Linking structural and compositional changes in archaeological human bone collagen: an FTIR-ATR approach

1 Vol.:(0123456789) Scientific Reports | (2020) 10:17888 | https://doi.org/10.1038/s41598-020-74993-y www.nature.com/scientificreports Linking structural and compositional changes in archaeological human bone collagen: an FTIR‑ATR approach Antonio Martínez Cortizas1 & Olalla López‑Costas1,2,3* Collagen is the main structural and most abundant protein in the human body, and it is routinely extracted and analysed in scientific archaeology. Its degree of preservation is, therefore, crucial and several approaches are used to determine it. Spectroscopic techniques provide a cost‑effective, non‑destructive method to investigate the molecular structure, especially when combined with multivariate statistics (chemometric approach). In this study, we used FTIR‑ATR spectroscopy to characterise collagen extracted from skeletons recovered from necropoleis in NW Spain spanning from the Bronze Age to eighteenth century AD. Principal components analysis was performed on a selection of bands and structural equation models (SEM) were developed to relate the collagen quality indicators to collagen structural change. Four principal components represented: (i) Cp1, transformations of the backbone protein with a residual increase in proteoglycans; (ii) Cp2, protein transformations not accompanied by changes in proteoglycans abundance; (iii) Cp3, variations in aliphatic side chains and (iv) Cp4, absorption of the OH of carbohydrates and amide. Highly explanatory SEM models were obtained for the traditional collagen quality indicators (collagen yield, C, N, C:N), but no relationship was found between quality and δ13C and δ15N ratios. The observed decrease in C and N content and increase in C:N ratios is controlled by the degradation of protein backbone components and the relative preservation of carbon‑rich compounds, proteoglycans and, to a lesser extent, aliphatic moieties. Our results suggest that FTIR‑ATR is an ideal technique for collagen characterization/pre‑screening for palaeodiet, mobility and radiocarbon research. Skeletal collagen is one of the most abundant proteins in vertebrate organisms, formed by a complex structure of fibres and microfibers that connect in a twisted, rope-like assembly1,2. Collagen 3D structure has been the focus of numerous studies in medical and biological sciences e.g.3–6 and special attention has been paid to the changes that occur at structural level since they can affect normal molecule functions in body e.g. sustention, connection, etc.7. Orthopaedic investigations have focused upon the degradation of human cartilage/bone, particularly the development of degenerative changes that result in osteoarthritis and cause modifications of the 3D structure, which occur in parallel with the advance of the disease see for example8,9. Due to its abundance and strength in skeleton, it is possible to find collagen (mainly Type I) molecules in a human body several centuries after death and even after millions of years in fossil animals10. Archaeology, forensic science and physical anthropology routinely analyse extracted collagen to understand the pre-mortem features of the deceased (e.g. diet and mobility with stable isotopes, animal species through ZooMS) and to use for radiocarbon dating11–15. Most of these analyses are based on isotopic composition fractionation with strict control at elemental composition level to discard unsuitable samples. Common concerns with the extraction of collagen are the presence of exogenous substances (e.g. humid acids) and the loss of integrity of the collagen molecule. The first concern has been alleviated by the improvement of extraction methods to provide an improved level of certainty about the elimination of non-collagen substances16. Efforts to understand extracted collagen integrity/ quality have mainly focused on applying elemental composition (i.e. C, N and C:N) cut-points, as described in17, to address the second concern. OPEN 1EcoPast, Faculty of Biology, Campus Vida, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain. 2Archaeological Research Laboratory, Stockholm University, Wallenberglaboratoriet, 10691 Stockholm, Sweden. 3Laboratory of Anthropology, Department of Legal Medicine, Toxicology and Physical Anthropology, Faculty of Medicine, Universidad de Granada, 18012 Granada, Spain. *email: [email protected] 2 Vol:.(1234567890) Scientific Reports | (2020) 10:17888 | https://doi.org/10.1038/s41598-020-74993-y www.nature.com/scientificreports/ In contrast to the invivo molecule, archaeological collagen degradation models are complex because they need to consider changes that occurred during post-mortem. Some authors have used collagen quality18,19 or modelled linear structure20 as an indicator of bone degradation with time, and, in contrast to medical sciences, less attention has been paid to structural changes. Even now, the mechanisms and processes that influence the degradation of collagen extracted from archaeological bone samples are still poorly understood. To redress this, it is necessary to unravel the changes at structural level to achieve a good understanding of archaeological collagen preservation. Despite its potential, few studies have used spectroscopic techniques to determine collagen preservation in archaeological bone21–25. Fourier Transform Infrared (FTIR) spectroscopy has been regarded as a suitable method to explore the structure of collagen26–32, by relating FTIR absorption bands (of the amide I, II and III) to specific chemical bonds and secondary structural features (α-helix, β-sheets, β-turns and random coils), even in the most recent investigations32. But, as early as the mid-twentieth century, there was a fundamental change in the comprehension of the collagen structure led by X-ray diffraction investigations, which showed that the traditional model was incorrect and the polyproline II (PPII) model was introduced and backed by later investigations, becoming the accepted model4,33–36. Although FTIR does not provide the same level of detail of the molecular composition compared with X-ray, Nuclear Magnetic Resonance (NMR) or Pyrolysis GC–MS, it can provide nonetheless valuable insights about the structure of complex molecules such as proteins37. FTIR has many advantages when compared with the conventional methods commonly used to study collagen. It is a quick, cost-effective and non-invasive method21,26. Most studies that have used FTIR on ancient skeletons focus on the characterisation of the bone mineral component among others38–41 or on taphonomic processes such as cremation42–44. The collagenous portion of bone has been analysed with relatively less frequency using FTIR15,22,45–47, and Raman spectroscopy21,48–50. Studies of bulk bone have also demonstrated that it is difficult to detect collagen content in poorly preserved bones47, whereas extracted archaeological collagen has only been directly analysed in few studies24,25,48. Therefore, previous research focused upon establishing criteria or parameters for collagen preservation screening while the changes in the structure of the molecule have received much less attention. The objective of our study is to characterise collagen extracted from archaeological human bone of different age, funerary context and burial environment, using FTIR-ATR in the mid infrared region (4000–400cm−1). By using a combination of principal components analysis (PCA) and partial least squares-structural equation modelling (PLS-SEM), we (i) discuss the possible mechanisms of archaeological bone collagen structural transformation, (ii) the potential of FTIR-ATR to predict collagen quality indicators (i.e. C, N, C:N, collagen yield) and (iii) whether collagen quality affects its isotopic (δ13C and δ 15N) composition, which is key for the study of human palaeodiet and radiocarbon dating. Results Collagen properties. Of the fifty samples analysed, collagen yield ranged between 25% (similar to intact bone) and 2% (above the proposed limit of 1%17), whereas the C:N ratio was between 3.18 and 3.57. Carbon and nitrogen contents showed a larger range (C: 44.3–17.9%; N: 6.1–16.1%). None of the samples analysed in this study exceeded the C and N values of fresh collagen (43% and 16%, respectively17,19) by more than 3%. Eight and twelve samples provided percentage C and N values below 80% of those of fresh collagen respectively and two samples (424 and 705) were below 50%. Only one sample (424) showed a C:N ratio (3.57) slightly above the range (3.02–3.56) proposed as representative for well-preserved collagen17. A wide distribution of isotopic results has been found in this study, especially for δ13C, which is interpreted as the result of palaeodietary preferences. For example, the observed differences in δ13C can be related to geographical location, whether coastal or inland, and δ13C was found to be influenced by the consumption of marine resources. Preference for the use of C4 plants in human and domestic animal diet and a strong reliance on seafood and fish—on the coast—occurs in North-Western Spain51. Historical and archaeological data agree with the obtained isotopic signatures and were discussed in detail for the analysed populations52. For the samples used in this study, collagen yield shows significant, although low, correlations only with C, N and the C:N ratio (r 0.44, 0.49 and −0.48, respectively; P < 0.01). Carbon and nitrogen contents are highly correlated (r 0.99; P < 0.01) with each other and are negatively correlated with the C:N ratio (−0.76 and −0.79, respectively; P < 0.01). Collagen compositional properties are not significantly correlated with the isotope ratios. Despite this, both isotope ratios are moderately correlated (r 0.55; P < 0.01), caused by the input of marine resources influencing some of the samples see51. Collagen FTIR‑ATR spectra. The average spectrum of the samples shows the characteristic band distribution of collagen, with high absorbance in the regions 1500–1700cm−1 and 2800–3500cm−1, moderate absorbance at 1300–1500cm−1 and relatively low average absorbance at 800–1200cm−1 (Fig.1a). The standard deviation spectrum is similar to the average one but shows a relatively large variation between samples in the region 800–1200cm−1, despite its low average absorbance (Fig.1a); whereas the 2800–3500cm−1 region only presents a peak around 3300cm−1. The most relevant peaks obtained from the second derivative spectra, in the region 800–1800cm−1, are shown in Fig.1b. Assignment of the selected bands can be found in SI_Table2. All spectra presented absorptions at 897, 918, 947, 974, 1030, 1059, 1080 and 1121cm−1 that are characteristic of carbohydrate moieties (CO st and COC st); 1236cm−1, of the amide III (CN st and NH d); 1337 and 1450cm−1, attributable to methylene (CH2 d and CH3 d; hereon named as aliphatic) absorptions; 1545, 1624 and 1719cm−1, due to amide II (CN st and NH bd) and amide I (mostly C = 0 st), respectively; 2874 and 2930cm−1 assigned to aliphatics (CH st and CH3 st); 3070cm−1, of the amide B (NH st); and a broad band 3500–3300cm−1 related to amide A (NH st) and OH 3 Vol.:(0123456789) Scientific Reports | (2020) 10:17888 | https://doi.org/10.1038/s41598-020-74993-y www.nature.com/scientificreports/ vibrations. Absorptions at 947, 974 and 1624cm−1 showed the largest variability; while absorptions at 1030, 1059, 1080, 1121, 1200, 1236, 1337, 1450, 1545 and 1655cm−1 displayed moderate variability (Fig.1b). Main spectroscopic signals of collagen. We selected 24 bands, which are representative of the different spectral regions of the type I collagen spectrum (carbohydrates, amide III, miscellaneous—mainly aliphatics— region, amide II, amide I, aliphatics, amide B, amide A/OH; for a definition of these regions see for example27,31), to perform the PCA. Four principal components accounted for 95.5% of the variance (Table1). The first component, Cp1, explains 45.5% of the total variance and it is characterised by large positive loadings (0.73–0.94) of absorptions of carbohydrates (i.e. collagen proteoglycans) and large negative loadings (−0.86 to −0.72) of absorptions of the amides (I, II and III) and the miscellaneous region (Table1). Figure1. (A) Average (black line) and standard deviation (grey line) mid infrared FTIR-ATR spectra of the whole set of collagen samples analysed in this study. (B) Average spectrum of the second derivative spectra of the analysed samples in the 1800–800cm−1 region; grey bars correspond to the standard deviation of the main absorptions. Vertical dashed lines separate the main collagen spectral regions (see “Collagen FTIR-ATR spectra” section). 4 Vol:.(1234567890) Scientific Reports | (2020) 10:17888 | https://doi.org/10.1038/s41598-020-74993-y www.nature.com/scientificreports/ The second component, Cp2, explains 38.3% of the total variance. Amide (I, II, III, A and B) and aliphatic (CH2 and CH3) absorptions show large positive loadings (Table1). Of the collagen absorption bands, Cp2 accounts for a large percentage of the 1690cm−1 (76%) and a moderate percentage of 1624cm−1 (45%) variance of amide I, and 1200cm−1 (86%) of amide III. It also contains a low (24%) percentage of the variance the 1655cm−1 absorption. Components Cp3 and Cp4 account for a minor part of the total variance, 6.7 and 5.2%, respectively (Table1). Absorptions related to aliphatics (2874, 2930 and 2982cm−1) have the largest (albeit moderate to low) loadings in Cp3. While absorptions of the amide A/OH region (3320 and 3458cm−1) and one of the carbohydrates bands (1030cm−1) show moderate and opposed (negative and positive, respectively) loadings in Cp4 (Table1). Cp1 is highly correlated (P < 0.01) to the PGI, C and N content, and the C:N ratio (Table2). Collagen yield is significantly correlated to Cp1 and Cp3, and the CI is negatively correlated with Cp3, although the correlation coefficients are low. Table 1. Factor loadings of the IR band of the extracted components. WN: wavenumber; Eigv: eigenvalue; Var: proportion of variance. The largest loading for each absorption band is in bold. WN cm−1 Cp1 Cp2 Cp3 Cp4 897 0.93 0.33 0.07 − 0.05 918 0.90 0.38 0.12 0.06 947 0.94 0.29 0.09 0.01 974 0.91 0.29 0.10 0.22 1030 0.73 0.22 0.34 0.50 1059 0.88 0.21 0.34 0.23 1080 0.91 0.31 0.25 0.00 1121 0.91 0.33 0.14 − 0.07 1236 − 0.75 0.64 0.06 0.09 1337 − 0.72 0.62 0.16 0.08 1450 − 0.72 0.58 0.19 0.21 1522 − 0.86 0.41 0.25 0.15 1545 − 0.80 0.52 0.23 − 0.04 1655 − 0.76 0.49 0.19 − 0.11 1200 − 0.28 0.93 −0.03 0.04 1624 − 0.59 0.67 0.37 0.07 1690 − 0.35 0.87 0.05 0.00 1719 0.09 0.84 − 0.33 0.02 2874 0.33 0.76 − 0.53 0.07 2930 0.02 0.88 − 0.44 0.13 2982 0.16 0.89 − 0.40 0.06 3070 0.20 0.91 − 0.30 0.07 3320 0.22 0.73 0.21 − 0.60 3458 0.46 0.58 0.15 − 0.63 Eigv 10.9 9.2 1.6 1.2 Var 45.4 38.2 6.7 5.2 Table 2. Correlation between de extracted IR principal components. Collagen yield (Coll_yield), IR indices (CI and PGI), elemental composition (C and N), C:N molar ratios and isotopic composition of the collagen. Cp1 Cp2 Cp3 Cp4 Coll_yield 0.42 0.21 0.41 − 0.22 CI 0.07 − 0.14 − 0.41 0.31 PGI 0.98 − 0.11 0.02 0.10 C − 0.93 0.05 0.11 0.09 N − 0.92 0.08 0.13 0.08 C:N 0.73 0.18 − 0.21 − 0.06 δ13C 0.00 − 0.15 − 0.09 0.17 δ 15N 0.23 − 0.07 − 0.17 0.18 5 Vol.:(0123456789) Scientific Reports | (2020) 10:17888 | https://doi.org/10.1038/s41598-020-74993-y www.nature.com/scientificreports/ Modelling collagen quality and isotopic composition. The PCA results suggest that the spectroscopic nature of extracted bone collagen can provide insights into the main transformations of its composition and structure, which may be related to collagen preservation. To do so, we applied PLS-SEM modelling to determine (i) whether transformations of the collagen structure are coupled to changes in collagen quality (i.e. C, N, C:N, collagen yield), and (ii) if changes in collagen quality affect the isotopic (δ13C and δ 15N) composition. The model was initially designed with four predictor LV (amides, backbone lipids, side-chain lipids, and carbohydrates; SI_Figure3), one primary response LV (collagen quality) and a secondary response LV (collagen isotopic composition; this one depending exclusively on collagen quality). As indicators, we used representative absorption bands for the predictor LV, analysed properties and indices (C, N, C:N, collagen yield, CI and PGI) and isotopic ratios (δ13C, δ 15N). Although the model predicted 92% of the collagen quality variance (SI_Fig- ure3), the lipids LV failed to pass the collinearity tests as it shared 88% of its variance with the amide LV and its total effect coefficient on collagen quality was very low (−0.04). As a result, for the final model we merged this LV with the amides into one LV, named as “structural components”. Of the 24 absorption bands used in the PCA, 17 met the criteria for good indicators (absolute value of the loading > 0.7, Table3) and were kept in the model. It is worth remembering that the square of the outer loading accounts for the proportion of variance of the indicator that is captured by the LV in PLS-SEM reflective mode. The loadings of the FTIR absorbances, with only one exception (1200cm−1, in the structural components LV), show that almost all their variance is captured by the modelled LV. Carbon, N, C:N and PGI also meet the criteria of good indicators of collagen quality, but collagen yield has a moderate loading and the CI a very low one (Table3). While the PGI highly co-varies with the common collagen quality parameters and maybe a valid indicator, collagen yield and CI are not. For this specific model, collagen quality is thus related to the former. Collagen yield has some dependence on operator processing (inaccuracy in pipetting, filtering, etc.). The total effects’ coefficients (Fig.2) show that the structural components have the strongest, positive effect (0.79) on collagen quality, while carbohydrates and side-chain lipids have negative total effects (−0.43 and −0.22 respectively). The weight of the structural components on collagen quality is almost two and four times higher than the weights of the other two LVs. This simple PLS-SEM model explains 92% of the variation in collagen quality (Fig.2), involving as much as 92–94% of the C and N, 85% of the PGI and 70% of the C:N variance. Figure3 shows the relationship between observed and expected values for the collagen quality indicators obtained with the PLS-SEM model. Total C and N contents and the PGI are accurately estimated, C:N ratios also show a good albeit lower performance, estimation of collagen yield is moderate and that of the CI is not significant. Table 3. Loadings of the indicators for the LV (predictors and responses) of the PLS-SEM model. LVsc: structural components, LVcb: carbohydrates, LVsc: side chains, LVcq: collagen quality, LVis: collagen isotopic composition. Indicator LVsc LVcb LVsc LVcq LVis 897cm−1 0.98 918cm−1 0.99 947cm−1 0.99 974cm−1 0.98 1059cm−1 0.95 1080cm−1 0.98 1200cm−1 0.78 1236cm−1 0.99 1337cm−1 0.97 1450cm−1 0.95 1522cm−1 0.96 1545cm−1 0.97 1624cm−1 0.94 1655cm−1 0.91 2874cm−1 0.97 2930cm−1 0.99 2982cm−1 0.99 C 0.96 N 0.97 C:N −0.84 Coll_yield 0.61 CI −0.19 PGI −0.92 δ13C 0.68 δ 15N 0.98 6 Vol:.(1234567890) Scientific Reports | (2020) 10:17888 | https://doi.org/10.1038/s41598-020-74993-y www.nature.com/scientificreports/ Additionally, at this level, collagen quality seems to have no significant effect on the isotopic composition: its total effect coefficient on the isotopic composition is low and the explained variance is almost negligible (4%). Discussion The results of the PCA are in agreement with previous investigations that use FTIR spectra to provide additional insights on protein, in particular collagen, composition and structure29,31,32,37,53–55. Different collagen types can be identified/discriminated efficiently using absorbances from selected regions of the spectrum27. In the samples analysed here, Cp1 and Cp2 seems to reflect a loss of protein backbone components. As most of the variation of the characteristic absorption of the aliphatic bonds (at 1337, 1450, 2874, 2934, and 2982cm−1) are also contained in Cp1 and Cp2, and only a smaller proportion is captured by Cp3 (Table2), it is likely that vibrations in the first two components are related to the methylene present in the backbone peptide structure whereas Cp3 may correspond to the aliphatic side chains. Cp4 seems to discriminate between the OH absorption of carbohydrates and that of the amide A. Figure4 represents a projection of samples’ scores for Cp1 and Cp2. Most samples (28 out of 50) show negative Cp1 scores and positive or slightly negative Cp2 scores. These may represent collagen with a more intact, PPII- like, molecular structure. Twelve samples show positive Cp1 scores and positive or slightly negative Cp2 scores, suggesting some degree of collagen transformation not affecting the main protein backbone structures. Samples with positive Cp1 and negative Cp2 scores may correspond to those with more intense structural modifications. Collagen quality parameters (C, N and C:N) with the most pronounced departure from those of fresh collagen occur in the two samples with the largest Cp1 values (424 from Ouvigo and 705 from Capela do Pilar; Fig.4). No evidence of soil contamination (i.e. humic acids) was detected. Our results are consistent with findings in a previous molecular study which used pyrolysis-GC–MS on 28 of the samples analysed here16. Although a detailed comparison with the molecular data cannot be done, there is an overall agreement in the classification of collagen as well or poorly preserved (20 samples out of 28). The PLS-SEM model (Fig.2) suggests that the more intact the collagen backbone structure (reflected by LVst), the higher collagen quality (higher C and N contents and, to some extent, collagen yield), while lower quality (higher C:N ratios and PGI values) is characterised by the relative abundance of carbohydrates (LVcb) and, to a limited extent, lipidic side chains (LVsc). Collagen transformation results in an overall decrease in C and N, and Figure2. Total effects coefficients of the final PLS-structural equation model, including three predictor LVs (structural components, polysaccharides, side chain lipids), one primary response LV (collagen quality) and a secondary response LV (isotopic composition). Proxies of the predictor LVs are identified by the wavenumbers of the main absorptions of collagen components. 7 Vol.:(0123456789) Scientific Reports | (2020) 10:17888 | https://doi.org/10.1038/s41598-020-74993-y www.nature.com/scientificreports/ Figure3. Correlation (coefficient of determination) between predicted (PLS-SEM model) and observed (standardized)-values for the main quality criteria indicators of collagen quality, plus de PGI and CI indices. 8 Vol:.(1234567890) Scientific Reports | (2020) 10:17888 | https://doi.org/10.1038/s41598-020-74993-y www.nature.com/scientificreports/ an increase in C:N ratios and the PGI. This points to selective bacterial degradation of the protein component (amides and backbone lipids) and the relative preservation of carbohydrates and lipidic side chains. In fact, the negative coefficient for the interaction between the structural components (LVst) and the carbohydrates (LVcb) accounts for the increase in carbohydrates as the protein component decreases, which is consistent with the results of the PCA. The ratio 1660/1690cm−1, related to maturity of collagen cross-links56, is negatively correlated to collagen quality (LVcq; r −0.77, P < 0.01) and positively correlated to carbohydrates (LVcb) and side-chain lipids (LVsc) (r 0.67 and 0.79, P < 0.01, respectively), also consistent with the PCA results. It has been proposed that the loss of spectral intensity of collagen backbone structures is most likely related to the fragmentation of the molecule due to bacterial preference for the relatively high-energy amide bonds21. Altogether, this reinforces the idea that the main collagen transformation in the samples analysed here is controlled by the degradation of the amide backbone structure. However, it is not possible to assess whether bacterial degradation occurred during body putrefaction or later soil contact. Raman analysis of collagen has shown that decreasing yield is accompanied by disappearance of amide peaks but not necessarily of aliphatic (C-H) components, since poorly preserved collagen samples produced spectra with well-defined aliphatic peaks21. Another study found that changes in amino acid composition alone could not account for the elevated C:N ratios in low collagen bone from experimentally aged human bones18. Moreover, low-collagen samples are more likely to show elevated ratios than contaminated samples17. Our results are in line with these observations since the less intact collagen samples are enriched in C-rich compounds (carbohydrates from proteoglycans and side chain lipids) and thus the C:N is expected to increase as degradation progresses. Although the presence of small amounts of non-carbon and non-nitrogen rich contaminants, as detected in other studies57, cannot be dismissed, their quantity was not deemed large enough to produce a detectable signal in the spectra. Another interesting feature is that the best-preserved samples characterised by negative Cp1 scores (Fig.4) show a high correlation (r 0.91; P < 0.01) between the CI and the PGI (Fig.5): the relative abundance of aliphatics and carbohydrates to the amide component tends to remain constant. In our opinion, this result has potential for the assessment of collagen transformation and integrity using FTIR-ATR; the larger the departure from the trend the more degraded the collagen structure. The model also suggests that collagen quality (i.e. C, N, C:N and collagen yield) has no significant effect on the isotopic composition of the collagen. This is also consistent with the PCA and correlation results obtained here and in previous investigations, since no correlation was found between molecular indicators of collagen diagenesis and isotopic composition16. Other research also found that the isotopic values (δ13C and δ 15N) and C:N ratios of the insoluble fraction remained almost stable until collagen yield represented less than 1%18. We performed ANOVA tests on the LV scores of the PLS-SEM model, using the necropoleis, archaeological period (Bronze Age to Modern period), burial environment (acidic or alkaline), sex (male or female), type of bone and age-at-death (< 19, 20–39, 40–59, > 60 estimated years old) as grouping variables. No significant differences were found for any of the LV scores (structural components, carbohydrates, side-chain lipids, and Figure4. Cp1-Cp2 projection of the PCA samples scores. FC: mass grave from post-medieval times (seventeenth or eighteenth century AD); SMM: Santa María church (Pontevedra), medieval cemetery (thirteenth–seventeenth century AD); SBA: San Bartolomé medieval churchyard (thirteenth–fifteenth century AD); CP: Capela do Pilar, inhumations from a chapel of the Lugo Cathedral (eleventh–fourteenth century AD); CR: Rúa Real, post-Roman (fifth–seventh century AD) inhumation necropolis; OUV: Ouvigo, Early-medieval (tenth–twelfth century AD) cemetery with a minor phase of burials from the Roman period (second–third century AD); LNZ: A Lanzada, inhumations from Roman and post-Roman times (first–seventh century AD); CS: Cova do Santo (nineteenth–sixteenth century BC), Bronze Age human remains found at a cavern surface. 9 Vol.:(0123456789) Scientific Reports | (2020) 10:17888 | https://doi.org/10.1038/s41598-020-74993-y www.nature.com/scientificreports/ collagen quality) for archaeological period, type of bone, sex and age-at-death (SI_Table3). Archaeological site and burial context presented significant differences for collagen quality (LVcq) and structural components (LVst) and carbohydrates (LVcb) for archaeological site only. Structural components (amides and backbone lipids) content was higher and carbohydrates content lower in Cova do Santo and Rúa Real compared to Capela do Pilar samples; the other necropoleis showing intermediate values between these two extremes. As a result, collagen quality was significantly higher in Cova do Santo and Rúa Real than in Capela do Pilar. In the latter case, the good macroscopic preservation of the skeletons does not agree with that suggested by the degree of integrity of the collagen structure. As for the burial context, the alkaline environments (the cave on limestone and the palaeodunes with biogenic carbonates) showed better collagen preservation than the acidic ones as found in previous research e.g.58,59. Although not significant at P < 0.05, structural components and carbohydrates were higher and lower (P < 0.10) respectively in the alkaline environments. Thus, alkaline conditions seem to be the main reason for the good quality of the collagen of samples from Cova do Santo (limestone cave) and those of Rúa Real and A Lanzada (burials on palaeodunes). This is perhaps surprising given the sensitivity of collagen to hydrolysis under alkaline conditions20. The reasons for this apparent disagreement may be explained by (i) relatively low alkalinity in the burial contexts (pH < 9), the rate of collagen hydrolysis largely increasing above pH 1120; (ii) well-drained/aerated conditions predominate; (iii) low decomposition of collagen matrix preventing post-mortem alteration in bone mineral crystal60,61; and (iv) the dissolution of the bone mineral phase is retarded, limiting collagen exposition to enzymatic attack. Recent research at A Lanzada concluded that the intensity of bone diagenesis was larger in burials in acidic soils than those on palaeodunes, regardless of the period (Roman or post-Roman)59. The confined environment of Cova do Santo cave could have had a larger effect than the high pH, as it was observed on research made in catacombs62. However, the particular mineral content of groundwater in this cave could also have promoted collagen preservation63. In our previous study of collagen molecular composition16, we identified a depolymerization process that differed depending on burial environment: acidic (soils/sediments) showing higher degree of depolymerization than alkaline (sand dunes and limestone cave) environments. Acidic conditions, which have been found to be the main cause of bioapatite alteration41,59 and promotion of collagen dissolution64, seem to be also important in the preservation of the protein structure—regardless of the chronological age. The oldest bones were the ones with the best preservation in our study. Finally, pH has been considered as part of “the site hydrology”—including also the mineral content of groundwater—a much more general factor that controls bone preservation65. In our study, well-drained sites (e.g. palaeodunes, such as the ones from Calle Real and A Lanzada) and places with constrained water movement (caves, as Cova do Santo) provided the best conditions for preservation. In both areas, groundwater is probably oversaturated for calcium phosphate, which would explain the good preservation of mineral and organic phases of the bone. The humidity of the soil can also promote bone degradation through microbial and fungal attack since alteration by microorganisms seems to dominate in temperate regions63: p.114. Humid conditions in NW Spain favour fungi in those soils neither well-drained nor anoxic. In addition, bones from Cova do Santo were exposed (not buried), which may have resulted in different postmortem changes61,63. Despite these reservations, we conclude that there is no single factor to explain the changes in collagen structure. All necropoleis presented relatively large variations in their samples´ collagen structural components (Fig.3); i.e. we found a range of preservation within populations rather than between populations of well/poorly preserved collagen. This may indicate that within any given geochemical environment conditions occurring Figure5. Correlation between the (standardized)-values of the PGI and CI indices. Samples showing good collagen integrity are highly correlated (those fitting the dashed line).