pathogens transmission to humans (Frantz et al2020; Nielsen et al2017; Spyrou et al2019). The aDNA data allowed to observe genomic signatures of selection processes in human populations and adaptive intro-gression: search positively selected genetic loci that accelerated adaptation and improved survival in new environments, thus possibly allowed modern humans to widely expand (Jensen et al., 2022; Racimo et al., 2015; Rees et al., 2020).

Secondly, as already noted, study of paleopathology previously based on examination of fossils, mummified tissue, skeletal remains.

Pathogens and the diseases are one of the most important selective forces experienced by humans during their evolutionary history. Beyond host genomes and their pathogens, meta-genomic characterization (analysis of genomes from an environmental sample) of our microbial selves and the identification of epigenetic marks have paved the way for ancient holobi-omes that lead to greater understanding of past social, dietary, and environmental shifts and their impact on health. individuals and populations. Paleopathology has emphasized the importance of DNA science not only in understanding our past, but also how our past can affect our lives, our biology and health today.

Thirdly, aDNA has been used to investigate not only human populations but wide-ranging topics, including biogeography and ecology: reconstruction of the phylogeny, extension ways and environmental shifts of controversial species, ecosystem responses to climate change such as impact of Holocene climate change on biodiversity and anthropogenic effect on extinction processes that providing an analogue for understanding potential impacts of future change (de Bruyn et al., 2011; Drummond et al., 2005; Flink et al., 2014; Miller et al., 2012; Mitchell et al., 2014; Rawlence et al., 2014; Shapiro et al., 2004; Willerslev et al., 2014).

DNA Survival

DNA has limited chemical stability and decays without the enzymatic repair mechanisms of living cells. It took about three decades from the beginning of aDNA research to numerous attempts to demonstrate a general relationship between age and DNA preservation. It for a long time could not be proved due to the lack of empirical data on the temporal trends in the rate of DNA decay over time. As a result, studies of radiocarbon-dated DNA from highly closely localized bone specimens showed that DNA damage occurring after death (post-mortem) can be seen as a rate process with a theoretically possible calculation of the upper limit of the DNA preservation (Allentoft et al., 2012).

Theoretical and empirical data presents that age of the upper limit of DNA survival differs between the mitochondrial and nuclear ge-

nomes. The limit for PCR amplifiable DNA between 400 kyr and 1,5 Ma, beyond which DNA is either severely crosslinked or non-detectable (Willerslev & Cooper, 2005). mtDNA is the first ancient genome that was sequenced through laborious bacterial cloning due to its short size and the approaches, it was extracted from the dried muscle of a museum specimen of the quagga in 1984 (Higuchi et al., 1984). Until 2013 the oldest ancient genomes were aDNA from a 560,000-780,000 year old horse leg bone (Orlando et al., 2013), the complete mitochondrial genome of a ~400 kyr-old cave bear (Dabney et al., 2013) and archaic hominin (Meyer et al., 2014) from Spain. Last year paper pushed Middle Pleistocene limit to the new ancient record - 1.65 million years old mammoth teeth from Siberian permafrost have produced aDNA obtained 49 million base pairs of nuclear DNA (Meyer et al., 2016; Orlando et al., 2013; van der Valk et al., 2021).

Dating methods

Radiocarbon dating

In order to correctly interpret the finds of an ancient specimen, it is important to have an accurate estimate of its age for confirming obtained by the molecular clock method age and verifying the results, for which radiocarbon dating is currently the standard. (Kulkova, 2016) Classic paleomaterials for studying aDNA are rich-collagen bones and teeth perfect fits for radiocarbon age analysis as they contain carbon isotops (Geigl & Grange, 2018).

The method of radiometric dating is based on the measurement of radioactive decay or the formation of specific elements. Prior to the advent of this class of methods, archaeologists determined the age of finds using the method of relative chronology - through iconic artifacts or objects of material culture, as well as the method of stratigraphy. Radiocarbon dating is a method of dating organic remains based on the beta decay of the naturally occurring radioactive isotope carbon (14C). Radioactive decay proceeds at a constant rate and regardless of environmental factors that makes it a convenient dating facility (Bronk Ramsey, 2008).

The 14C radionuclide is formed in the upper atmosphere under the influence of cosmic rays, then the 14C isotopes spreads through all layers of the atmosphere, participating in the biochemical processes of living organisms. Through food chains and chemical transformations, the 14C isotopes enters living organisms, in which carbon isotopes maintain the concentration ratio as in the atmosphere. When an object falls out of carbohydrate metabolism, for example, at the death of an organism, its replenishment with all carbon isotopes stops, while the concentration of stable isotopes does not change, and the radioactive decay of 14C continues at a constant rate - this is the starting point for dating. The radioactive 14C atom is unstable and gradually transforms into nitrogen atoms, the half-life of 14C is 5730 ± 40 years. With the help of radiocarbon analysis, it is possible to estimate the age of samples no older than 50 thousand years, after which the radionuclides will exhaust themselves (Kulkova, 2016).

By comparing the content of the radioactive isotope 14C in the sample and in the atmosphere radiocarbon dating provides age of the object. However, in different time periods and at different latitudes the accumulation of 14C fluctuates due to changes in the intensity of cosmic rays, solar activity and a number of anthropogenic factors. To calculate the absolute geologic age the 14C derived age must be converted to an equivalent calendar through the calibration curve that compensates atmospheric carbon concentration fluctuations. Calibration curves are based on independently dated archives renewing by dendrochronology data or uranium series dating of corals (Bronk Ramsey, 2008). The latest update was released in 2020, IntCal20 (International Calibration 2020), which consider the features of the northern and southern hemispheres, as well as the surface layer of the ocean (Reimer et al., 2020; van der Plicht et al., 2020).

The new version of the IntCal20 calibration curve improves the precision for the study of human evolution in terms of chronological overlap between the presence of H. sapiens and Neanderthals, e.g., directly dated fossil of the oldest H. sapiens so far found in Eurasia from

Ust'-Ishim carries a similar amount of Neanderthal DNA ancestry as present-day Eurasians. In this case, the Neanderthals gene flow occurred 7000 to 13,000 years before Ust'-Ishim lived (Fu et al., 2014). In terms of uncalibrated radiocarbon dating, the chronological overlap between the earliest H. sapiens (Bacho Quiro Cave) and the latest H. neanderthalensis (Saint Cesaire Cave) in Europe is 6250 ± 910 years. When calibrating against the IntCal13 and IntCal20 curves, this difference decreases to 5000 ± 860 and 3960 ± 710 calendar years, respectively. This conclusion is important for studies of the mixing of Neanderthals and anatomically modern humans in Europe. It also provides better resolution in relation to climatic events (Bronk Ramsey, 2008; van der Plicht et al., 2020).

Genetic dating

Differences in DNA sequences correspond to nucleotide substitutions that have accumulated since their separation from the most recent common ancestor. When the average number of substitutions occurring per unit of time can be determined, the "molecular clock" rate can be estimated. Assuming a constant rate of change among lineages, molecular clocks used to estimate the time of divergence between closely related species or between populations, that allows a direct conversion of the estimate of the degree of replacement due to genetic data into absolute time scales. Fossil evidence has often been used to estimate the MRCA date of two related groups, thus providing a calibration point for the molecular clock (Rohde et al., 2004).

Calibration points are necessary because they anchor the samples and/or population breakdown to some date, which allows extrapolation of the time scale for the remaining samples and nodes (Shapiro et al., 2004). Traditionally, calibration points are found within the fossil record, and/or biogeographic dates (Ho et al., 2015). Clock calibration is usually performed at Bayesian analysis provided by various software.

Substitution rate estimates can also be derived from the branch length differences ob-

served along the tree. The dating of ancient genomes using genetic calculations based on the assumption that ancient samples have experienced fewer generational changes than living descendant relatives. Recombination occurs at approximately the same rate in all generations, the accumulated number of recombination events provides a molecular clock for the time elapsed or, in the case of an ancient sample, the number of missing generations since it ceased to evolve. This approach gain greater accuracy in substitution rate measurements is to analyze genetic data from ancient specimens for which reliable radiocarbon dates are available. Ancient humans are well suited to provide calibration points for the human mitochondrial molecular clock: reliable radiocarbon dates are available for many specimens, so the number of substitutions accumulated across lineages can be directly converted to the number of substitutions per site per year. Branch shortening - the effect of fewer nucleotide substitutions on ancient branches of a phylogenetic tree compared to modern ones, is commonly seen in phylogenetic studies of ancient humans (Fu et al., 2013). The observed shortening of the branches reflects the relatively shorter time from a common ancestor for ancient human compared to modern: the present-day lineage had more time to accumulate nucleotide substitution. If we know the age of an ancient specimen from a 14C date, we can thus suppose the mutation rate required to produce the observed degree of branch shortening (Moorjani et al., 2016).

Molecular clocks calibrated with ancient DNA have also been used to estimate the age of samples for which direct dates cannot be obtained, such as biological material beyond the range of radiocarbon dating. Phylogenetic trees are reconstructed within a Bayesian framework, and the ages of internal nodes are treated as unknown parameters. The ages of all sampled external nodes are known (e.g., from radiocarbon dating and/or cultural context) except the one that needs to be estimated, which represents an additional model parameter. This method firstly was applied for dating ancient samples on a genome-wide scale research of the Denisova finger bone, also was used to date the Sima de los

Huesos hominin to about 400 kyr ago (Leonardi et al., 2016; Meyer et al., 2012, 2014).

An alternative approach of the branch shortening method based on the recombination clock (Hinch et al., 2011). It was developed for dating ancient non-African individuals, leveraging on the size of Neanderthal blocks present in their genome. The basic idea is to estimate the date of Neanderthal introgression separately for ancient and modern non-African genomes, by together modeling recombination rates across the genome and the decay of Neanderthal ancestry through time. Tested on five ancient Eurasian genomes older than 10,000 years ago, this method provided age estimates largely consistent with radiocarbon dates (Chen et al., 2020; Moorjani et al., 2016).

Sources of aDNA

Certain materials have already been proven as classical for aDNA extracting, nevertheless new DNA reservoirs are still being discovered in a huge variety of forms: desiccated, charred, waterlogged or mineralized (Orlando et al., 2021). Depending on the object of study, these can be plants parts (seeds, pollen, cobs, herbarium specimens), animal remains (leathers, eggshells, mollusk shells), «cultural» artefacts including skin parchments and drinking horns, pottery or birch pitch mastics (Orlando et al., 2021). In addition to DNA from a single species, whole communities can also be recovered from a single sample, such as preserved copro-lites or calcified dental plaque (calculus). This enables metagenomic analyses of the gut and oral microbiota, as well as the detection of pathogens, parasites, and foods. Environmental archives including sediments, ice, and lake cores can be used to rebuild complete paleoecosys-tems at greater scales (Warinner et al., 2014).

The first studies of aDNA were focused on extracting DNA material from soft tissues, for example, from mummified tissues, tissue remains from zoological museum stuffeds, and preserved specimens (Higuchi et al., 1984; Paabo, 1988). But soft tissue, however, is rarely preserved and even if not, it does not mean preserving DNA, e.g., bog bodies have remarkable conservation across thousands of years, how-

ever, the acidic environment of bogs makes recovering genetic material almost impossible even from bones. Djinis, E. (2021) Joshua Lev-ine, P. by C. A. (2017) Thus, the first successful sequenced high-coverage (~20x) sample of aDNA were isolated in 2010 from a ~4 kyr-old hair shaft of a palaeo-Eskimo Greenland, which caused the physicochemical properties of hair keratin - resistance to contamination, that are atypical for soft tissues. The use of mineralized tissues for DNA extraction soon became popular due to more abundant e and much better preservation, so the bones and teeth of vertebrates gained popularity (Miller et al., 2008; M. Rasmussen et al., 2010).

The focus of more recent aDNA researches has switched to mineralized tissue - vertebrate bones. Skeletal tissue, such as bones or teeth, can resist post-mortem degradation better than other types of tissues like skin and hair, thereby it is a suitable biological material for studying human ancient DNA. The choice of skeletal element type and its intra-bone part is important because of differences in DNA preservation. The content of endogenous DNA is a key factor. Since DNA degrades over time and skeletal tissues become colonized by microbes, the percentage of endogenous DNA in ancient samples is often less than 1%, that makes genome-scale analysis impossible or unreasonably expensive.

However, not all bone elements are equally effective in preserving DNA. Pore structure of bones is the resulting mineral dissolution, which increases with higher porosity, leading to more mineral loss and lower bone mineral density. The petrous bone, part of the temporal bone, is the hardest and most dense bone in the mammal body (Robling et al., 2006). The otic capsule surrounds and protects the sensory organs of the inner ear, known as the vestibulocochlear organ. With ear ossicles and teeth serving as additional acceptable options, the petrous bone has been selected for retaining a high degree of endogenous DNA preservation (Damgaard et al., 2015; Gamba et al., 2014).

It has been demonstrated that levels of nucleated cells in the apical cementum layer are unaffected by age whereas concentrations of nuclear DNA in the inner dentine layer substan-

tially decrease throughout the individual's life. Furthermore, a quantitative PCR method revealed that the cementum of old teeth typically contains higher concentrations of human mtDNA than the dentine. The cementum layer, which is exposed at the root surface and may be more susceptible to microbial colonization than dentine, would nonetheless show a lower amount of endogenous DNA. Dentine is typically favored for genetic research because, in addition to host DNA, it also enables the recovery of ancient blood-borne pathogens, despite the fact that tooth cementum might contain significant amounts of host DNA (Adler et al., 2011; Higgins et al., 2013).

Tooth and petrous bones are two the most excellent substrates for ancient genomic research but overall, the petrous bones perform better than the teeth. At once petrous bones display a higher C to T damage rate and have smaller ratios of mtDNA to nuclear DNA compared to tooth cementum (Hansen et al., 2017). When choosing a material, it is necessary to assess the harm: there are two temporal bones in the skull and at least a few teeth under various conditions of preservation. The use of the entire otic capsule by drilling can bring great value losses, but the tooth sample can also be poorly represented, at the same time average obtained coverage of genome can differ several times for the same price as a one genome.

The bone portions that had the highest concentrations of dead osteocytes is ones with the highest chances of having endogenous DNA, this part can avoid phagocytosis by osteoclasts during bone remodeling - removal of mineralized bone by osteoclasts followed by the formation of bone matrix through the osteoblasts that subsequently become mineralized (Robling et al., 2006). Petrous bone undergoes minimal remodeling after childhood which may be one of the explanations for its high endogenous DNA concentration (Srensen & Bretlau, 1997). Excellent DNA preservation also results from other in vivo calcification processes, such as those that turn dental plaques (biofilm of microorganisms on teeth surface) into calculus. It is nearly ubiquitous, allowing simultaneous investigation of pathogen activity, host immunity,

and diet, thereby expanding direct research into common diseases in the human evolutionary past (Warinner et al., 2014, 2015).

There is link between visual and molecular preservation, and well-preserved teeth perform on average just as well as the petrous bones (Hansen et al., 2017). Physical and chemical conditions (temperature, humidity, pH, etc.) that affect the bone from the time an organism dies until the time a skeletal component is excavated are the key determinants of long-term degradation. Even ancient petrous bones can be devoid of any endogenous DNA due to the rates of the decay events, which are influenced by these factors. This enormous variation between burial sites is likely caused by these factors. With the exception of empirical data and the thermal age (Smith et al., 2001), there is a lack information that would help to identify the archaeological or palaeontological sites that are best for DNA preservation. Finally, the density of bones may also help to exclude ambient DNA (Geigl & Grange, 2018).

Challenges in aDNA work

Preserved aDNA is often limited in quantity: highly degraded, fragmented and chemically modified, which can be also complicated by contamination with modern DNA. The successful retrieval of aDNA sequences from remains depends on the postmortem instability of nucleic acids. Post-mortem DNA degradation consists of two separate phases that determine the outcome. The first phase of decay accords to the enzymatic decomposition of most of the organic material, including autolysis and putrefaction. This phase of early diagenesis most influences the number and size of surviving DNA molecules and is probably the reason for the lack of correlation between sample age and the average size of surviving DNA molecules (Kistler et al., 2017; Sawyer et al., 2012).

The first phase is likely to be subject to a lot of variation because it cannot be reconstructed. During the second phase of decay, non-enzymatic chemical degradation seems to correlate with the thermal age of the samples and, in particular, with the deamination of cytosines at the ends of DNA molecules. The third factor deter-

mining the success of paleogenomic approaches is the varying degree of penetration of DNA from the environment into the bone during burial. This environmental DNA dilutes endogenous DNA and can make shotgun sequencing very costly (Sawyer et al., 2012).

Post-mortem damage

DNA is damaged mainly by two phenomena: hydrolysis and oxidation. In alive organism DNA damage such as from UV radiation is fixed by repair mechanisms of cell system. But DNA damage continues after organism death, while repair pathways no longer function, thereby aDNA strands accumulate various postmortem damage. A diversity of chemical reactions is known to affect aDNA strand breaks and include the fragmentation of aDNA molecules into ultra-short pieces. The most dramatic of these changes is aDNA fragmentation, which occurs through hydrolytic depurination followed by elimination reactions, during this process the aDNA double helix breaks apart into pieces that can be millions of times shorter than their original length during life (Gaeta, 2021).

Hydrolysis can induce misincorporations, most commonly the deamination of cytosine to uracil causing (C to T), also deamination adenine to hypoxanthine (A to G), 5-methylcyto-sine to thymine (C to T), guanine to xanthine (G to A). The rate of such misincorporations increases towards the ends of reads when mapped against a reference sequence because of cyto-sine deamination preferentially occurring in the single-stranded overhang termini of aDNA fragments. While the misincorporation of C to T provides convenient genetic signatures for identifying aDNA, it can potentially lead to incorrect results of further sequence analysis (Briggs et al., 2007).

Oxidation as a result of interaction with ionizing radiation or the action of free radicals can induce base misincorporations or block poly-merases and either stop amplification or lead to "jumping PCR" and the production of chimeric sequences. Cross-links either within or between DNA strands via alkylation (Paabo, 1989) will also block the polymerase and stop amplification. Strand breaks caused by nuclease activity,

microorganism degradation, preservation conditions, direct break (hydrolysis), depurination causes abasic site (hydrolysis). These post-mortem changes potentially interfere the manipulation of aDNA and reduce the amount of recoverable genetic information. Most saved DNA strands in ancient remains are short in length (fewer than100 bp).

To reduce the impact of sequence errors caused by damage, DNA extracts can be further processed prior to library generation with a commercially available uracil-DNA glycosyl-ase (UDG) and endonuclease VIII enzyme mixture known as the USER reagent (New England Biolabs). This reagent removes uracils and cleaves the resulting abasic sites, thus repairing the damage but also shortening the DNA molecule (Briggs et al., 2010). Despite the positive effect of reducing sequencing errors, USER processing also has the negative effect of removing damage patterns that are necessary to authenticate aDNA sequences and differentiate them from contaminating DNA. However, despite the elimination of uracils in the template molecules, the sequencing of aDNA USER-treated samples can still shows a slight signal of C to T at the aDNA fragments termini. This signal is mostly driven by the presence of methylated CpG epialleles in the sequence data (Pedersen et al., 2014). For non-mammalian DNA analysis, a raw (non-UDG) DNA library can be initially screened to determine the authenticity of sequence origin, and then second library processed by USER (full-UDG) can be generated for analysis. To decrease the cost of library preparation, there is the USER (partial UDG-treatment) protocol, which eliminates most of the damage, but retains one uracil at each terminus, thereby providing DNA authentication and preserved damage signal at the terminal bases can be cut off computationally during subsequent analysis (Rohland et al., 2015).

Simple modification to high-throughput sequencing library preparation removes uracil residues from ancient DNA and subsequently repairs the DNA fragments, greatly increasing the accuracy of the DNA sequences determined while maintaining DNA sequence yield from precious DNA sources.

Post-mortem damage can accumulate with the age of the sample, but the amount of damage is sample-dependent and linked to preservation conditions, as a result, some recent samples can be more damaged than older ones (Dabney et al., 2013). Under favorable conditions, DNA can survive for thousands of years in the remains. Cold, dry, temperature-stable environments such as permafrost regions and caves are among the best sources of well-preserved specimens. Preservation in cold environments may reduce nuclease activity, reducing some of the damage that occurs immediately after death. The rate of depurination is influenced by temperature, among other factors (Shapiro et. al., 2004), which explains why the most extreme survival of DNA was recorded in ice cores (Willerslev & Cooper, 2005). With rare exceptions, it is possible to effectively conduct aDNA study of samples extracted from the remains found in the equatorial climate, therefore sampling for paleoge-netic studies poorly represented at the territory of Africa, such as genome-wide DNA data from 4 children from Shum Laka (Cameroon) buried 3-8 kyr - one of the earliest known archaeological sites within the probable homeland of the Bantu language group (Lipson et al., 2020). Also, some of the populations stay understudied due to various burial practices in cultures.

Contamination

Not all ancient samples are equally possible to be affected by contamination. Studies of ancient humans or microorganisms are at highest risk for contamination due to the pervasive nature of both potential contaminants and challenges distinguishing a potential contaminant from an authentic ancient sequence. Human contamination can be introduced to ancient human samples in several ways: during excavation, for example, bones and teeth are typically handled in non-sterile environments by bare hands. Bones and teeth are also sometimes cleaned by washing in water, which can contain human cells like skin flakes. This is a problem because hydroxyapatite, which is the main mineral component of bone, tooth enamel, and den-

tin, absorbs DNA in a liquid environment (Korlevic et al., 2015). Ancient samples can also be contaminated during museum storage, both through touching and contact with other samples.

At any point during the processing of an aDNA sample, contamination can happen. The sample itself can be contaminated. Additionally, contamination may be introduced during subsequent stages of an experiment, such as DNA extraction, the creation of a sequencing library, or PCR setup. Reagents may be contaminated, laboratory staff may introduce their DNA, or any DNA brought into the lab on their person, their shoes, or their clothing, and airborne particles may enter through the building's air supply. Another potential source of contaminated DNA is previously amplified DNA found in the lab setting. Over a million copies of the template may be present in even the minuscule amount of DNA that is released when a tube is opened. The laboratory where prehistoric samples are created and any laboratory where samples are processed after amplification should be kept strictly separate (Fulton & Shapiro, 2019).

The reduced size of the DNA molecules makes it difficult to separate endogenous DNA of interest from contaminating DNA and from co-extracted small molecules that could act as inhibitors in toward enzymatic reactions. After death, tissues are colonized by microbial decomposers, which can introduce exogenous contamination by microbial DNA that in some cases represents >99% of recovered DNA (Green et al., 2010a). The most challenging complication of aDNA research stems from the small proportion of surviving copies of endogenous DNA in an extract, compared to the ubiquitous nature of DNA in the environment. In an ideal experiment setup collected from excavated remains samples should be immediately with minimal handing processed to limit potential contamination from modern sources, including staffs and storage facilities (Fulton & Shapiro, 2019). There are numerous examples of studies where contamination has led to erroneous results, the field of aDNA has developed various techniques to identify, remove or re-

duce contamination introduced during post-excavation storage and handling (Gamba et al., 2016; Korlevic et al., 2015).

Even when the level of contamination is extremely low, PCR will preferentially amplify modern DNA over damaged ancient molecules. Copies of the targeted fragment may contain blocking lesions, for example, which affect pol-ymerase processing, or may simply be in low abundance so that PCR enters the exponential phase many cycles after the reaction has begun. If only a few contaminant molecules are present and amplified during the initial cycles of the PCR, these will rapidly outnumber (and outcompete) amplification of the authentic ancient DNA (Fulton & Shapiro, 2019).

Computational pipelines have been developed to detect the presence of contaminating DNA after DNA sequencing has been performed. It is more difficult to identify authentic and contaminated DNA when the aDNA is more closely related to the potentially contaminating DNA. In archaic hominins such as Neanderthals, all mitochondrial genome sequences published so far fall outside the variation of modern humans, making present-day human contamination estimates achievable if mitochondrial coverage is sufficiently high (Green et al., 2010b; Reich et al., 2010; Renaud et al., 2019).

aDNA research design

Work with ancient DNA is time-consuming and expensive. However, when care and appropriate precautions are taken from the outset, it can be a powerful tool for investigating evolutionary processes that cannot be addressed using modern data alone.

The peculiarities of working with aDNA require that the laboratory should be maximally optimized in order to exclude contamination of ancient samples. The researcher is responsible for creating a critical study design, with thoughtful experimental objectives. Not all laboratories can afford full equipment, methods and materials, some of the requirements may be optional, but do not neglect the conditions for working with ancient samples. With the move from PCR-based analysis to NGS, it has be-

come easier to identify and prevent contamination, but be attentive, stringency and scientific soundness of action is a key for work (Cooper & Poinar, 2000; Gilbert et al., 2006).

Appropriate setup of the aDNA workspace is critical. The aDNA facility should be isolated from locations where PCR is performed routinely, preferably in a separate building that does not house any PCR labs. Ideally, the room will be positively pressurized, so that air does not flow in from the adjoining room/hallway when the door is opened.

One should assume that all reagents and tools are contaminated with human DNA, even those marked as sterile. All equipment should be decontaminated before use, for example, by UV irradiation, baking, acid-treatment or bleach. All of the methods are aimed at destroying DNA and must be strictly followed according to the protocol in order to exclude the remains of fragmented DNA. In an effort to clean ancient bones and teeth of surface pollutants, numerous techniques have been proposed. These include physically removing the outer surface, washing it using chemicals like water, EDTA, bleach, ethanol, acid, or hydrogen peroxide for an extended period of time, UV-light-ing the sample, and/or extracting the interior material (Kemp & Smith, 2005). Bleaching the bone powder appears to degrade contaminant DNA more quickly than endogenous DNA if the material is well-preserved (Salamon et al., 2005).

It is important rule in the laboratory to isolate samples as much as possible before the amplification step and to follow the concentration gradient of potential contaminants. According to this rule, equipment and reagents can only be moved in one direction - from a clean area to a dirty one, or in the direction of the previous stages. Division into zones: sample preparation zone; decontamination and preparation for extraction; zone for purification and preparation of libraries for sequencing. If it is necessary to move against the gradient, the worker must change suit and take a shower.

Negative extraction and PCR controls obligatory. Extraction and PCR controls containing no DNA (negative controls) should be carried

out next to the samples set at every step. Appropriate molecular behavior for PCR-based studies, an inverse relationship should be observed between the length of the targeted PCR fragment and the strength of the amplification. Reproducibility within-lab replication of PCR amplifications, overlapping PCR products, and amplifications from multiple DNA extractions must be consistent. Cloning - at minimum 10% of subset of PCR amplifications should be cloned to assess damage and detect nuclear mi-tochondrial insertions, chimeric sequences from jumping PCR, and contaminants (Handt et al., 1996).

Biochemical preservation of extracted DNA and quantitation of starting material. DNA from associated remains using for confidence. Use of a «carrier DNA» negative in PCR-based assays for cleaning from low concentrated pollutants. Time-dependent or preservation-dependent pattern of DNA damage and sequence diversity. Critical assessment of the sensibility of the results obtained from an ancient DNA experiment is an important aspect of aDNA research.

Obtaining almost all aDNA data to date requires PCR amplification of libraries to amplify signals prior to sequencing. There are 2 types of NGS applying for aDNA: double-stranded library preparation and single-stranded library preparation. Each has different characteristics in terms of costs, hands-on time and sensitivity. In the double-stranded preparation method, aDNA molecules are end-repaired and ligated to double-stranded adapters whereas in the single-stranded preparation method, heat-denatured aDNA templates and adapters are ligated as single-stranded molecules (Briggs et al., 2007; Gansauge et al., 2020; Gansauge & Meyer, 2013; Kircher et al., 2012). Advantage of the single-stranded preparation method is that it enables the molecular selection of DNA templates carrying evidence of post-mortem DNA damage, which can increase the fraction of endogenous DNA incorporated into sequencing libraries and thus reduce downstream sequencing costs 145 146.

Although single-molecule sequencing has provided a PCR-free alternative for sequencing aDNA libraries, the generation of almost all

aDNA data to date has required library PCR amplification to boost signals prior to sequencing. (Dabney & Meyer, 2012). Conventional DNA polymerases exhibit different preferences for templates of a particular size and/or base composition and differ in their ability to «correct» to bypass post-mortem damage, so the use of other polymerases such as Pfu Turbo Cx, Herculase II and Accuprime Pfx is necessary. They can perform uniform amplification of fragments with different base compositions and lengths, as well as maintain the original complexity of the library (Seguin-Orlando et al.,

2015).

Endogenous DNA due to low concentration in libraries can make gun sequencing of entire ancient genomes uneconomical, target-enrichment approaches have thus been developed to focus sequencing efforts on library content of particular interest Target enrichment is a DNA preparation step prior to sequencing whereby DNA is either directly amplified (based on PCR) or captured (hybrid capture-based). These improved DNA fragments quality that can then be sequenced using DNA sequencers. Targeted enrichment is a targeted sequencing technique, also known as resequencing. Targeted enrichment improves and sequences only a portion of the entire genome, or regions of interest, without sequencing the entire genome of the sample. To focus only on the detection of a genome or DNA sample, targeted enrichment is required (Lang et al., 2020; Suchan et al.,

2016).

Despite occasional use of alternative methods, Illumina instruments have produced the vast majority of aDNA sequence data due to its accessibility, large data output, cost-effectiveness, and generally low error rates. Additionally, it functions best with relatively short DNA sequences (300 bp) and is ideally suited for sequencing DNA with a length of 50-150 bp, which is the average for aDNA.

The choice of the most suitable methods for aDNA sequence analysis depends on the the research project's objectives, such as whether they are focused on population history modeling, microbiological profiling, or paleoenvironmental reconstruction. How-

ever, there are several processes in the analytical workflow that apply to the majority of projects. Raw sequencing data processing, alignment against reference genomes and/or sequence databases, and the evaluation of authenticity and error rates, including miscoding lesions brought on by post-mortem damage, are all part of these processes.

Sequencing DNA molecules in one or both directions can provide single readings or paired-end reads. Index demultiplexing, read trimming, and consensus sequence creation are the first processing steps. Read trimming removes adapters and/or low-quality terminal sequences, and consensus sequence creation collapses overlapping read pairs into a single read pair. Processed reads are usually aligned to reference genomes of the focal species, such as the human genome, as well as potential microbial pathogens that may have infected the person during life. This is often done using BWA or Bowtie2 (Li & Durbin, 2010; Schubert et al., 2016).

The next steps in the analysis are error deletion and endogenous DNA authentication, partly discussed in the Challenges in aDNA work chapter.

Ancient microbiome

Together with classical approaches in paleo-pathology and paleodemography, aDNA from microorganisms, including pathogens and commensals, can provide insights into the health of ancient peoples as well as shifts in diets and disease ecology.

Just as in the study of ancient genomes, a significant breakthrough in the ancient microbi-omes has occurred since the introduction of NGS technology. The accumulated array of sequence data allowed authentication based on template sequences. The analysis of poorly preserved ancient material is facilitated by the use of library enrichment for NGS. Targeted enrichment contributed to the study of the first ancient bacterial genome of Yersinia pestis, which was extracted from the teeth of a 14th-century plague victim with a 30-fold coverage (Bos et al., 2011). Up to this point, there had been controversy about the possibility of isolating

plague bacteria from teeth (Drancourt & Ra-oult, 2014).

The material for the study of microbiome DNA contains endogenous and exogenous DNA reflecting the pre-mortem and post-mortem diversity of the microbiome respectively. Endogenous DNA consists of host-associated commensal species and epidemic pathogens while exogenous DNA contains bacteria from the environment, contamination from human interaction with samples and other contaminants (Warinner et al., 2017). False-positive results often occur due to the relationship of pathogenic bacteria and bacteria due to the environment. Authentication is also complicated by the inconvenience of applying signature analysis of postmortem damage since postmortem colonization leads to a number of similar damage patterns from ancient to modern, making it difficult to distinguish (Weiß et al., 2020).

In this case of the problem of contamination and exogenous and endogenous DNA mixing is solved by using mapDamage software to calculate damage and authenticate ancient samples in such genetically mixed samples (Ginolhac et al., 2011). Another method effectively amplifies age associated degradation patterns in microbial ancient and modern mixtures is sequencing DNA libraries enriched in molecules carrying uracils. This facilitates the discovery of authentic ancient microbial taxa in cases where degradation patterns are difficult to detect due to large sequence divergence in micro-bial mixtures (Weiß et al., 2020).

Another difficulty of the analysis is that the balance of the microbiome shifts after death, while anaerobic bacteria survive more successfully, such obtained results may be misinterpreted. The composition of a microbe's cellular membrane plays a key role in the postmortem preservation Gram-positive bacteria, whose nuclear acids are rich in GC content have advantage, also like mycobacteria, whose membranes includes mycolic acid that perform a protective function (Rivera-Perez et al., 2016). Glycosidic ether lipids and hopanoids are components of bacterial membranes (not found in eukaryotes) protect DNA from enzymatic degradation (Schouten et al., 2010).

Due to the conservation advantages and despite particular problems, researchers have been able to gain a wealth of information from the study of bacterial aDNA. The history of bacterial DNA study begins with the analysis of 16S rRNA of a single species and ends with the reconstruction and comparison of complete genomes from several genera. This is a widely used method for identifying microorganisms by comparing the nucleotide sequences of the gene encoding 16S rRNA. Low-cost and fast amplification and sequencing of the 16S rRNA gene can be easily carried out in mixed microbial communities without cultivation, since the sequence of this gene is similar in a large pool of microbes, and it can be used universal primers. Further analysis allows diversity to be distinguished as taxonomic units (OTUs), convenient measurable proxies for microbes related by origin (Kowalchuk et al., 2004).

Ancient microbial DNA has been isolated from various sample types, including permafrost, halites, amber, bones, internal organs, dental pulp, and coprolites. Coprolites are mummified or fossilized feces and are perhaps one of the richest sources of ancient microbes. Coprolites have been found in dry, cold and even tropical environments (Poinar et al., 2003; Santiago-Rodriguez et al., 2013). Early studies used morphological observations to determine the parasitological composition of coprolites. Similarly, the possible diet of an individual was determined on the basis of grains and seeds preserved in the feces. Also, it was shown that helminth eggs (Tito et al., 2012). have persisted for thousands of years, although it is not clear if they were still infectious. Paleoparasitological studies of coprolites were among the first to suggest that the dietary habits and even lifestyles of ancient cultures (such as hunter-gatherers and farmers) could be discerned from ancient specimens (Cano et al., 2014).

In caprolites, it is possible to determine both pathogenic and microorganisms characteristic of a healthy organism (52). A more interesting direction is the study of the reflection of diet on the intestinal microbiome of ancient organisms, especially humans by not

only food remains but using metabolites remaining in coprolites (Kowalchuk et al., 2004). This demonstrates that the presence or absence of DNA from spe-cific microorganisms in coprolites may be important to investigate diet but also that the metabolic pathways for the degradation of key compounds may be a great source of information as well. Geographically as well as ethnically distinct populations may have different variations in microbiome diversity (Tito et al., 2012; Yatsunenko et al., 2012).

The transition to farming and agriculture with the changes to a sedentary lifestyle contributed to the growth of infectious diseases and the emergence of new infections (Harper & Armelagos, 2013). The progression to denser, more fertile human populations in close proximity to domestic animals has been termed the Neolithic demographic transition. The use of the molecular clock method showed that at least three paleontological pathogens are associated with this event in human history: Y. pestis, M. tuberculosis, and M. leprae (Pearce-Duvet, 2006).

As the causal agent of plague, Y. pestis is responsible for multiple human pandemics with millions of deaths through human history and is therefore a crucial species for epidemiological research According to paleogenetic data, the first appearance of the plague occurred about 5 thousand years ago, which coincides with the expansion from central Eurasia and eastern and central Europe (de Barros Damgaard et al., 2018; Rasmussen et al., 2015). The first and second plague pandemics take place in 6th -8th and 14th -18th centuries, the third pandemic started in the mid-19th and is still going on (Wagner et al., 2014).

Y. pestis is a slowly evolving and well-studied pathogen with a well-documented historical record and many paleogenomes available. However, it still lacks a reliable chronology of evolution, highlighting the difficulty of mapping the bacterial past (Arning & Wilson, 2020). Studying the spread of come across pathogens between host and microorganism is weighty to discover the mechanisms by which these pathogens interact with humans and animals; it also

helps in finding new treatments (Dcosta et al., 2011).

The human body, with its microbiome diversity, is a site for horizontal gene transfer, potentially making it a vessel for the development of antimicrobial resistance. Studies of the oral mi-crobiome of modern humans have found genetic sequences associated with antibiotic resistance on dental calculus (Xie et al., 2010). Later, sequences homologous to antibiotic resistance genes found in oral and pathogenic bacteria were identified in ancient calculus. Despite the poorly researched role of these genes, their presence indicates that even before the use of therapeutic antibiotics, the molecular mechanisms of antibiotic resistance were already at a low level (Sommer et al., 2009).

Although the exact function of these genes in our samples is unclear, their presence nonetheless demonstrates that the low-level bio-molecular mechanism for broad-spectrum antibiotic resistance has long been present in the human microbiome, showing how the oral mi-crobiome functions as a source and reservoir of new antibiotic resistance (Warinner et al., 2014, 2017).

Conclusion

To obtain valid data on ancient genomes, that is necessary to responsibly carry out research at all its stages: from archaeological excavations to strict laboratory processes and ethical standards. Successful work requires close collaboration between geneticists and archaeologists, as well as interdisciplinary specialists.

Paleogenetics is becoming more and more widespread and provides an amount of sciences by fast accumulating data with the remarkable rate. If the basic aim of ancient DNA research was more driven by enthusiasm and a desire to discover new human's ancestors and evolutional processes, then today's data show how much paleogenetics has advanced and highly specialized. Bringing practically applied genetic data, especially to medicine, paleogenetics has developed a respectful attitude towards itself through a difficult path with restrictions and failures to incredible annual news.

Acknowledgments

This work has been supported by the grants of the Russian Science Foundation, RSF 22-24-00681.

References

ADLER C.J., HAAK W., DONLON D. & COOPER A. (2011): Survival and recovery of DNA from ancient teeth and bones. Journal of Archaeological Science 38(5), 956-964. https://doi.org/10.10167J.JAS.2010.11.010.

ALLENTOFT M.E., COLLINS M., HARKER D., HAILE J., OSKAM C.L., HALE M L., CAMPOS P.F., SAMANIEGO J.A., GILBERT M.T.P., WILLERSLEV E., ZHANG G., SCOFIELD R.P., HOLDA-WAY R.N. & BUNCE M. (2012): The half-life of DNA in bone: measuring decay kinetics in 158 dated fossils. Proceedings of the Royal Society B: Biological Sciences 279(1748), 4724-4733. https://doi.org/10.1098/rspb.2012.1745.

ARNING N. & WILSON D.J. (2020): The past, present and future of ancient bacterial DNA. Microbial Genomics 6(7), 1-19. https://doi.org/10.1099/MGEN.0.000384.

BOS K.I., SCHUENEMANN V.J., GOLDING G.B., BURBANO H.A., WAGLECHNER N., COOMBES B.K., MCPHEE JB., DEWITTE S.N., MEYER M., SCHMEDES S., WOOD J., EARN D.J.D., HERRING D.A., BAUER P., POINAR H.N. & KRAUSE J. (2011): A draft genome ofYersinia pestis from victims of the Black Death. Nature 478(7370), 506. https://doi.org/10.1038/NATURE10549.

BRIGGS A.W., STENZEL U., JOHNSON P.L.F., GREEN R.E., KELSO J., PRÜFER K., MEYER M., KRAUSE J., RONAN M.T., LACHMANN M. & PÄÄBO S. (2007): Patterns of damage in genomic DNA sequences from a Neandertal. Proceedings of the National Academy of Sciences of the United States of America 104(37), 14616-14621. https://doi.org/10.1073/PNAS.0704665104/SUPPL_FILE/IMAGE1185.GIF.

BRIGGS A.W., STENZEL U., MEYER M., KRAUSE J., KIRCHER M. & PÄÄBO S. (2010): Removal of deaminated cytosines and detection of in vivo methylation in ancient DNA. Nucleic Acids Research 38(6), e87. https://doi.org/10.1093/NAR/GKP1163.

BRONK RAMSEY C. (2008): RADIOCARBON DATING: REVOLUTIONS IN UNDERSTANDING*. Archaeometry 50(2), 249-275. https://doi.org/10.1111/J.1475-4754.2008.00394.X.

CANO R.J., RIVERA-PEREZ J., TORANZOS G.A., SANTIAGO-RODRIGUEZ T.M., NARGANES-STORDE Y.M., CHANLATTE-BAIK L., GARCiA-ROLDÄN E., BUNKLEY-WILLIAMS L. & MASSEY S.E. (2014): Paleomicrobiology: Revealing Fecal Microbiomes of Ancient Indigenous Cultures. PLoS ONE 9(9), 106833. https://doi.org/10.1371/JOURNAL.PONE.0106833.

CHEN L., WOLF A.B., FU W., LI L. & AKEY J.M. (2020): Identifying and Interpreting Apparent Neanderthal Ancestry in African Individuals. Cell 180(4), 677-687.e16. https://doi.org/10.1016ZJ.CELL.2020.01.012.

COOPER A. & POINAR H.N. (2000): Ancient DNA: do it right or not at all. Science (New York, N.Y.) 289(5482), 1139-1139. https://doi.org/10.1126/SCIENCE.289.5482.1139B.

DABNEY J. & MEYER M. (2012): Length and GC-biases during sequencing library amplification: A comparison of various polymerase-buffer systems with ancient and modern DNA sequencing libraries. BioTechniques 52(2). https://doi.org/10.2144/000113809/ASSET/IMAGES/LARGE/FIGURE4.JPEG.

DABNEY J., MEYER M. & PÄÄBO S. (2013): Ancient DNA Damage. Cold Spring Harbor Perspectives in Biology 5(7). https://doi.org/10.1101/CSHPERSPECT.A012567.

DAMGAARD P.B., MARGARYAN A., SCHROEDER H., ORLANDO L., WILLERSLEV E. & ALLENTOFT M.E. (2015): Improving access to endogenous DNA in ancient bones and teeth. Scientific Reports 5(11184), 1-12. https://doi.org/10.1038/srep11184.

D'COSTA V.M., KING C.E., KALAN L., MORAR M., SUNG W.W.L., SCHWARZ C., FROESE D., ZAZULA G., CALMELS F., DEBRUYNE R., GOLDING G. B., POINAR H.N. & WRIGHT G.D. (2011): Antibiotic resistance is ancient. Nature 477(7365), 457-461. https://doi.org/10.1038/na-ture10388.

DE BARROS DAMGAARD P., MARCHI N., RASMUSSEN S., PEYROT M., RENAUD G., KORNELIUS-SEN T., MORENO-MAYAR J.V., PEDERSEN M.W., GOLDBERG A., USMANOVA E., BAIMUKHA-NOV N., LOMAN V., HEDEAGER L., PEDERSEN A G., NIELSEN K., AFANASIEV G., AK-MATOV K., ALDASHEV A., ALPASLAN A., ... WILLERSLEV E. (2018): 137 ancient human genomes from across the Eurasian steppes. Nature 557(7705), 369-374. https://doi.org/10.1038/S41586-018-0094-2.

DE BRUYN M., HOELZEL A.R., CARVALHO G.R. & HOFREITER M. (2011): Faunal histories from Holocene ancient DNA. Trends in Ecology & Evolution 26(8), 405-413. https://doi.org/10.10167J.TREE.2011.03.021.

DJINIS E. (2021): Last meal of sacrificial bog body was surprisingly unsurprising, researchers say. National Geographic, 20 July. Available at: https://www.nationalgeographic.com/history/article/tollund-mans-last-meal (Accessed: November 10, 2022).

DRANCOURT M. & RAOULT D. (2014): Yersinia pestis and the three plague pandemics. The Lancet Infectious Diseases 14(10), 918-919. https://doi.org/10.1016/S1473-3099(14)70877-1.

DRUMMOND A.J., RAMBAUT A., SHAPIRO B. & PYBUS O.G. (2005): Bayesian Coalescent Inference of Past Population Dynamics from Molecular Sequences. Molecular Biology and Evolution 22(5), 11851192. https://doi.org/10.1093/MOLBEV/MSI103.

FLINK LG., ALLEN R., BARNETT R., MALMSTRÖM H., PETERS J., ERIKSSON J., ANDERSSON L., DOBNEY K. & LARSON G. (2014): Establishing the validity of domestication genes using DNA from ancient chickens. Proceedings of the National Academy of Sciences of the United States of America 111(17), 6184-6189. https://doi.org/10.1073/pnas.1308939110.

FRANTZ L.A.F., BRADLEY D.G., LARSON G. & ORLANDO L. (2020): Animal domestication in the era of ancient genomics. Nature Reviews Genetics 2020 21:8, 21(8), 449-460. https://doi.org/10.1038/s41576-020-0225-0.

FU Q., LI H., MOORJANI P., JAY F., SLEPCHENKO SM., BONDAREV A.A., JOHNSON P.L.F., AX-IMU-PETRI A., PRÜFER K., DE FILIPPO C., MEYER M., ZWYNS N., SALAZAR-GARCiA DC., KUZMIN Y.V., KEATES S.G., KOSINTSEV P.A., RAZHEV D.I., RICHARDS M P., PERI-STOV N.V., ... PÄÄBO S. (2014): The genome sequence of a 45,000-year-old modern human from western Siberia. Nature 514(7523), 445. https://doi.org/10.1038/NATURE13810.

FU Q., MITTNIK A., JOHNSON P.L.F., BOS K., LARI M., BOLLONGINO R., SUN C., GIEMSCH L., SCHMITZ R., BURGER J., RONCHITELLI A.M., MARTINI F., CREMONESI R.G., SVOBODA J., BAUER P., CARAMELLI D., CASTELLANO S., REICH D., PÄÄBO S. & KRAUSE J. (2013): A Revised Timescale for Human Evolution Based on Ancient Mitochondrial Genomes. Current Biology 23(7), 553-559. https://doi.org/10.1016ZJ.CUB.2013.02.044.

FULTON T.L. & SHAPIRO B. (2019): Setting Up an Ancient DNA Laboratory (pp. 1 -13). https://doi.org/10.1007/978-1-4939-9176-1_1.

GAETA R. (2021): Ancient DNA and paleogenetics: risks and potentiality. Pathologica 113(2), 141. https://doi.org/10.32074/1591-951X-146.

GAMBA C., HANGH0J K., GAUNITZ C., ALFARHAN A.H., ALQURAISHI S.A., AL-RASHEID K.A.S., BRADLEY D.G. & ORLANDO L. (2016): Comparing the performance of three ancient DNA extraction methods for high-throughput sequencing. Molecular Ecology Resources 16(2), 459-469. https://doi.org/10.1111/1755-0998.12470.

GAMBA C., JONES E.R., TEASDALE M.D., MCLAUGHLIN R.L., GONZALEZ-FORTES G., MATTI-ANGELI V., DOMBOROCZKI L., KOVÄRI I., PAP I., ANDERS A., WHITTLE A., DANI J., RACZKY P., HIGHAM T.F.G., HOFREITER M., BRADLEY D.G. & PINHASI R. (2014): Genome flux and stasis in a five millennium transect of European prehistory. Nature Communications 5(5257), 1-9. https://doi.org/10.1038/ncomms6257.

GANSAUGE M.T., AXIMU-PETRI A., NAGEL S. & MEYER M. (2020): Manual and automated preparation of single-stranded DNA libraries for the sequencing of DNA from ancient biological remains and other sources of highly degraded DNA. Nature Protocols 15(8), 2279-2300. https://doi.org/10.1038/s41596-020-0338-0.

GANSAUGE M.T. & MEYER M. (2013): Single-stranded DNA library preparation for the sequencing of ancient or damaged DNA. Nature Protocols 8(4), 737-748. https://doi.org/10.1038/nprot.2013.038.

GEIGL E M. & GRANGE T. (2018): Ancient DNA: The quest for the best. Molecular Ecology Resources 18(6), 1185-1187. https://doi.org/10.1111/1755-0998.12931.

GILBERT M.T.P., HANSEN A.J., WILLERSLEV E., TURNER-WALKER G. & COLLINS M. (2006): Insights into the processes behind the contamination of degraded human teeth and bone samples with exogenous sources of DNA. International Journal of Osteoarchaeology 16(2), 156-164. https://doi.org/10.1002/OA.832.

GINOLHAC A., RASMUSSEN M., GILBERT M.T.P., WILLERSLEV E. & ORLANDO L. (2011): mapDamage: testing for damage patterns in ancient DNA sequences. Bioinformatics 27(15), 2153-2155. https://doi.org/10.1093/BIOINFORMATICS/BTR347.

GREEN R.E., KRAUSE J., BRIGGS A.W., MARICIC T., STENZEL U., KIRCHER M., PATTERSON N., LI H., ZHAI W., FRITZ M.H.Y., HANSEN N.F., DURAND E.Y., MALASPINAS AS., JENSEN J.D., MARQUES-BONET T., ALKAN C., PRÜFER K., MEYER M., BURBANO H.A., ... PÄÄBO S. (2010a): A draft sequence of the neandertal genome. Science 328(5979), 710-722. https://doi.org/10.1126/SCIENCE.1188021/SUPPL_FILE/GREEN_SOM.PDF.

GREEN R.E., KRAUSE J., BRIGGS A.W., MARICIC T., STENZEL U., KIRCHER M., PATTERSON N., LI H., ZHAI W., FRITZ M.H.Y., HANSEN N.F., DURAND E.Y., MALASPINAS AS., JENSEN J.D., MARQUES-BONET T., ALKAN C., PRÜFER K., MEYER M., BURBANO H.A., ... PÄÄBO S. (2010b): A draft sequence of the neandertal genome. Science 328(5979), 710-722. https://doi.org/10.1126/SCIENCE.1188021/SUPPL_FILE/GREEN_SOM.PDF.

HANDT O., KRINGS M., WARD RH. & PÄÄBO S. (1996): The retrieval of ancient human DNA sequences. American Journal of Human Genetics 59(2), 368. /pmc/articles/PMC1914746/?re-port=abstract.

HANSEN H.B., DAMGAARD P.B., MARGARYAN A., STENDERUP J., LYNNERUP N., WILLERSLEV E. & ALLENTOFT M.E. (2017): Comparing Ancient DNA Preservation in Petrous Bone and Tooth Cementum. PLOS ONE 12(1), e0170940. https://doi.org/10.1371/journal.pone.0170940.

HARPER K.N. & ARMELAGOS G.J. (2013): Genomics, the origins of agriculture, and our changing microbe-scape: Time to revisit some old tales and tell some new ones. American Journal of Physical Anthropology 152(Suppl 57), 135. https://doi.org/10.1002/AJPA.22396.

HIGGINS D., KAIDONIS J., TOWNSEND G., HUGHES T. & AUSTIN J.J. (2013): Targeted sampling of cementum for recovery of nuclear DNA from human teeth and the impact of common decontamination measures. Investigative Genetics 4(1). https://doi.org/10.1186/2041-2223-4-18.

HIGUCHI R., BOWMAN B., FREIBERGER M., RYDER O.A. & WILSON A.C. (1984): DNA sequences from the quagga, an extinct member of the horse family. Nature 312(5991), 282-284. https://doi.org/10.1038/312282a0.

HINCH AG., TANDON A., PATTERSON N., SONG Y., ROHLAND N., PALMER CD., CHEN G.K., WANG K., BUXBAUM S.G., AKYLBEKOVA E L., ALDRICH M C., AMBROSONE C.B., AMOS C., BANDERA E.V., BERNDT S.I., BERNSTEIN L., BLOT W.J., BOCK C.H., BOERWIN-KLE E., ... MYERS S.R. (2011): The landscape of recombination in African Americans. Nature 476(7359), 170-175. https://doi.org/10.1038/NATURE10336.

HO A.K., SIDANIUS J., KTEILY N., SHEEHY-SKEFFINGTON J., PRATTO F., HENKEL K.E., FOELS R. & STEWART A.L. (2015): The nature of social dominance orientation: Theorizing and measuring preferences for intergroup inequality using the new SDO7 scale. Journal of Personality and Social Psychology 109(6), 1003-1028. https://doi.org/10.1037/PSPI0000033.

JENSEN E.L., DÍEZ-DEL-MOLINO D., GILBERT M.T.P., BERTOLA L.D., BORGES F., CUBRIC-CU-RIK V., DE NAVASCUÉS M., FRANDSEN P., HEUERTZ M., HVILSOM C., JIMÉNEZ-MENA B., MIETTINEN A., MOEST M., PECNEROVÁ P., BARNES I. & VERNESI C. (2022): Ancient and historical DNA in conservation policy. Trends in Ecology & Evolution 37(5), 420-429. https://doi.org/10.1016/J.TREE.2021.12.010.

JOSHUA LEVINE P. BY C.A. (2017): Europe's famed bog bodies are starting to reveal their secrets, Smithsonian Magazine. Available at: https://www.smithsonianmag. com/science-nature/europe-bog-bodies-reveal-secrets-180962770/ (Accessed: November 10, 2022).

KEMP B.M. & SMITH D.G. (2005): Use of bleach to eliminate contaminating DNA from the surface of bones and teeth. Forensic Science International 154(1), 53-61. https://doi.org/10.1016/J.FOR-SCIINT.2004.11.017.

KIRCHER M., SAWYER S. & MEYER M. (2012): Double indexing overcomes inaccuracies in multiplex sequencing on the Illumina platform. Nucleic Acids Research 40(1), e3-e3. https://doi.org/10.1093/NAR/GKR771.

KISTLER L., WARE R., SMITH O., COLLINS M. & ALLABY R.G. (2017): A new model for ancient DNA decay based on paleogenomic meta-analysis. Nucleic Acids Research 45(11), 6310-6320. https://doi.org/10.1093/NAR/GKX361.

KORLEVIC P., GERBER T., GANSAUGE M.T., HAJDINJAK M., NAGEL S., AXIMU-PETRI A. & MEYER M. (2015): Reducing microbial and human contamination in dna extractions from ancient bones and teeth. BioTechniques 59(2), 87-93. https://doi.org/10.2144/000114320/AS-SET/IMAGES/LARGE/FIGURE4.JPEG.

KOWALCHUK G.A., DE BRUIJN F.J., HEAD IM., AKKERMANS A.D. & VAN ELSAS J.D. (2004): Section 7 - Statistical, computer-assisted and other analyses. Molecular Microbial Ecology Manual, 1319-1560. https://doi.org/10.1007/978-1-4020-2177-0_7.

KRAUSE J., BRIGGS A. W., KIRCHER M., MARICIC T., ZWYNS N., DEREVIANKO A. & PÄÄBO S. (2010): A Complete mtDNA Genome of an Early Modern Human from Kostenki, Russia. Current Biology 20(3), 231-236. https://doi.org/10.1016ZJ.CUB.2009.11.068.

KULKOVA M.A (2016): Radiocarbon dating method, calibration of radiocarbon age and calibration curves. In: Radiocarbon Neolithic chronology of Eastern Europe in the VII-III millennium BC (Eds Mazurkevich A.N., Dolbunova E.V. & Kulkova M.A), pp. 24-31, Smolensk: Svitok.

LANG P.L.M., WEIß C.L., KERSTEN S., LATORRE S.M., NAGEL S., NICKEL B., MEYER M. & BUR-BANO H.A. (2020): Hybridization ddRAD-sequencing for population genomics of nonmodel plants using highly degraded historical specimen DNA. Molecular Ecology Resources 20(5), 1228-1247. https://doi.org/10.1111/1755-0998.13168.

LEONARDI M., LIBRADO P., DER SARKISSIAN C., SCHUBERT M., ALFARHAN A.H., ALQURAISHI S.A., AL-RASHEID K.A.S., GAMBA C., WILLERSLEV E. & ORLANDO L. (2016): Evolutionary Patterns and Processes: Lessons from Ancient DNA. Systematic Biology syw059. https://doi.org/10.1093/sysbio/syw059.

LI H. & DURBIN R. (2010): Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26(5), 589-595. https://doi.org/10.1093/BIOINFORMATICS/BTP698.

LIPSON M., RIBOT I., MALLICK S., ROHLAND N., OLALDE I., ADAMSKI N., BROOMANDKHOSHBACHT N., LAWSON A.M., LÓPEZ S., OPPENHEIMER J., STEW-ARDSON K., ASOMBANG R.N., BOCHERENS H., BRADMAN N., CULLETON B.J., COR-NELISSEN E., CREVECOEUR I., DE MARET P., FOMINE F.L.M., ... REICH D. (2020): Ancient West African foragers in the context of African population history. Nature 577(7792), 665-670. https://doi.org/10.1038/s41586-020-1929-1.

MEYER M., ARSUAGA J.L., DE FILIPPO C., NAGEL S., AXIMU-PETRI A., NICKEL B., MARTÍNEZ I., GRACIA A., DE CASTRO J.M.B., CARBONELL E., VIOLA B., KELSO J., PRÜFER K. & PÄÄBO S. (2016): Nuclear DNA sequences from the Middle Pleistocene Sima de los Huesos hom-inins. Nature 531(7595), 504-507. https://doi.org/10.1038/nature17405.

MEYER M., FU Q., AXIMU-PETRI A., GLOCKE I., NICKEL B., ARSUAGA J.L., MARTÍNEZ I., GRACIA A., DE CASTRO J.M.B., CARBONELL E. & PÄÄBO S. (2014): A mitochondrial genome sequence of a hominin from Sima de los Huesos. Nature 505(7483), 403-406. https://doi.org/10.1038/NA-TURE12788.

MEYER M., KIRCHER M., GANSAUGE M.T., LI H., RACIMO F., MALLICK S., SCHRAIBER JG., JAY F., PRÜFER K., DE FILIPPO C., SUDMANT PH., ALKAN C., FU Q., DO R., ROHLAND N., TANDON A., SIEBAUER M., GREEN R.E., BRYC K., ... PÄÄBO S. (2012): A high-coverage genome sequence from an archaic Denisovan individual. Science 338(6104), 222-226. https://doi.org/10.1126/SCIENCE.1224344/SUPPL_FILE/PAPV2.PDF.

MILLER W., DRAUTZ D.I., RATAN A., PUSEY B., QI J., LESK A.M., TOMSHO LP., PACKARD M.D., ZHAO F., SHER A., TIKHONOV A., RANEY B., PATTERSON N., LINDBLAD-TOH K., LANDER E.S., KNIGHT J.R., IRZYK G.P., FREDRIKSON K.M., HARKINS T.T., ... SCHUSTER S.C. (2008): Sequencing the nuclear genome of the extinct woolly mammoth. Nature 456(7220), 387-390. https://doi.org/10.1038/nature07446.

MILLER W., SCHUSTER SC., WELCH A.J., RATAN A., BEDOYA-REINAA O.C., ZHAO F., KIM H.L., BURHANS R.C., DRAUTZ D.I., WITTEKINDT N E., TOMSHO L P., IBARRA-LACLETTE E., HERRERA-ESTRELLA L., PEACOCK E., FARLEY S., SAGE G.K., RODE K., OBBARD M., MON-TIEL R., ... LINDQVIST C. (2012): Polar and brown bear genomes reveal ancient admixture and demographic footprints of past climate change. Proceedings of the National Academy of Sciences of the United States of America 109(36), E2382. https://doi.org/10.1073/PNAS.1210506109/-/DCSUPPLE-MENTAL.

MITCHELL K.J., LLAMAS B., SOUBRIER J., RAWLENCE N.J., WORTHY T.H., WOOD J., LEE M.S. Y. & COOPER A. (2014): Ancient DNA reveals elephant birds and kiwi are sister taxa and clarifies ratite bird evolution. Science 344(6186), 898-900. https://doi.org/ 10.1126/SCIENCE.1251981/SUPPL FILE/MITCHELL.SM.PDF.

MOORJANI P., SANKARARAMAN S., FU Q., PRZEWORSKI M., PATTERSON N. & REICH D. (2016): A genetic method for dating ancient genomes provides a direct estimate of human generation interval in the last 45,000 years. Proceedings of the National Academy of Sciences of the United States of America 113(20), 5652-5657. https://doi.org/10.1073/PNAS.1514696113/SUPPL_FILE/PNAS.1514696113.SAPP.PDF.

NIELSEN R., AKEY J.M., JAKOBSSON M., PRITCHARD J.K., TISHKOFF S. & WILLERSLEV E. (2017): Tracing the peopling of the world through genomics. Nature 541(7637), 302-310. https://doi.org/ 10.1038/nature21347.

ORLANDO L., ALLABY R., SKOGLUND P., DER SARKISSIAN C., STOCKHAMMER P.W., ÁVILA-ARCOS MC., FU Q., KRAUSE J., WILLERSLEV E., STONE A.C. & WARINNER C. (2021): Ancient DNA analysis. Nature Reviews Methods Primers 1(14), 1-26. https://doi.org/ 10.1038/s43586-020-00011-0.

ORLANDO L., GINOLHAC A., ZHANG G., FROESE D., ALBRECHTSEN A., STILLER M., SCHUBERT M., CAPPELLINI E., PETERSEN B., MOLTKE I., JOHNSON P.L.F., FUMAGALLI M., VIL-STRUP J.T., RAGHAVAN M., KORNELIUSSEN T., MALASPINAS AS., VOGT J., SZKLAR-CZYK D., KELSTRUP C.D., ... WILLERSLEV E. (2013): Recalibrating Equus evolution using the genome sequence of an early Middle Pleistocene horse. Nature 499(7456), 74-78. https://doi.org/10.1038/nature12323.

PAABO S. (1988): The Mummy of Ramses II reconsidered. Orientalistische Literaturzeitung 83(1-6), 195197. https://doi.org/10.1524/OLZG.1988.83.16.195.

PAABO S. (1989): Ancient DNA: extraction, characterization, molecular cloning, and enzymatic amplification. Proceedings of the National Academy of Sciences of the United States of America 86(6), 1939. https://doi.org/10.1073/PNAS.86.6.1939.

PÄÄBO S. (2015): The diverse origins of the human gene pool. Nature Reviews Genetics 16(6), 313-314. https://doi.org/ 10.1038/nrg3954.

PÄÄBO S., POINAR H., SERRE D., JAENICKE-DESPRÉS V., HEBLER J., ROHLAND N., KUCH M., KRAUSE J., VIGILANT L. & HOFREITER M. (2004): Genetic Analyses from Ancient DNA. Annual Review of Genetics 38, 645-679. https://doi.org/10.1146/ANNUREV.GENET.37.110801.143214.

PEARCE-DUVET J.M.C. (2006): The origin of human pathogens: evaluating the role of agriculture and domestic animals in the evolution of human disease. Biological Reviews of the Cambridge Philosophical Society 81(3), 369-382. https://doi.org/10.1017/S1464793106007020.

PEDERSEN J.S., VALEN E., VELAZQUEZ A.M.V., PARKER B.J., RASMUSSEN M., LINDGREEN S., LILJE B., TOBIN D.J., KELLY T.K., VANG S., ANDERSSON R., JONES P.A., HOOVER C.A., TIKHONOV A., PROKHORTCHOUK E., RUBIN EM., SANDELIN A., GILBERT M.T.P., KROGH A., ... ORLANDO L. (2014): Genome-wide nucleosome map and cytosine methylation levels of an ancient human genome. Genome Research 24(3), 454-466. https://doi.org/10.1101/GR.163592.113.

POINAR H., KUCH M., MCDONALD G., MARTIN P. & PÄÄBO S. (2003): Nuclear gene sequences from a late pleistocene sloth coprolite. Current Biology: CB 13(13), 1150-1152. https://doi.org/10.1016/S0960-9822(03)00450-0.

PUSCH C.M., BROGHAMMER M. & SCHOLZ M. (2000): Cremation practices and the survival of ancient DNA: burnt bone analyses via RAPD-mediated PCR, Anthropologischer Anzeiger; Bericht uber die biologisch-anthropologische Literatur 58(3), pp. 237-251.

RACIMO F., SANKARARAMAN S., NIELSEN R. & HUERTA-SÁNCHEZ E. (2015): Evidence for archaic adaptive introgression in humans. Nature Reviews. Genetics 16(6), 359. https://doi.org/10.1038/NRG3936.

RASMUSSEN M., LI Y., LINDGREEN S., PEDERSEN J.S., ALBRECHTSEN A., MOLTKE I., METSPALU M., METSPALU E., KIVISILD T., GUPTA R., BERTALAN M., NIELSEN K., GILBERT M.T.P., WANG Y., RAGHAVAN M., CAMPOS P.F., KAMP H.M., WILSON A.S., GLEDHILL A., ... WILLERSLEV E. (2010): Ancient human genome sequence of an extinct Palaeo-Eskimo. Nature 463(7282), 757. https://doi.org/10.1038/NATURE08835.

RASMUSSEN S., ALLENTOFT M.E., NIELSEN K., ORLANDO L., SIKORA M., SJÖGREN K.G., PEDERSEN A.G., SCHUBERT M., VAN DAM A., KAPEL C.M.O., NIELSEN H B., BRUNAK S., AVETISYAN P., EPIMAKHOV A., KHALYAPIN M.V., GNUNI A., KRIISKA A., LASAK I., METSPALU M., ... WILLERSLEV E. (2015): Early divergent strains of Yersinia pestis in Eurasia 5,000 years ago. Cell 163(3), 571-582. https://doi.org/10.1016J.CELL.2015.10.009.

RAWLENCE N.J., LOWE D.J., WOOD J.R., YOUNG J.M., CHURCHMAN G.J., HUANG Y.T. & COOPER A. (2014): Using palaeoenvironmental DNA to reconstruct past environments: progress and prospects. Journal of Quaternary Science 29(7), 610-626. https://doi.org/10.1002/JQS.2740.

REES J.S., CASTELLANO S. & ANDRÉS A.M. (2020): The Genomics of Human Local Adaptation. Trends in Genetics 36(6), 415-428. https://doi.org/10.1016ZJ.TIG.2020.03.006.

REICH D., GREEN R.E., KIRCHER M., KRAUSE J., PATTERSON N., DURAND E.Y., VIOLA B., BRIGGS A.W., STENZEL U., JOHNSON P.L.F., MARICIC T., GOOD J.M., MARQUES-BONET T., ALKAN C., FU Q., MALLICK S., LI H., MEYER M., EICHLER EE., ... PÄÄBO S. (2010): Genetic history of an archaic hominin group from Denisova Cave in Siberia. Nature 468(7327), 1053-1060. https://doi.org/ 10.1038/nature09710.

REIMER P.J., AUSTIN W.E.N., BARD E., BAYLISS A., BLACKWELL P.G., BRONK RAMSEY C., BUTZIN M., CHENG H., EDWARDS R.L., FRIEDRICH M., GROOTES P.M., GUILDERSON TP., HAJDAS I., HEATON T.J., HOGG AG., HUGHEN K.A., KROMER B., MANNING S.W., MUSCHELER R., ... TALAMO S. (2020): The IntCal20 Northern Hemisphere radiocarbon calibration curve (0-55 kcal BP). Radiocarbon 62(4), 725-757. https://doi.org/10.1017/rdc.2020.41.

RENAUD G., SCHUBERT M., SAWYER S. & ORLANDO L. (2019): Authentication and Assessment of Contamination in Ancient DNA (pp. 163-194). https://doi.org/10.1007/978-1-4939-9176-1_17.

RIVERA-PEREZ J.I., SANTIAGO-RODRIGUEZ T.M. & TORANZOS G.A. (2016): Paleomicrobiology: a Snapshot of Ancient Microbes and Approaches to Forensic Microbiology. Microbiology Spectrum 4(4). https://doi.org/10.1128/MICROBIOLSPEC.EMF-0006-2015.

ROBLING A G., CASTILLO A.B. & TURNER C.H. (2006): Biomechanical and Molecular Regulation of Bone Remodeling. Annu. Rev. Biomed. Eng 8, 455-498. https://doi.org/10.1146/ANNUREV.BIO-ENG.8.061505.095721.

ROHDE D.L.T., OLSON S. & CHANG J.T. (2004): Modelling the recent common ancestry of all living humans. Nature 431(7008), 562-566. https://doi.org/10.1038/nature02842.

ROHLAND N., HARNEY E., MALLICK S., NORDENFELT S. & REICH D. (2015): Partial uracil-DNA-glycosylase treatment for screening of ancient DNA. Philosophical Transactions of the Royal Society B: Biological Sciences 370(1660). https://doi.org/10.1098/RSTB.2013.0624.

SALAMON M., TUROSS N., ARENSBURG B. & WEINER S. (2005): Relatively well preserved DNA is present in the crystal aggregates of fossil bones. Proceedings of the National Academy of Sciences of the United States of America 102(39), 13783-13788. https://doi.org/10.1073/PNAS.0503718102/SUPPL_FILE/03718TA-BLE4.PDF.

SANTIAGO-RODRIGUEZ T.M., NARGANES-STORDE Y.M., CHANLATTE L., CRESPO-TORRES E., TORANZOS G.A., JIMENEZ-FLORES R., HAMRICK A. & CANO R.J. (2013): Microbial communities in pre-columbian coprolites. PloS One 8(6). https://doi.org/10.1371/JOURNAL.PONE.0065191.

SAWYER S., KRAUSE J., GUSCHANSKI K., SAVOLAINEN V. & PÄÄBO S. (2012): Temporal Patterns of Nucleotide Misincorporations and DNA Fragmentation in Ancient DNA. PLoS ONE 7(3), 34131. https://doi.org/10.1371/JOURNAL.PONE.0034131.

SCHOUTEN S., MIDDELBURG J.J., HOPMANS EC. & SINNINGHE DAMSTÉ J.S. (2010): Fossilization and degradation of intact polar lipids in deep subsurface sediments: A theoretical approach. Geochimica et Cosmochimica Acta 74(13), 3806-3814. https://doi.org/10.1016J.GCA.2010.03.029.

SCHUBERT M., LINDGREEN S. & ORLANDO L. (2016): AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Research Notes 9(1), 88. https://doi.org/10.1186/s13104-016-1900-2.

SEGUIN-ORLANDO A., HOOVER C A., VASILIEV S.K., OVODOV N.D., SHAPIRO B., COOPER A., RUBIN E.M., WILLERSLEV E. & ORLANDO L. (2015): Amplification of TruSeq ancient DNA libraries with AccuPrime Pfx: consequences on nucleotide misincorpora-tion and methylation patterns. Science and Technology of Archaeological Research 1(1), 1-9. https://doi.org/10.1179/2054892315Y.0000000005/SUPPL_FILE/YSTA_A_11738261_SM000 2.EPS.

SHAPIRO B., DRUMMOND A.J., RAMBAUT A., WILSON MC., MATHEUS P.E., SHER AV., PY-BUS O.G., GILBERT M.T.P., BARNES I., BINLADEN J., WILLERSLEV E., HANSEN A.J., BA-RYSHNIKOV G.F., BURNS J.A., DAVYDOV S., DRIVER J.C., FROESE D.G., HARINGTON C.R., KEDDIE G., ... COOPER A. (2004): Rise and fall of the Beringian steppe bison. Science 306(5701), 1561-1565. https://doi.org/10.1126/SCIENCE.1101074/SUPPL_FILE/SHAPIRO.SOM.PDF.

SMITH C.I., CHAMBERLAIN A T., RILEY M.S., COOPER A., STRINGER C.B. & COLLINS M.J. (2001): Neanderthal DNA. Not just old but old and cold? Nature 410(6830), 771-772. https://doi.org/10.1038/35071177.

SOMMER M.O.A., DANTAS G. & CHURCH G.M. (2009): Functional characterization of the antibiotic resistance reservoir in the human microflora. Science 325(5944), 1128-1131. https://doi.org/10.1126/SCIENCE. 1176950/SUPPL_FILE/SOMMER_SOM.PDF.

S0RENSEN M. S. & BRETLAU P. (1997): Spatial organization of bone modelling and remodelling in the otic capsule. In: Progress in human auditory and vestibular histopathology (Eds Iurato S. & Veldman J.E.), pp. 23-29. Amsterdam, the Netherlands: Kugler Publications.

SPYROU M.A., BOS K.I., HERBIG A. & KRAUSE J. (2019): Ancient pathogen genomics as an emerging tool for infectious disease research. Nature Reviews Genetics 20(6), 323-340. https://doi.org/10.1038/s41576-019-0119-1.

SUCHAN T., PITTELOUD C., GERASIMOVA N.S., KOSTIKOVA A., SCHMID S., ARRIGO N., PAJKOVIC M., RONIKIER M. & ALVAREZ N. (2016): Hybridization Capture Using RAD Probes (hyRAD), a New Tool for Performing Genomic Analyses on Collection Specimens. PLOS ONE 11(3), e0151651. https://doi.org/10.1371/JOURNAL.PONE.0151651.

TITO R.Y., KNIGHTS D., METCALF J., OBREGON-TITO A.J., CLEELAND L., NAJAR F., ROE B., REINHARD K., SOBOLIK K., BELKNAP S., FOSTER M., SPICER P., KNIGHT R. & LEWIS CM. (2012): Insights from Characterizing Extinct Human Gut Microbiomes. PLoS ONE 7(12), 51146. https://doi.org/10.1371/JOURNAL.PONE.0051146.

VAN DER PLICHT J., BRONK RAMSEY C., HEATON T.J., SCOTT EM. & TALAMO S. (2020): Recent Developments in Calibration for Archaeological and Environmental Samples. Radiocarbon 62(4), 1095-1117. https://doi.org/10.1017/RDC.2020.22.

VAN DER VALK T., PECNEROVÄ P., DiEZ-DEL-MOLINO D., BERGSTRÖM A., OPPENHEIMER J., HARTMANN S., XENIKOUDAKIS G., THOMAS JA., DEHASQUE M., SAGLICAN E., FIDAN F.R., BARNES I., LIU S., SOMEL M., HEINTZMAN P.D., NIKOLSKIY P., SHAPIRO B., SKOGLUND P., HOFREITER M., ... DALEN L. (2021): Million-year-old DNA sheds light on the genomic history of mammoths. Nature 591(7849), 265-269. https://doi.org/10.1038/s41586-021-03224-9.

WAGNER D.M., KLUNK J., HARBECK M., DEVAULT A., WAGLECHNER N., SAHL J. W., ENK J., BIRDSELL D.N., KUCH M., LUMIBAO C., POINAR D., PEARSON T., FOURMENT M., GOLDING B., RIEHM J.M., EARN D.J.D., DEWITTE S., ROUILLARD J.M., GRUPE G., ... POINAR H. (2014): Yersinia pestis and the plague of Justinian 541-543 AD: a genomic analysis. The Lancet. Infectious Diseases 14(4), 319-326. https://doi.org/10.1016/S1473-3099(13)70323-2.

WARINNER C., HERBIG A., MANN A., FELLOWS YATES J.A., WEISS C.L., BURBANO H.A., ORLANDO L. & KRAUSE J. (2017): A Robust Framework for Microbial Archaeology. Annual Review of Genomics and Human Genetics 18, 321. https://doi.org/10.1146/ANNUREV-GENOM-091416-035526.

WARINNER C., RODRIGUES J.F.M., VYAS R., TRACHSEL C., SHVED N., GROSSMANN J., RADINI A., HANCOCK Y., TITO R.Y., FIDDYMENT S., SPELLER C., HENDY J., CHARLTON S., LUDER H.U., SALAZAR-GARCiA DC., EPPLER E., SEILER R., HANSEN L.H., CASTRUITA J.A.S., ... CAPPELLINI E. (2014): Pathogens and host immunity in the ancient human oral cavity. Nature Genetics 46(4), 336-344. https://doi.org/10.1038/ng.2906.

WARINNER C., SPELLER C. & COLLINS M.J. (2015): A new era in palaeomicrobiology: prospects for ancient dental calculus as a long-term record of the human oral microbiome. Philosophical Transactions of the Royal Society B: Biological Sciences 370(1660). https://doi.org/10.1098/RSTB.2013.0376.

WEIß C.L., GANSAUGE M.T., AXIMU-PETRI A., MEYER M. & BURBANO H.A. (2020): Mining ancient microbiomes using selective enrichment of damaged DNA molecules. BMC Genomics 21(1), 432. https://doi.org/10.1186/S12864-020-06820-7.

WILLERSLEV E. & COOPER A. (2005): Ancient DNA. In: Proceedings of the Royal Society B: Biological Sciences (Vol. 272, Issue 1558, pp. 3-16). Royal Society. https://doi.org/10.1098/rspb.2004.2813.

WILLERSLEV E., DAVISON J., MOORA M., ZOBEL M., COISSAC E., EDWARDS M.E., LORENZEN E D., VESTERGÄRD M., GUSSAROVA G., HAILE J., CRAINE J., GIELLY L., BOES-SENKOOL S., EPP L S., PEARMAN P.B., CHEDDADI R., MURRAY D., BRÄTHEN K.A., YOC-COZ N., ... TABERLET P. (2014): Fifty thousand years of Arctic vegetation and megafaunal diet. Nature 506(7486), 47-51. https://doi.org/10.1038/nature12921.

XIE G., CHAIN P.S.G., LO C.C., LIU K.L., GANS J., MERRITT J. & QI F. (2010): Community and gene composition of a human dental plaque microbiota obtained by metagenomic sequencing. Molecular Oral Microbiology 25(6), 391-405. https://doi.org/10.1111/J.2041-1014.2010.00587.X.

YATSUNENKO T., REY F.E., MANARY M.J., TREHAN I., DOMINGUEZ-BELLO M.G., CONTRERAS M., MAGRIS M., HIDALGO G., BALDASSANO R.N., ANOKHIN A.P., HEATH A C., WARNER B., REEDER J., KUCZYNSKI J., CAPORASO J.G., LOZUPONE C.A., LAUBER C., CLEMENTE J.C., KNIGHTS D., ... GORDON J.I. (2012): Human gut microbiome viewed across age and geography. Nature 486(7402), 222. https://doi.org/10.1038/NATURE11053.