Assessing the Extent of Bone Degradation Using Glutamine

Article pubs.acs.org/ac

Assessing the Extent of Bone Degradation Using Glutamine Deamidation in Collagen Julie Wilson,*,†,‡ Nienke L. van Doorn,§ and Matthew J. Collins§ †

Department of Mathematics, University of York, York YO10 5YW, U.K. Department of Chemistry, University of York, York YO10 5YW, U.K. § BioArCh, Biology (S-Block), University of York, York YO10 5YW, U.K. ‡

S Supporting Information *

ABSTRACT: Collagen peptides are analyzed using a low-cost, highthroughput method for assessing deamidation using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). For each chosen peptide, the theoretical distribution is calculated and the measured distribution for each sample compared with this to determine the extent of glutamine deamidation. The deamidation of glutamine (Q) to glutamic acid (E) results in a mass shift of +0.984 Da. Thus, from the resolution of our data, the second peak in the isotope distribution for a peptide containing one glutamine residue coincides with the first peak of the isotope distribution for the peptide in which the residue is deamidated. A genetic algorithm is used to determine the extent of deamidation that gives the best fit to the measured distribution. The method can be extended to peptides containing more than one glutamine residue. The extent of protein degradation assessed in this way could be used, for example, to assess the damage of collagen, and screen samples for radiocarbon dating and DNA analysis.

T

to have the highest rates. By modeling peptide deamidation and interpolating between experimental values, the list of sequencedependent rates was extended to include half-lives of up to 50 years.10 Although the three-dimensional structure makes the influence of neighboring groups more complex in proteins than in short peptides, they found that the effects could be estimated where structures were available. Although few protein studies involving glutamine deamidation have been reported due to the slow rates, significant levels were detected in a recombinant monoclonal antibody, suggesting a possible role in the binding affinity of the protein.11 As expected, glutamine residues at different locations in the three-dimensional structure showed different susceptibilities to deamidation. Type I collagen is the dominant protein in bone and widely used in archeology for radiocarbon (14C) and stable isotope analyses.12 The deamidation of collagen for dating materials has been discussed by Hurtado and O’Connor.14 The upper-limit of radiocarbon dating even in the best-preserved bones is 60 000 years; however, collagen can persist for more than 1 order of magnitude longer in temperate environments.13 Bone persists over the periods in a ‘recrystallization window’ of between pH 7.6 and 8.1,15 which will buffer the within-bone pH while at the same time destroying the original bone bioapatite. Below pH 7.6, bone will undergo sacrificial dissolution,11,16 and above pH 8.2 the mineral will persist. Observed rates of glutamine deamidation in the range typical

he nonenzymatic deamidation of asparagine (Asn) and glutamine (Gln) residues represents one of the most widely studied post-translational modifications.1 Asparaginyl deamidation may play a role in timing biological processes and has been implicated in neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases.2,3 Glutamine is generally much more stable than asparagine, with the much slower deamidation rates being attributed to less stable reaction intermediates. 4 As a result, studies relating glutamine deamidation with the aging process involve low-turnover proteins, such as eye lens crystallin, associated with cataract formation.5 The slower rates of glutaminyl deamidation make it suitable for the dating of historical artifacts. Mass spectrometry has been used to study the deterioration and aging of proteinaceous paint media used in works-of-art to allow suitable restoration processes to be designed.6 The deamidation of Gln was found to be a major factor in the degradation process, with asparagine deamidation occurring too fast in their samples to be useful. The rate of Asn deamidation is strongly affected by both primary and higher order structure,7 but it has been shown that the level of asparaginyl deamidation could be useful for dating (structurally coherent) wool textiles from museum collections.8 Rates of asparagine and glutamine deamidation are heavily influenced by neighboring residues. The analysis of pentapeptides with different permutations of adjacent amino acids showed that, under physiological conditions (pH 7.4), half-lives for asparagine ranged from 6 to 507 days in comparison to 96 to 3409 days for glutamine in similar positions.9 Peptides with small charged residues on either side of the amide were found © 2012 American Chemical Society

Received: May 21, 2012 Accepted: October 2, 2012 Published: October 2, 2012 9041

dx.doi.org/10.1021/ac301333t | Anal. Chem. 2012, 84, 9041−9048

Analytical Chemistry

Article

Isotope Calculation. Each peptide gives rise to a sequence of peaks reflecting its isotopic distribution, and the expected distribution, based on the average terrestrial abundances of the stable isotopes, can be calculated from the peptide’s elemental composition. Precise calculations involve the product of polynomial expansions, one for each element in the peptide, representing the naturally occurring isotopes.23 These methods become progressively more impractical with increased molecular weight and produce more information than is necessary for most applications. Methods have been developed to reduce the computational effort required, including the introduction of a strategy for pruning24 and the use of Fourier transforms.25 To assess the extent of deamidation, we only consider the terms related to the first few peaks of the distribution that have intensities reliably above the noise level. Furthermore, although we are concerned with molecular weights of up to 4000 Da, each peptide consists of just five elements so that the possible isotopic compositions are given by expansion of the following product of polynomials:

for most buried bone (pH 5 to 9) are highly dependent on pH, with the lowest at pH 6 and the highest level detected in the most alkaline sample (pH 9).17 At low pH, bone will be rare in the archeological record, but at both neutral to alkaline pH, bone will be present but the extent of glutamine deamidation will be higher from an alkaline environment (e.g., wood ash). We describe a low-cost high-throughput method for assessing deamidation using matrix-assisted laser desorption/ ionization mass spectrometry (MALDI-MS) of archeological bone extracts. The method has previously been used to identify species from peptide mass fingerprints.18,19 A nondestructive variant of this method20 extracts soluble peptides in warm (65 °C) ammonium bicarbonate buffer (pH 7.5), close to the equilibrium pH of bone apatite (pH 7.8).15 By identifying peptides containing glutamine residues, the same highthroughput method can be used to assess the level of deamidation by deconvolution of the overlapping isotope distributions. It has been argued that asparagine deamidation was arrested in an intact triple helix due to the difficulty of forming the cyclic succinimidyl intermediate.21 However deamidation of asparagine residues is very rapid in unconstrained structures like that of denatured gelatin. In our method20 we extract a soluble fraction released following room temperature soaking and mild heating (65 °C). It is highly likely that during these processes, asparagine will deamidate. We therefore focus on glutamine deamidation, which is less prone to cyclization-mediated deamidation and more likely to occur by direct hydrolysis. To assess any deamidation during the digestion process, Li and colleagues22 used H218O labeling and found no detectable Asn deamidation in 4 h trypsin digestion of three rapidly deamidating peptides. Furthermore, Araki and Moini8 found no significant artificial deamidation (of Asn) during overnight digestion at pH 8.3. Given that Gln deamidation is much slower than Asn, it is very unlikely that our sample preparation method will lead to artificial deamidation. The very low levels of glutamine deamidation we have found in medieval cattle bone from York and also in human bone from Greenland both attest to this. As well as a method for screening samples, for example, for expensive DNA analysis, the extent of glutamine deamidation could potentially be used to date fossil bone. The technique is experimentally simple, requires a much smaller sample than radiocarbon dating, and has a longer range, extending up to more than 100 000 years.

(12C + 13C)n1 + (1H + 2 H)n2 (14 N + 15 N)n3 (16O + 17O + 18O)n4 + (32S + 33S +

34

S + 35S)n5

(1)

where n1 is the number of carbon atoms in the peptide, n2 the number of hydrogen atoms, n3 is the number of nitrogen atoms, n4 is the number of oxygen, and n5 is the number of sulfur atoms. The expected relative intensities are obtained by substitution of the relevant natural abundances and collecting together the appropriate terms. Although a number of web applications, such as iMass,26 Isotopica,27 and Isotopident,28 are available to calculate individual isotope distributions, we required the calculation to be integrated within the software to estimate deamidation levels, and further details on the equations used are given in the Supporting Information (SI.2). Estimation of Deamidation Level. The deamidation of glutamine (Q) to glutamic acid (E) results in a mass shift of +0.984 Da. Thus, glutamine deamidation results in a shift of the isotope distribution and, from the resolution of our data, the second peak in the isotope distribution for a peptide containing one glutamine residue coincides with the first peak of the isotope distribution for the peptide in which the residue is deamidated. An example is shown in the Supporting Information (SI.3). The extent of Q to E deamidation can be found by deconvolution of the two overlapping distributions. If we let α denote the proportion that is not deamidated, then the ith peak in the combined distribution is proportional to

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Pi = αIi + (1 − α)Ii − 1

MATERIALS AND METHODS Samples. A total of 87 ungulate bone samples were obtained from archeological sites of differing ages and environmental conditions. Of these, 36 samples were obtained from the “Evolved Chatelperronian” site at Les Cottés, (Vienne, France), 12 samples from the Roman period burial and settlement site of Castricum (Noord-Holland, The Netherlands), 3 samples from a Mesolithic−Neolithic site in Rosenhof (Germany), 16 samples from the middle Neolithic site at Wangels (Ostholstein, Germany), 11 samples from a Neolithic site at Kerma (Sudan) dated third and second millennia BC, and 9 samples of well preserved whole bovid bone fragments from midninth to tenth century Coppergate, York (UK). Details on sample preparation and mass spectrometric analysis are provided in the Supporting Information (SI.1).

(2)

where Ii represents the ith peak in the theoretical isotope distribution calculated for the nondeamidated peptide for the peptide, with Ii = 0 if i < 1. Thus, for a peptide containing a single glutamine residue, α can be calculated by simultaneously solving the set of equations given by eq 2 for i = 1, ..., n, where n is the number of peaks with significant intensities. As α is a number between 0 and 1, with α = 1 denoting no deamidation and α = 0 denoting total deamidation, we can consider α as the probability that the glutamine residue is not deamidated. The method can be extended to peptides with multiple glutamine residues. For example, for a peptide containing two glutamine residues, the ith peak of the combined distribution, Pi, has contributions from Ii, Ii−1, and Ii−2 of the theoretical distribution of the nondeamidated peptide: 9042



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Figure 1. The full mass spectrum obtained from a modern cow collagen sample is shown in spectrum a with the sections of the spectrum labeled A and B shown in spectra b and c, respectively. The ends of the bins over which the peaks are integrated are indicated by the dotted lines.

Pi = β1Ii + β2Ii − 1 + β3Ii − 2

expected. When data were synthesized so that the deamidation rate of the second residue was dependent on the first residue, fitting eq 4 still gave α values that satisfied the fitness criterion set in the genetic algorithm, although the fitness was worse (by a factor of 10) than that for the β values fitted with eq 3. The use of MS/MS would allow the deamidation states of individual residues to be determined. Data Processing. Figure 1 shows the mass spectrum obtained from a modern cow sample with close-ups showing isotope distributions from different parts of the spectrum. It can be seen that the noise level is not constant, decreases with increasing m/z value. We therefore estimated local noise levels by considering the spectrum in the region of the peptide being analyzed. Figure 1b shows that background ions in the early part of the spectrum give rise to noise peaks but that these peaks are of a uniform size, allowing them to be distinguished from the peaks in the isotope distribution. In the later part of the spectrum the noise looks less like peaks but can still be expected to have a roughly uniform level in the neighborhood. For each isotope distribution under consideration, a total of seven bins are defined. Five are related to peaks in the distribution, the first of these being located 1 Da beyond the

(3)

where β1, β2, β3 ∈ [0,1], β3 = (1 − β1 − β2) and we consider Ii = 0 if i < 2. If the deamidation rates for the two glutamine residues are independent, then the deamidation rates could be multiplied and we would have Pi = α1(α2Ii + (1 − α2)Ii − 1) + (1 − α1) (α2Ii − 1 + (1 − α2)Ii − 2) = α1α2Ii + (α1(1 − α2) + (1 − α1)α2)Ii − 1 + (1 − α1)(1 − α2)Ii − 2

(4)

where α1 and α2 denote the nondeamidated proportion of the respective glutamine residues. It may be that the deamidation of one glutamine affects the deamidation of other glutamine residues nearby so that the deamidation rates for multiple glutamines within the same peptide are not independent. To compare the use of eqs 3 and 4, peaks in experimental spectra were rescaled and combined to give isotope distributions for two residues with various deamidation levels. With two independent rates, both equations led to the same results, where β1 = α1α2 and β2 = α1(1 − α2) + α2(1 − α1), as 9043



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Table 1. First and Third Quartiles of the β1 Values Obtained for Various Peptides, Calculated over All Observations in Which the Peptide Could Be Modeled Reliably β1 values calculated mass

peptide

no. of reliable observations

1st quartile

3rd quartile

835.4 897.5 1104.6 1191.7 1207.6 1689.8 1705.8 1830.9 1846.9 1862.9 2055.9 2071.9 2087.9 2104.0 2688.2 2704.2

GPAGPQGPR GVVGLPGQR (Pro → Hyp) GVQGPPGPAGPR (Pro → Hyp) IGQPGAVGPAGIR IGQPGAVGPAGIR (Pro → Hyp) DGEAGAQGPPGPAGPAGER DGEAGAQGPPGPAGPAGER (Pro → Hyp) GEPGPTGIQGPPGPAGEEGK GEPGPTGIQGPPGPAGEEGK (Pro → Hyp) GEPGPTGIQGPPGPAGEEGK (2 Pro → Hyp) TGPPGPAGQDGRPGPPGPPGAR (4 Pro → Hyp) TGPPGPAGQDGRPGPPGPPGAR (5 Pro → Hyp) TGPPGPAGQDGRPGPPGPPGAR (6 Pro → Hyp) TGPPGPAGQDGRPGPPGPPGAR (7 Pro → Hyp) GFSGLQGPPGPPGSPGEQGPSGASGPAGPR (2 Pro → Hyp) GFSGLQGPPGPPGSPGEQGPSGASGPAGPR (3 Pro → Hyp)

29 38 47 10 17 31 62 34 40 9 31 23 26 18 27 53

0.74 0.96 0.55 0.73 0.74 0.62 0.47 0.00 0.00 0.07 0.00 0.44 0.53 0.76 0.26a 0.21a

0.98 1.00 0.92 1.00 0.98 0.92 0.92 0.02 0.01 0.33 0.40 0.80 0.79 0.93 0.73a 0.79a

a For the peptide GFSGLQGPPGPPGSPGEQGPSGASGPAGPR, with two glutamine residues, the values shown are for β1, as β2 ∼ 1 − β1 for all observations so that β3 = 0 and only one glutamine is deamidated.

monoisotopic mass of the nondeamidated peptide where the first peak of the distribution is observed. The mass spectrum for each sample is integrated between ±0.3 m/z units of this point, with bins of the same width defined at points incremented by 1 Da for the following four peaks of the distribution. A further two bins, with the same spacing, are defined immediately before the distribution. The bin ends are indicated by dotted lines in Figure 1. The two bins immediately before the distribution are used to determine the noise level. If no other distribution is overlapping, the two integrated values should be very similar. If this is not the case, the spectrum is rejected (for this particular peptide) as unreliable. Otherwise, the average of the two values is taken as local noise level, which is compared with the greatest of the integrated intensities for the five peaks of the distribution. If this signal-to-noise ratio is at least 3.0, the value obtained for the noise is subtracted from each of the five bins and the resulting values are used to represent the experimental distribution for that spectrum. The three analytical replicates from each biological sample are combined in proportion to the signal-to-noise ratio. Peptide sequences containing precisely one glutamine residue were initially selected from the theoretical peptides produced by trypsin digestion of bovine collagen. As the available data sets include other species, only those sequences common to all ungulates were considered. Furthermore, any sequences containing asparagines were discarded to avoid confusion with the deamidation of this residue. For each peptide, the expected isotope distribution was calculated from the input sequence as described above (adding an H for the Nterminus and an OH for the C-terminus). Where appropriate, hydroxylation was allowed for by also considering the masses obtained on incrementing by 16 Da for each hydroxylation site. To determine the extent of deamidation, a genetic algorithm is used to find the value of α that gives the best solution to the set of simultaneous equations given in eq 2 above. A random starting population of possible α values between 0 and 1, each represented by a string of integers corresponding to digits after the decimal point, is generated. These representations are manipulated using computational operations that mimic

biological evolution until convergence is reached. After each generation, the fitness of each individual (representation of α) is evaluated according to the sum of squares error between the observed peaks, Oi, and the peaks, Pi, calculated using eqs 2: 4

fitness =

∑ (Oi − P)i 2 i=1

The best individuals become the “parents” for the next generation, obtained using operations for crossover and mutation. In crossover, which mimics sexual reproduction, the sections before and after a randomly chosen point are interchanged between two of the chosen parent strings. Mutation is achieved by randomly selecting a position in the string and replacing that digit. Examples of both operators are shown in the Supporting Information (Figure S3). The algorithm converges quickly, and the maximum number of generations, defined as an alternative stopping criterion, was never reached. The fitness of the chosen solution, i.e., the sum of squares error, can be used to identify those distributions with a poor fit, in some cases due to overlap (on the right) with a different peptide, as seen by the naked eye. Multiple glutamine residues can be considered simultaneously by allowing each string to represent more than one value (so that the first n1 digits represent β1 and so on). The fitness of each individual is then calculated from the sum of squares error obtained between the observed peaks and, for example in the case of two glutamine residues, the peaks calculated using eqs 3 or 4 (for i = 1,...,4).

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RESULTS AND DISCUSSION For all potential peptides containing a single glutamine residue and no asparagines, α values were calculated where possible for each individual spectrum. Any spectra with low signal-to-noise ratio or poor fit in the genetic algorithm were rejected and, for those remaining, α values for analytical replicates were combined in proportion to the signal-to-noise ratio. Analysis of the 87 samples here showed distinct patterns in the α values obtained for different peptides. Some peptides 9044



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have very high α values, showing little or no deamidation, regardless of the age or preservation state of the sample. Conversely, some peptides always have very low values of α, indicating a high degree of degradation whatever the age of the sample. Table 1 shows the first and third quartiles of the α values calculated for peptides in this study, giving an indication of the pattern in deamidation levels. It can be seen that the peptide, GVVGLPGQR with hydroxylated proline giving a calculated m/z value of 897.5, shows very little deamidation whereas the peptide GEPGPTGIQGPPGPAGEEGK with calculated m/z value 1830.9 is completely deamidated in almost all samples. The overlay of all spectra in Figure 2 shows

Figure 2. Section of the mass spectra showing the isotope distribution for the peptide GEPGPTGIQGPPGPAGEEGK with calculated m/z value 1830.9. The first peak of the distribution appears at 1832.9 rather than 1831.9, as would be expected for the nondeamidated peptide. Figure 3. The α values obtained for the peptide GPAGPQGPR (calculated m/z value 835.4) plotted against the estimated chronological age of the samples in panel a and the thermally adjusted age calculated for the samples in panel b.

that the first peak of the distribution appears at m/z 1832.9 rather than 1831.9, as would be expected for the nondeamidated peptide. The equivalent peptides with one and two hydroxylated prolines, at m/z values 1846.9 and 1862.9, respectively, show the same very high deamidation levels. One of the peptides, GFSGLQGPPGPPGSPGEQGPSGASGPAGPR with calculated m/z value 2688.2, and its equivalent with a hydroxylated proline at m/z 2704.2, has two glutamine residues. For both hydroxylation states, two separate α values were fitted using eq 4, but one of these values, say α2, was very close to 1.0 for all samples, suggesting that one of the two glutamine residues shows no deamidation. Equation 3 was also used to fit β values for each sample, which gave β1 = α1 and β2 = 1 − α1, also showing that just one glutamine is deamidated in any particular sample. MS/MS analysis would be required to determine whether this is always the same residue or alternates between the two glutamines in different samples. Figure 3a shows the α values calculated for the peptide, GPPGPQGAR, with a calculated m/z value of 835.4 plotted against the age of the sample, estimated by archeologists based on associated objects on site. Apart from a group of observations, dated as around 5500 years old, a trend can be seen with α values decreasing with age. These samples were obtained from an archeological site at Kerma, Sudan, where the environmental conditions are likely to significantly increase degradation. Environment is of vital importance in the degradation process with cold climates resulting in better preservation than hot climates. The thermal history of fossils is seen as a key parameter in the survival of biomolecules with the thermal age

of a fossil defined as “the time taken to produce a given degree of DNA degradation when temperature is held at a constant 10 °C”.16 Collagen content is one of the diagenetic parameters used to describe bone degradation,29 and we have calculated an adjusted biological age, analogous to the thermal age for DNA degradation, to reflect the breakdown and leaching of collagen. We use the Arrhenius equation: k = Ae−Ea/RT

with pre-exponential constant A = 6.3115 × 1026 s−1 and activation energy Ea/R = 173 kJ mol−1 to reflect the speed at which collagen solubilizes (estimated from experimental data),30 where R is the universal gas constant, 8.314 J mol−1K−1, and T is the temperature (in Kelvin). Thermal ages were calculated using the beta.thermal-age.eu web site (accessed April 2012). The calculation contrasts the estimated rate from a site (from its estimated temperature and thermal history) with the rate, assuming a constant 10 °C. The true age is then multiplied by the rate difference to get the thermal age. Thermal age estimates therefore require information on the rate differences of observed reactions at different temperatures but do not require an explicit kinetic model. Although significantly higher than values reported for glutamine deamidation in solution, the higher activation energy assumes (as a working hypothesis) that deamidation is accelerated in denatured sequences and that the latter may be the rate9045



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hydroxylation of a nearby proline seems to have little influence on the deamidation of the glutamine residue. We have noted a similar strong correlation between α values obtained for the different hydroxylation states of other peptides, which is also evident in the quartiles given in Table 1. For most peptides, observed in more than one hydroxylation state, the quartiles calculated are very similar for the different forms. This is not the case for the peptide, TGPPGPAGQDGRPGPPGPPGAR, observed with between four and seven hydroxylated prolines, which shows different patterns in the α values obtained for the different hydroxylation states. Figure 4b shows the β1 values obtained on fitting eq 3 for the nonhydroxylated (m/z 2688.2) and the hydroxylated (m/z 2704.2) forms of the peptide, GFSGLQGPPGPPGSPGEQGPSGASGPAGPR. For this peptide, we found that β2 ≈ 1 − β1 for all samples in both hydroxylation states, showing that only one glutamine residue is deamidated in any sample. Although we cannot say whether one of the two glutamines is preferentially deamidated, it is interesting that the two hydroxylation states show a correlation of 0.911 in the β1 values obtained. Figure 4 also shows the line corresponding to equal values for the two hydroxylation states. For both peptides shown, more points lie above this line, a pattern that is repeated for other peptides, showing that there may be a tendency for the hydroxlated peptide to be less deamidated. To demonstrate the potential utility of the method, we have used the α values obtained to predict the thermally adjusted age (representing the extent of degradation of the sample). The samples were divided into training and test sets by randomly assigning 2/3 of the samples from each site to the training set. The remaining samples formed an independent test set and were not used to build the regression model. Individual regression models were fitted for each peptide with α values related to age, using only the values from the training set. It was found empirically that the best models were achieved by fitting simple linear regression models relating α values to the square root of the thermally adjusted age with an additional α value of 1.0 at time zero. Individual models were fitted for each peptide in preference to a single multiple regression model to prevent problems with missing values. Each equation provides some information about the degradation of a sample, although the significance of the regression varies between peptides. We therefore weight the results to give a combined estimate for the thermal age, t, as

controlling factor in collagen; however, if this were the case, the observed disparity in the rates of deamidation would not be expected. The calculated rates are used to adjust the chronological age of different sites according to their individual thermal histories, resulting in an adjusted age of 360 020 years for the Kerma samples. Figure 3b shows the relationship between α values and the adjusted age for each sample. This pattern of decreasing α value with increased biological age is repeated for a number of different peptides, as shown in the Supporting Information (Figure S4). The percentage of deamidation varies significantly between peptides for the same sample. We have found that certain peptides show very little or no deamidation regardless of the age of the sample, whereas some appear to be completely deamidated for all samples. Although the relative rates of glutamine deamidation for those peptides with a percentage of deamidation between these two extremes have been considered,31 neither primary structure nor position within the collagen triplet repeat seem to wholly explain the difference in rates. Figure 4a shows the α values obtained for the nonhydroxylated (m/z 1689.8) and hydroxylated (m/z 1705.8) forms of the peptide, DGEAGAQGPPGPAGPAGER. The correlation between the two states is 0.902, showing that the

t = (∑ wk(ak + bk αk))2 k

(5)

where ak and bk are the regression parameters for the kth peptide. The weights, wk are given by wk =

Fk /df k ∑i Fi /dfi

(6)

where Fk and dfk denote the F-statistic and degrees of freedom for the regression equation of the kth peptide. Equation 5 was then used to predict the thermally adjusted age of the samples in the independent test set, and Figure 5 shows the results.

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Figure 4. (a) The α values obtained for the peptide, the nonhydroxylated (m/z 1689.8) and hydroxylated (m/z 1705.8) forms of the peptide, DGEAGAQGPPGPAGPAGER. (b) The β1 values obtained for the nonhydroxylated (m/z 2688.2) and the hydroxylated (m/z 2704.2) forms of the peptide, GFSGLQGPPGPPGSPGEQGPSGASGPAGPR.

CONCLUSIONS While some peptides show either no glutamine deamidation or total glutamine deamidation, regardless of the age of the sample, others show a relationship with the age of the sample adjusted to reflect the degradation associated with its thermal 9046



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racemization is not a useful screening technique for ancient DNA from bone.38 The relationship between glutamine degradation and sample age when the thermal history of the site is taken into account could potentially provide a low-cost estimate of age as well as a method to identify suitable samples for more expensive DNAbased analysis. The method requires a much smaller sample than for radiocarbon dating and has a longer range, extending to beyond one million years.

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ASSOCIATED CONTENT

S Supporting Information *

The project is a part of LeCHE and funded by Marie Curie FP7 Framework (MC-ITN 215362 LeCHE). The Ultraflex III was used courtesy of the York Centre of Excellence in Mass Spectrometry. The York Centre of Excellence in Mass Spectrometry was created from a major capital investment through Science City York, supported by Yorkshire Forward with funds from the Northern Way Initiative. Matthew Collins was supported by the SYNTHESYS Project (http://www. synthesys.info/), which is financed by European Community Research Infrastructure Action under the FP7 “Capacities” Programme. This material is available free of charge via the Internet at http://pubs.acs.org.

Figure 5. The thermally adjusted age predicted from α values plotted against that calculated from the estimated chronological age for samples assigned as an independent test set.

history. Most peptides observed in more than one hydroxylation state have highly correlated deamidation rates between the states, suggesting that the addition of a hydroxyl group to a nearby proline residue does not change the local environment enough to affect the deamidation rate of the glutamine residue. If anything, there is a tendency for the hydroxylated peptide to be less deamidated, consistent with hydroxylation leading to a tighter, more stable triple helix. In the case of a peptide containing two glutamine residues, we found that only one of the glutamine residues was deamidated in any sample. It could be that one of the glutamine residues consistently shows no deamidation, as we have seen with other peptides. However, from this data, we cannot say whether one of the two glutamines is selectively deamidated and the other not, and we have therefore not modeled independent rates. Previous studies have shown that asparagine and glutamine deamidation rates are heavily influenced by neighboring residues and structural constraints.9,11 The amino acid composition and tight packing of the collagen triple helix is atypical for proteins, and we have considered the position of the glutamine residues in relation to the “gap” and “overlap” regions of the collagen microfibril32 as well as the local environment but have not been able to explain the difference in deamidation rates seen here.31 Further data are necessary to investigate the different rates highlighted here, and we aim to apply our method to well-dated bones (from sites above and below datable tephras) to study glutamine deamidation in fossil samples of various ages and storage/burial conditions. The chronological ages for the samples used in this study were estimated by archeologists based on associated objects on site and are therefore very approximate. However, we have shown that the extent of glutamine deamidation can be related to bone degradation as determined by the thermally adjusted age. There is a need for reliable techniques to screen for DNA preservation prior to destructive sampling. An increase in amino acid racemization has been linked with age,33−35 and aspartic acid racemization has been successful in forensic age estimation when the postmortem interval is relatively short.36,37 The extent of aspartic acid racemization (AAR) was thought to be a good measure of the likelihood of DNA survival, but Collins et al. found no correlation between the extent of AAR and DNA amplification success and conclude that amino acid

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors thank the following for sample donations. Rosenhof and Wangels: Ulrich Schmölke and Dr. Sönke Hartz (Stiftung Schleswig-Holsteinische Landesmuseen, Germany). Castricum: Hege Hollund (Institute for Geo- and Bioarchaeology, Amsterdam, The Netherlands) and Rob van Eerden (Archaeological service of the Province of NoordHolland, The Netherlands). Kerma: Roz Gillis (National Museum of Natural History (MNHN), France), Lois Chaix (Department of genetics and Evolution, University of Geneva, Switzerland), and Jaqueline Studer (Muséum d’Histoire Naturelle, Geneva). Les Cottés: Marie Soressi, Sahra Talamo (Max Planck Institute for Evolutionary Anthropology, Germany). York: Terry O’Connor (University of York, United Kingdom).We also thank Hege Hollund, at the Institute for Geo- and Bioarchaeology, in Amsterdam for helpful comments.

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REFERENCES

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