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Intracranial and hierarchical perspective on dietary plasticity in mammals
Accepted Manuscript Title: Intracranial and hierarchical perspective on dietary plasticity in mammals Authors: Erin M. Franks, Jeremiah E. Scott, Kevin R. McAbee, Joseph P. Scollan, Meghan M. Eastman, Matthew J. Ravosa PII: DOI: Reference:
S0944-2006(16)30128-3 http://dx.doi.org/doi:10.1016/j.zool.2017.03.003 ZOOL 25562
To appear in: Received date: Revised date: Accepted date:
21-10-2016 10-3-2017 10-3-2017
Please cite this article as: Franks, Erin M., Scott, Jeremiah E., McAbee, Kevin R., Scollan, Joseph P., Eastman, Meghan M., Ravosa, Matthew J., Intracranial and hierarchical perspective on dietary plasticity in mammals.Zoology http://dx.doi.org/10.1016/j.zool.2017.03.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Intracranial and hierarchical perspective on dietary plasticity in mammals
Erin M. Franksa, Jeremiah E. Scottb, Kevin R. McAbeea, Joseph P. Scollana, Meghan M. Eastmana, Matthew J. Ravosaa,c,d, *
a
Department of Biological Sciences, 100 Galvin Life Science Center, University of Notre Dame, Notre Dame, IN 46556, USA b Department of Anthropology, Southern Illinois University, 1000 Faner Drive, Carbondale, IL 62901, USA c
Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
d
Department of Anthropology, University of Notre Dame, Notre Dame, IN 46556, USA
* Corresponding author. Phone: +1-574-631-2556; Fax: +1-574-631-7413 Email address: [email protected]
Email addresses of co-authors: Erin M. Franks - [email protected] Jeremiah E. Scott – [email protected]; Kevin R. McAbee – [email protected]; Joseph P. Scollan – [email protected]; Meghan E. Eastman – [email protected]
HIGHLIGHTS
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Bony responses to diet varied in the skull of rabbits by region and level of tissue organization. Cortical bone formation was positively affected in masticatory elements.
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Biomineralization patterns did not track those of bone formation in the oral cavity.
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Non-masticatory sites did not develop increased bone formation or quality.
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Findings inform the inference of behavior and performance from skeletal structures.
ABSTRACT The effect of dietary properties on craniofacial form has been the focus of numerous functional studies, with increasingly more work dedicated to the importance of phenotypic plasticity. As bone is a dynamic tissue, morphological variation related to differential loading is well established for many masticatory structures. However, the adaptive osteogenic response of several cranial sites across multiple levels of bony organization remains to be investigated. Here, rabbits were obtained at weaning and raised for 48 weeks until adulthood in order to address the naturalistic influence of altered loading on the long-term development of masticatory and non-masticatory elements. Longitudinal data from micro-computed tomography (μCT) scans were used to test the hypothesis that variation in cortical bone formation and biomineralization in masticatory structures is linked to increased stresses during oral processing of mechanically challenging foods. It was also hypothesized that similar parameters for neurocranial structures would be minimally affected by varying loads as this area is characterized by low strains during mastication and reduced hard-tissue mechanosensitivity. Hypotheses were supported regarding bone formation for maxillomandibular and neurocranial elements, although biomineralization trends of masticatory structures did not mirror macroscale findings. Varying osteogenic responses in masticatory elements suggest that physiological adaptation, and corresponding variation in skeletal performance, may reside differentially at one level of bony architecture, thus potentially affecting accurate behavioral and in silico reconstructions. Together, these findings underscore the complexity of bone adaptation and highlight functional and developmental variation in determinants of skull form. Keywords: Masticatory loading; Dietary plasticity; Skull, functional morphology; Food mechanical properties
1. Introduction Bone is a dynamic, hierarchically organized tissue capable of sensing and responding to mechanical loads in order to produce a structure better equipped to withstand routine loads. This capability implies that bone is a plastic structure whereby altered external conditions initiate an osteogenic cascade that modulates anatomical structure. Generally, these environmentally induced, postnatal responses reflect the functional or adaptive nature of an element or system of interest (Gotthard and Nylin, 1995; Agrawal, 2001; West-Eberhard, 2003). By fine-tuning the link between form and behavior, an organism can achieve a phenotype better matched to its surroundings. Adaptive plasticity in skeletal form is related to functional adaptation, or the dynamic coordinated series of cellular, tissue, and molecular
processes of skeletal modeling and remodeling that maintain a sufficient safety factor for routine peak and cyclical loads (Bouvier and Hylander, 1981, 1996a,b; Lanyon and Rubin, 1985; Biewener, 1993). Given increasing evidence that safety factors vary across the vertebrate skeleton (Hylander et al. 1991a,b; Ravosa et al., 2000a, 2010a), it is particularly important to evaluate if patterns of functional adaptation also differ regionally. Among mammals, the plasticity of masticatory elements to altered loading conditions has been well studied in lagomorphs, rodents, carnivorans, suids, hyracoids, and primates (Beecher and Corruccini, 1981; Bouvier and Hylander, 1981, 1982, 1984, 1996a.b; Beecher et al., 1983; Bouvier, 1988; Yamada and Kimmel, 1991; Ciochon et al., 1997; He and Kiliaridis, 2003; Lieberman et al., 2004; Larsson et al., 2005; Nicholson et al., 2006; Ravosa et al., 2007, 2008a,b, 2010b, 2016; Menegaz et al., 2009, 2010; Scott et al., 2014a,b; Menegaz and Ravosa, 2017). These prior studies, though limited primarily to the feeding apparatus, have generally demonstrated a significant response to elevated loading conditions in various skeletal parameters such as external dimensions, cortical bone thickness, cross-sectional area, and tissue mineral density. Limited research has been conducted on the plasticity of sites less directly involved with oral processing, though gross proportions of the neurocranium and calvarial cross-sectional thickness have been investigated in lagomorphs (Menegaz et al., 2010; Franks et al., 2016), with results suggesting a lack of a significant plastic response. As stated above, previous studies of diet-induced plasticity were largely focused on a single masticatory element or functional region or, to a lesser degree, the hard- and soft-tissue responses that maintain the integrity of composite structures such as the mandibular symphysis or temporomandibular joint (Ravosa et al., 2007, 2016; Ravosa and Kane, 2017). As such, a comprehensive regional analysis of adaptive plasticity of hard tissues across multiple craniomandibular sites in the same specimens is lacking. This is of critical importance as within-element variation in limb responses (Hsieh et al., 2001; Hamrick et al., 2006) suggests that similar regional variation may exist in the skull. It is also becoming increasingly evident that varying skeletal parameters respond differently to a given loading pattern (Kohn et al., 2009; Wallace et al., 2009; Scott et al., 2014a; Ravosa et al., 2015b, 2016) and that the hierarchical nature of bone allows it to respond adaptively to mechanical stimuli at multiple levels. To this end, the performance of skeletal elements is dictated by a number of factors including bone quality, bone quantity, and bone distribution and there are multiple mechanisms for increasing bone strength. Thus, it is possible that a functional signal may be differentially represented at one level of organization vs. another level, potentially posing an issue for accurate behavioral and functional characterizations. For example, in a prior study of the rabbit mandibular
symphysis, it was demonstrated that an internal dimension, cortical bone thickness, exhibited a greater disparity vs. gross external dimensions (Ravosa et al., 2007, 2008a). Furthermore, the disparity in cortical bone biomineralization between rabbit dietary groups was shown to be lower than that for cortical bone thickness (Ravosa et al., 2007, 2008b). These findings are of critical importance because gross skeletal dimensions are more commonly used to track diet-related variation in morphological studies (e.g., Ravosa, 1991, 1996; Ravosa and Hogue, 2004; Wright, 2005; Friscia et al., 2007) and, thus, it is unlikely that the singular use of external dimensions will furnish the requisite evidence for meaningful paleobiological reconstructions (Ravosa et al., 2016). Additionally, it further underscores the complexity of bony organization and adaptation. Currently, there is a significant gap in our understanding of the effects of varying diets on regional and hierarchical variation in hard tissues of the developing skull and feeding apparatus. To complement prior analyses, here we report the results of a long-term diet manipulation experiment conducted using an animal model (white rabbit) that examines adaptive plasticity at various levels of bony organization at multiple bony sites across the skull vis-à-vis variation in food mechanical properties. More specifically, we probe the relationship between masticatory loading and morphological plasticity in a number of representative bony regions in order to ascertain the effect of elevated loading on the overall craniomandibular unit. Moreover, to investigate how altered loads differentially affect varying levels of bony organization, each region was assessed on a macro- and microscale to document the dynamic cascade of coordinated adaptive events. We test the hypothesis that cortical bone formation and bone quality in the developing skull adapt postnatally to increased masticatory loading and the resulting elevated stresses via coordinated osteogenic processes. Given that maxillomandibular bone strain levels are higher than elsewhere along the mammalian skull during routine feeding behaviors (e.g., Hylander et al., 1991a,b; Ravosa et al., 2006, 2010a) and that neurocranial osteoblasts exhibit reduced mechanosensitivity vs. elsewhere in the skeleton (Rawlinson et al., 1995; Ravosa et al., 2015b), it is first predicted that masticatory regions will exhibit a more pronounced response to elevated loading, indicating the presence of regional variation in diet-induced plasticity. Second, it is predicted that long-term increased masticatory stresses will result in skeletal elements of the feeding complex with more robust proportions and increased bone quality. Thus, hard tissues in the masticatory region of rabbits raised on a more challenging diet should develop increased cortical bone thicknesses and elevated biomineralization. In contrast, non-masticatory regions (i.e., the neurocranium) should display a minimal plasticity response at both levels of analysis given that these regions
experience lower strains during oral processing.
2. Materials and methods 2.1. Animal model and experimental design To evaluate the long-term plasticity of cranial elements vis-à-vis altered loading levels, 20 genetically similar male New Zealand white rabbits (Oryctolagus cuniculus) were obtained at weaning (five weeks old) from Harlan Laboratories and housed at the University of Notre Dame’s animal care facility, Freimann Life Science Center. Both institutions are USDA-licensed and AAALAC-accredited and subject to periodic inspections. Day-to-day care of the animals, including periodic health evaluations, was handled by trained veterinary staff. Additionally, all procedures were approved by the University of Notre Dame’s Institutional Animal Care and Use Committee (IACUC). The animals were raised for 48 weeks immediately following weaning, making them 53 weeks old at the conclusion of the experimental period. In white rabbits weaning typically occurs at 4–5 weeks of age, while skeletal and sexual maturity are reached at ~26 weeks of age (Masoud et al., 1986; Isaksson et al., 2010). Previous experimental work has established that the masticatory apparatus of growing rabbits is sensitive to variation in food mechanical properties (Ravosa et al., 2007, 2008a, 2010b; Menegaz et al., 2009; Scott et al., 2014a,b), exhibiting levels of phenotypic diversity between treatment groups that mirror evolutionary variation between closely related species with different diets (Ravosa et al., 2016). Importantly, white rabbits resemble other mammalian herbivores in key features of the masticatory apparatus and bone biology that make them a suitable model organism. These features include: (i) the configuration of the skull, which is characterized by a vertically deep facial skeleton, tall mandibular ramus, and a temporomandibular joint (TMJ) situated high above the occlusal plane; (ii) mandibular kinematics, with a TMJ capable of rotational and translational movements, and transverse jaw movements during mastication; (iii) intracortical bone remodeling; and (iv) well-characterized patterns of covariation among dietary properties, jaw-adductor muscle activity, and jaw-loading regimes (Weijs and de Jongh, 1977; Weijs and Dantuma, 1981; Weijs et al., 1989; Hirano et al., 2000; Langenbach and van Eijden, 2001; Crompton et al., 2006; Ravosa et al., 2010a). Weaning was chosen as the starting point for dietary manipulation because it mitigates the effect of other dietary influences that may confound subsequent comparisons between groups. Additionally, because mammals begin to adopt adult diets and chewing behaviors around the onset of weaning (Weijs and Dantuma, 1981; Herring, 1985;
Weijs et al., 1989), commencement of diet manipulation at this early developmental stage facilitates a more naturalistic experiment regarding dietary plasticity (Ravosa et al., 2007). Upon arrival, the rabbits were divided equally into two cohorts (n = 10 each). Animals in the first group, the control rabbits, were fed an ad libitum diet of Purina rabbit pellets (Purina Mills, LLC, Gray Summit, MO, USA) throughout the experiment. Animals in the second group, the over-use rabbits, were fed two hay cubes (~3.21 cm ×1.9 cm ×1.9 cm) per day in addition to ad libitum pellets for the entirety of the experimental period, modeling a mechanically challenging diet. Data on the mechanical properties of pellets and hay cubes demonstrate that the latter are more mechanically challenging in terms of the effort required for oral breakdown. Hay has a higher elastic modulus (i.e., it is stiffer: pellets, E = 29.2 MPa; wet hay, E = 277.8 MPa; dry hay, E = 3335.6 MPa) and is tougher (Ravosa et al., 2007; Menegaz et al., 2009). Stiffness characterizes an object’s resistance to elastic deformation and is quantified via the stress/strain ratio at small deformations; this is known as the elastic, or Young’s modulus (E). Toughness is an energetic property describing the work that has to be performed to propagate a crack through an item. Indeed, rabbits use approximately three times more chewing cycles per unit food mass when processing hay vs. pellets (2.95 times more chews per g), which results in longer chewing durations, averaging 3.22 times longer (Ravosa et al., 2015a). The singular importance of cyclical loading for our analyses is further underscored by the similarity in mandibular peak-strain levels during hay and pellet processing (Weijs and de Jongh, 1977). Given the adaptive role of increased cyclical loading in bone formation (Bouvier and Hylander, 1981; Biewener et al., 1986), hay consumption should thus stimulate osteogenesis and result in larger jaw proportions in the over-use dietary group in comparison with the control rabbits (Ravosa et al., 2007, 2008a, 2016; Menegaz et al., 2009; Scott et al., 2014a,b).
2.2. μCT analysis and measurements Longitudinal skull growth was tracked in vivo using (μCT) (Bioscan/Mediso X-CT, Budapest, Hungary; settings: 70 kVp and 100 μA, with a 71 μm reconstructed isotropic voxel size). Sedated rabbits were scanned at the beginning of dietary manipulation upon arrival (week 0; 5 weeks of age) and then every 2 weeks thereafter until week 24, the end of the first half of the experimental period. Week 24 corresponds to a chronological age of 29 weeks, or 6.67 months (i.e., young adulthood). At this point in the experiment, the rabbits were too large to be scanned. We therefore lack longitudinal data for the second half of the experiment. All subjects were scanned a final time at the end of the experimental period (week 48, or 53 weeks of age) following death. Reconstructed scans were analyzed using the
program PMOD version 3.3 (PMOD Technologies Ltd, Zurich, Switzerland). Scans were oriented so that the sagittal plane was parallel to the computer monitor and the occlusal plane was horizontal. Following orientation, a series of measurements were collected including cortical bone thicknesses and biomineralization at two mandibular sites, one maxillary site, and three sites along the neurocranium (Fig. 1 and Table 1). Cortical thicknesses were linearly measured, whereas biomineralization was quantified using uniform size circular regions of interest (ROIs). These were sampled at the same locations of cortical thickness as described in Table 1. Biomineralization was measured in dimensionless Hounsfield units, which range from –1000 (air) to 3000 (dense cortical bone). Maximum cranial length was recorded for size adjustment of linear measures so that findings singularly reflect the direct effects of dietary manipulation.
2.3. Statistical analysis To confirm the presence of postnatal growth and the potential for a plasticity response, we examined within-group differences over the experimental period in raw measurements using t-tests. For each dietary group, we compared mean differences between week 0 and week 24 (before skeletal maturity) and between week 24 and week 48 (after skeletal maturity). To test for between-group (i.e., dietary) effects, we compared size-adjusted measurements using t-tests. We also examined differences in raw measurements between dietary groups, but the pattern of results differed little from the analysis of size-adjusted values. Size-adjusted measurements used cranial length as a proxy for overall skull size to correct for size-related variation unrelated to the diet protocol. To simplify the analysis, between-group analyses were conducted at four timepoints: week 0 (onset of dietary manipulation), week 12, week 24 (halfway through the experimental period) and week 48 (end of dietary manipulation). To reduce the chances of obtaining a type I error due to multiple comparisons, a Bonferroni correction was employed (α/n comparisons at each time point (0.05/13 = 0.0038). Thus, the p-value threshold for significance was 0.0038.
3. Results 3.1. Cortical thickness 3.1.1. Within-group differences For all variables within both dietary groups, there were significant mean differences between week 0 and week 24 (Tables 2 and 3), indicating that cortical thicknesses increased significantly throughout the first half of the
experimental period. Between week 24 and week 48, however, there were no additional changes in cortical thicknesses.
3.1.2. Between-group differences At the onset of dietary manipulation, there were no differences between groups, indicating that cohorts were similar prior to the experimental period. Groups remained similar at week 12 with significant differences not appearing until week 24; analyses revealed significant differences in three out of seven masticatory variables (lateral corpus, lateral symphysis, hard palate) with thicknesses being greater in the over-use group for each variable (Tables 4 and 6). No significant differences were present in any of the neurocranial thicknesses. Similarly, at week 48, the end of the experimental period, analyses indicated significant differences between groups in the same three masticatory elements (Table 4), whereas the other masticatory thicknesses and neurocranial measures remained nonsignificant. Trends of the mean differences were the same as in week 24, with the over-use group having the largest thicknesses (Table 6).
3.2. Biomineralization 3.2.1 Within-group differences For all variables within both dietary groups, there were significant mean differences between week 0 and week 24 (Tables 7 and 8). Except for the three symphyseal variables (of both groups), the means were greater at week 0 than week 24, indicating a decrease in mineral density over the first half of the experimental period. Between weeks 24 and 48, signficant changes occurred though they were not as uniform (Tables 7 and 8). For the neurocranial sites within both groups, there were no significant changes between weeks 24 and 48 except for the superior cortical thickness of the parietal bone in the over-use group, which had a further decrease in mineralization (Table 8; p = 0.0016). None of the masticatory variables in the control cohort changed significantly between week 24 and week 48 (Table 7). By contrast, five of seven masticatory sites (two of three corpus sites and all three symphyseal site) in the over-use group decreased significantly over the latter half of the experimental period (Table 8). In comparing patterns across the experimental period, patterns were similar between groups in neurocranial variables but differed at masticatory sites during the second half of the experimental period (after skeletal maturity).
3.2.2. Between-group differences There were no differences between groups at weeks 0, 12, or 24, with no discernable trend as to which dietary group and/or sites exhibited greater mineralization (Tables 9 and 10). At week 48, however, significant differences were found in two masticatory elements, the inferior corpus and the hard palate (Table 9). Mineralization in the inferior corpus was less in the over-use group; this pattern was reversed in the hard palate, with the over-use group having greater mineralization (Table 10). Despite a significant difference at only one of the mandibular sites, mineralization was lower in all six mandibular sites in the over-use group when compared to the control group. This is in contrast to the maxillary site (hard palate) where the over-use cohort had greater mineralization values.
4. Discussion 4.1. Masticatory region 4.1.1. Within-group trends Rabbits in both groups experienced significant cortical thickness increases in all of the masticatory variables between the onset of dietary manipulation and halfway through the experimental period (24 weeks). These macroscale increases were accompanied by microscale decreases in tissue mineral density except at the three symphyseal sites, which experienced an increase in biomineralization. Greater mineralization at the symphysis is consistent with previous findings that demonstrated elevated mineralization in the symphysis in association with symphyseal cartilage degradation (Ravosa et al., 2007, 2008a,b, 2016). As joints are composed of soft and hard tissues that respond individually and interactively, increased biomineralization in part represents a compensatory response to cartilage degradation that maintains the overall functional integrity of such composite joint systems (Ravosa and Kane, 2017). This may explain age-/load-related fusion of the symphysis in older and over-use rabbits, as this would represent a compensatory osteogenic response to degradation of the symphyseal cartilage. An inverse relationship between thickness and mineralization suggests that rabbits are born with thinner, yet more highly mineralized cortical bone and that as growth occurs the cortical bone becomes thicker without a corresponding increase in mineralization. Interestingly, this pattern yet again highlights the role of different aspects of skeletal architecture for maintaining the biomechanical integrity of masticatory structures throughout ontogeny. It likewise underscores the novel benefits of a multifactorial perspective to understanding skeletal form and function. Halfway through the experimental period roughly corresponds to the age at which skeletal maturity is reached in
rabbits (~26 weeks) (Masoud et al., 1986). Thus, such significant changes during the first half of dietary manipulation are expected given the dual role of bone modeling, which is diminishing, and remodeling. In this regard, the second half of the experimental period (from week 24 to week 48) corresponds to post-skeletal maturity, when only remodeling is occurring in response to loading environment. Significant changes should, therefore, reflect adaptation to dietary loading. Aside from an increase in one cortical thickness (lateral symphyseal thickness in the over-use group), there were no other significant changes among masticatory sites. The fact that the lateral symphyseal thickness increased in the over-use cohort is unsurprising given the high compressive loads experienced along the lateral symphysis during mastication coupled with the elevated loads induced by a more mechanically challenging diet (see Section 4.1.2). Changes in biomineralization between weeks 24 and 48 were not similar between dietary groups, suggesting that mineralization was more plastic over the second half of the dietary manipulation period. There were no further changes in any of the masticatory variables, whereas the over-use cohort underwent more significant changes whereby five of seven masticatory sites decreased in biomineralization. Further decreases in mineralization are likely due to elevated remodeling levels resulting from increased stresses as a function of a more mechanically challenging diet (see Section 4.1.2).
4.1.2. Between-group trends Between-group comparisons indicated that growing rabbits subjected to elevated masticatory loads demonstrated varying plasticity patterns across the maxillomandibular complex. The presence and magnitude of the reaction norm was dependent on the masticatory site and level of skeletal organization. For cortical bone thicknesses, sites responded significantly by developing increased thicknesses at three of seven sites. Postweaning differences appeared by week 24 and were maintained through week 48. Though nonsignificant when applying the Bonferroni correction, thicknesses of the inferior corpus and superior symphysis were close to significance, while the medial corpus and medial symphysis were mostly nonresponsive. Within the corpus and symphysis there appears to be a graded response, with the lateral dimensions displaying the greatest plastic response, superior/inferior dimensions demonstrating an intermediate response, and the medial dimensions exhibiting a minimal response. Indeed, the lateral dimensions of the corpus and symphysis showed the greatest osteogenic response of all sites, differing by as much as 30% between treatment groups. The pattern of thinner cortical bone along the lingual aspect of the postcanine corpus has been documented across
numerous anthropoid primates (Daegling and Holtzman, 2003, and references therein) and likely corresponds to the stress distribution along the mandible. In vivo bone strain analyses at the mandibular symphysis and corpus have indicated that the anthropoid mandible experiences lateral transverse bending or “wishboning” during the power stroke of mastication. This loading regime results in compressive strains along the labial symphysis and lateral corpus coupled with tensile strains along the lingual symphysis and medial corpus (Hylander 1984, 1985; Hylander et al., 1987; Dechow and Hylander, 2000). Wishboning occurs during the end of the power stroke of mastication and is due primarily to the late peak activity of the balancing-side deep masseter and posterior temporalis muscles (Hylander et al., 1987, 2000, 2005; Hylander and Johnson, 1994). Although a wishboning strain pattern has yet to be documented in rabbits, they are characterized by a balancing-side deep-masseter muscle recruitment pattern similar to that of anthropoids (Weijs and Dantuma, 1981; Weijs et al., 1989). This suggests that the pattern of forces experienced across the rabbit mandible is similar and may serve as an explanation for the cortical bone distribution noted herein. Though specific to masticatory structures, these results underscore the presence of among- and within-element variation in the distribution of cortical bone, supporting previous findings that suggested the presence of varying functional responses among masticatory elements in the same organisms (Scott et al., 2014a; Ravosa et al., 2015b, 2016). These results likewise correspond to numerous studies that have shown variability in the response of limb bones to mechanical stimuli, not only among skeletal elements but also among regions within the same bone (Jaworski et al., 1980; Mosley et al., 1997; Iwamoto et al., 1999; Hsieh et al., 2001; Hamrick et al., 2006). Taken together, this implies that functional adaptations are site-specific and that there is considerable heterogeneity in osteogenic responses across the skeleton (Rawlinson et al., 1995; Ravosa et al., 2010a, 2015b, 2016). Interestingly, biomineralization trends did not track the results seen in cortical bone thicknesses. No differences between groups were observed until the end of the experimental period, with differences present at only two of seven maxillomandibular sites (inferior corpus, hard palate). Though differences were found at these two sites at the same time point as in cortical bone thickness, the overall trends were different. At the inferior corpus, tissue mineral density was significantly decreased in the over-use group though it increased significantly at the hard palate. Despite a lack of significance in biomineralization at the five other masticatory sites, biomineralization was decreased in the over-use group at all sites, contradicting the hypothesis that increased cortical thickness would be coupled with increased mineralization throughout the experimental period.
This hierarchical disparity in plasticity responses of masticatory elements could be due to the interplay between two different mechanisms of bone formation. Bone modeling occurs largely from birth to skeletal maturity and is responsible for increases in skeletal mass and changes in form. In contrast, remodeling occurs during later stages of skeletal development (Goldman et al., 2009). Only at a year old, the final time point for the present study, would mineralization values reflect a more singular role of bone remodeling in the maintenance of skeletal integrity. As previously mentioned, bone is a dynamic tissue that continuously undergoes adaptive remodeling (i.e., osteonal resorption and formation) to meet the requirements of its functional environment (Lanyon and Rubin, 1985). Mechanical loading of bone is caused predominantly by muscle contractions and, thus, muscles provide an important stimulus for bone remodeling by inducing strains in the skeletal system (Turner, 2000). Therefore, the remodeling rate of bone is related to the magnitude and frequency of mechanical loading and the resulting strains in the tissue (Lisková and Hert, 1971; Turner, 1998). While this may suggest that bone subjected to elevated loading will adapt by increasing both bone quantity and quality to better withstand forces, it has been demonstrated that these parameters need not track one another. Remodeling rate is a major determinant of the degree of mineralization of bone, with a higher remodeling rate due to increased loads resulting in decreased time available for secondary mineralization and, in turn, bone with a lower degree of mineralization (Lanyon and Rubin, 1985; Reid and Boyde, 1987; Meunier and Boivin, 1997; Cullen et al., 2001; Boivin et al., 2009). In other words, more heavily loaded bone in organisms that have stopped growing has a higher remodeling rate, which reduces its average lifespan and consequently produces less mineralized tissue (as a whole) than less-stressed bone not undergoing such rapid turnover. In the context of diet-induced plasticity of the masticatory apparatus, a previous study compared the degree of mineralization in cortical bone of multiple mandibular sites in rabbits fed soft vs. control pellets (Grünheid et al., 2011). Soft pellets required significantly reduced peak loadings (10 N/cm2) to break the pellet in comparison to control pellets (120 N/cm2). Unlike the present study, however, that research found no significant differences between groups at any of the sites examined. This discrepancy can possibly be explained by the fact that Grünheid et al. (2011) only raised rabbits until 20 weeks of age, which may not have been a sufficient amount of time to generate differences between groups. Indeed, no differences were found in the present study until 48 weeks. However, the findings of Grünheid et al. (2011) also differ from the results of Ravosa et al. (2007) where rabbits were grown to the same age (20 weeks) and did demonstrate between-group differences in biomineralization along the symphysis
and condyle. Between-group differences present in Ravosa et al. (2007), but not Grünheid et al. (2011), may be the result of different dietary regimens. Ravosa et al. (2007) employed an over-use vs. under-use diet where the underuse diet consisted of ground pellets and over-use was standard pellets supplemented daily with 2-cm hay cubes. This regimen imposes a greater disparity in mechanical loads than a soft vs. control diet. Indeed, in Grünheid et al. (2011) a slighter disparity in food properties may not have imposed a great enough difference in masticatory functional loads to affect the remodeling rate and resulting degree of mineralization. Furthermore, these findings differ from the results of other studies, which showed that feeding rats a less mechanically challenging diet leads to a reduction in the rate of bone apposition (Yamada and Kimmel, 1991), resulting in lower bone mass (Bresin et al., 1999) and alveolar bone density (Mavropoulos et al., 2004, 2005), as well as a higher degree of mineralization in the mandible (Tanaka et al., 2007). Thus, the results of the present study are in agreement with the majority of past studies; increased functional loads generate a higher rate of bone remodeling and, ultimately, a less mineralized structure.
4.2. Non-masticatory region 4.2.1. Within-group trends Like the cortical thicknesses of the masticatory region, the thicknesses of all neurocranial sites in both groups increased across the first half of the experimental period. These increases were also accompanied by decreases in biomineralization. Again, this inverse pattern suggests that the neurocranium maintained mechanical integrity across ontogeny. It appears that at younger ages tissue strength is achieved through thinner, more mineralized bone; throughout the growth period, the bone becomes thicker without a corresponding increase in mineralization. Given the disparity in strain levels between masticatory and non-masticatory regions, shared biomineralization decreases and cortical bone increases may highlight a general postnatal pattern of bone formation in the skull (and perhaps the skeleton). Aside from one neurocranial site, there were no additional changes in any of the thicknesses between weeks 24 and 48. The exception was a further decrease in the mineralization of the superior thickness of the parietal bone, which occurred in the over-use group during the latter half of the study. Overall, these results suggest that most mineralization values become stable around skeletal maturity (~26 weeks). Similar patterning of cortical bone parameters within groups lends credence to the notion that growth of the neurocranium may be more genetically canalized (see more in Section 4.2.2).
4.2.2. Between-group trends There were no differences between groups in cortical thickness or biomineralization at any of the neurocranial sites throughout the experimental period. Together, these results indicate that the neurocranial region did not respond to heightened masticatory loads. This finding can potentially be explained by multiple factors. First, there is a considerable body of in vivo experimental work that has established the presence of a strain gradient in the skull of primates and other tetrapods. These examinations have unequivocally demonstrated that peak-strain magnitudes are highest along the maxilla and mandible, and markedly lower along the neurocranium and circumorbital region (Buckland-Wright, 1978; Hylander et al., 1991a,b; Ross and Hylander, 1996; Hylander and Johnson, 1997; Herring and Teng, 2000; Ravosa et al., 2000a, b, c, 2006, 2010a; Thomason et al., 2001; Lieberman et al., 2004; Ross and Metzger, 2004). In this model, relatively high strains associated with the feeding complex are due to its direct involvement in resisting loads during biting and chewing. In contrast, low strains in the cranial vault suggest that it is overbuilt for resisting chewing stresses. Such low strains could perhaps be due to a larger distance from the application of bite, muscle, and reaction forces; however, this would be contrary to models regarding optimal strain levels and similar safety factors across skeletal elements (Lanyon and Rubin, 1985; Biewener et al., 1986). Alternatively, bone formation in the neurocranium and circumorbital region may be less labile and, thus, under greater intrinsic genetic control to ensure that sufficient bone exists to resist rare, traumatic loads (Hylander and Johnson, 1997; Ravosa et al., 2000a,b,c, 2010a). This implies the presence of intracranial variation in tissue sensitivity to mechanical stimuli and related osteogenic responses. Such site-specific phenomena characterize the disparate responses of calvarial and ulnar bone cells to similar loads (Rawlinson et al., 1995). This suggests that the reduced osteogenic response of calvarial bone cells may not be associated with extrinsic factors such as lower stresses due to a more distant position from the oral cavity. Rather, it is possible that osteoblast activity further varies according to craniomandibular region (Quarto et al., 2010; Li et al., 2013; Ravosa et al., 2015b, 2016). While it may be reasonable to infer that negligible levels of diet-induced plasticity in the neurocranium are due to the presence of a strain gradient and reduced hard-tissue mechanosensitivity, several lines of evidence provide added support for the latter interpretation. A low strain environment should, according to the functional adaptation paradigm (Lanyon and Rubin, 1985), lead to a decrease in the amount of bone, yet this does not occur. Moreover, avian ulnae subjected to low-level dynamic strains have demonstrated bone formation (Rubin and Lanyon, 1984), a
result not witnessed in our analyses despite the presence of dynamic high-frequency masticatory loads (Ravosa et al., 2015a). Thus, regardless of whether one accepts the claim that the neurocranium is overbuilt to resist traumatic loads, the lack of plasticity in this region coupled with lower mechanosensitivity of calvarial osteoblasts strongly implicates the presence of a mechanism that maintains skeletal mass and structural integrity in the neurocranium independent of the osteogenic stimuli associated with feeding.
4.3. Dietary plasticity and bone formation While cortical bone thicknesseses and biomineralization of masticatory regions revealed disparate adaptive signals, a related paper (Franks et al., 2016) investigated diet-induced plasticity in the zygomatic arch, an intervening structure connecting the neurocranium to the feeding apparatus. It was demonstrated that elevated masticatory loading did not yield differences in cortical bone thickness, though biomineralization was positively affected (Franks et al., 2016), an arrangement not seen in the present study. Taken together, the findings of the two studies reveal that there are multiple ways of increasing bone strength and, ultimately, functional performance. In the present study, differences in bone quality were always accompanied by differences in bone quantity, suggesting the coupling of these two parameters. The reverse was not true, however; macroscale differences in bone quantity were not always accompanied by a microscale difference in bone quality. As evidenced in related work on the plasticity of the zygomatic arch in the same experimental subjects (Franks et al., 2016), changes in bone quality are possible without corresponding changes in bone quantity. Together, these findings highlight the various ways of maintaining the structural integrity of bony elements that are subjected to elevated stresses and underscore the complexity of inferring form from function as bone quantity and quality can be positively related, inversely related (Kohn et al., 2009; Wallace et al., 2009, 2010), or potentially unrelated. More broadly, observations from the present study have considerable implications for interpreting the behavior and evolutionary history of fossil (and living) mammals. As hard tissues are highly mineralized, the remains of bony elements represent the primary means by which we infer the paleobiology of extinct organism, with skeletal anatomy used to infer a given behavior. Analysis of cranial remains typically focuses on external gross dimensions (e.g., lengths, widths) of phenotypic variation and less frequently employs a hierarchical or regional perspective that incorporates information on variation in internal anatomy from multiple sites. The present findings suggest that standard, morphology-based analyses of single cranial (and postcranial) variables likely hinder accurate
paleobiological inference as the adaptive signal can vary hierarchically within an element and across skeletal sites, conveying different signals for behavioral reconstructions using fossil remains (Ravosa et al., 2016). The present study thus echoes previous notions that certain cranial sites (e.g., the neurocranium) may be under greater genetic control, with the phenotype expression driven primarily by selective forces (Hylander and Johnson, 1997). In contrast, other elements, such as the mandible, may be more labile, with morphological variation resulting from a combination of adaptive plasticity and selection. This poses a challenge when attempting to interpret the functional signal of a given feature, as one must partition how much of an observed phenotype is due to the individual loading history of a specific organism. This is also of relevance to the process of evolution in that altered reaction norms change the heritable variation upon which natural selection acts. Because plasticity affects phenotypic covariance structure, this can alter the manner in which hereditary variation and selection interact to generate evolutionary change (Gupta and Lewontin, 1982; Cheverud et al., 1983; Stearns, 1989). This means that the influence of diet-induced plasticity on cranial variance and covariance patterns will, in turn, modulate the phenotypic response to natural selection. Findings from the present study are also relevant to in silico modeling, such as the use of finite element analysis (Richmond et al., 2005; Strait et al., 2005, 2009; Rayfield, 2007; Gill et al., 2014). This technique has been increasingly employed in functional analyses of fossil skeletal remains and requires the incorporation of realistic tissue properties to ensure biologically meaningful results. Consequently, our results highlight the potential limitations of computer modeling as currently practiced by illustrating the hierarchical and site-specific variation of bone material properties present in a morphologically complex structure such as the skull (see also Thompson et al., 2017). Moreover, if tissue mineral density indeed tracks bone mechanical properties, the presence of intracranial variation in biomineralization (within treatment groups) implies the presence of considerable anisotropy in hardtissue properties. That bone is typically assumed to be isotropic in most computer models of extant and extinct organisms (e.g., Richmond et al., 2005) suggests that further functional accuracy will be attained when site-specific data regarding bone anisotropy are fully incorporated (Ravosa et al., 2016). As bony structures that appear similar based on external form can have a highly disparate internal organization (e.g., anterior pillar in australopiths – Villmoare and Kimbel, 2011), this, too, may undermine the accuracy of an in silico model.
5. Conclusion
In summary, this study presents an examination of the effects of varying diets on regional and hierarchical variation in hard tissues of the developing skull and feeding apparatus. The data presented here illustrate diet-related differences in both cortical bone thicknesses and the degree of mineralization, with differences found between masticatory and non-masticatory regions as well as within regions. However, the functional signals at these two levels of bony organization did not mirror one another and initially seemed contradictory. While increased cortical bone thicknesses were expected at maxillomandibular sites, decreased biomineralization was not predicted. This discrepancy can ultimately be explained by considering the process of adaptive remodeling as it relates to loading environment, whereby greater mechanical loads induce a higher rate of remodeling, resulting in less mineralized tissue. Crucially, this pattern further speaks to the hierarchical organization of bone and the potential for variation in the functional signal depending on the level of analysis. Additionally, it highlights the importance of incorporating multiple architectural parameters into the investigation of morphological variation and functional interpretation, while cautioning against the use of a single variable. For example, if the degree of mineralization were used in the present study as the sole variable for assessing the form and function of these cranial sites, it would yield a much different assessment than when used in combination with cortical thickness. Coupling parameters that describe both the quantity and quality of the bone allows for a more thorough understanding of adaptive plasticity across the skull (and postcranium). Moreover, it allows for the development of a more comprehensive analysis of form–function relationships that may reveal novel patterns of bony organization and biomechanical performance throughout ontogeny.
Acknowledgments Olga Panagiotopoulou and Pepe Iriarte-Diaz kindly invited us to contribute to the Special Issue on “Determinants of mammalian feeding system design”. We also thank the Notre Dame Integrated Imaging Facility and the staff of the Freimann Life Science Center. Funding was obtained from the Wenner-Gren and Leakey Foundations to E.M.F. and the NSF to M.J.R. (BCS-1029149/1214767).
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Fig. 1. μCT coronal cross-sections depicting sites of interest on a rabbit skull.
Table 1. Description of collected measurements on rabbit skulls. Variable Corpus
Location Lateral
Medial
Symphysis
Description Thickness and biomineralization of the lateral cortical bone of the corpus, taken approximately midway between the superior and inferior borders of the corpus at P4 in the coronal plane Thickness and biomineralization of the medial cortical bone of the corpus, taken approximately midway between the superior and inferior border of the corpus at P4 in the coronal plane
Inferior
Thickness and biomineralization of the inferior cortical bone of the corpus taken at P4 in the coronal plane
Lateral
Thickness and biomineralization of the lateral cortical thickness of the symphysis taken in the coronal plane at 25% of the distance from the posterior border of the main body of the symphysis
Medial
Thickness and biomineralization of the medial cortical thickness of the symphysis taken in the coronal plane at 25% of the distance from the posterior border of the main body of the symphysis
Superior
Thickness and biomineralization of the medial cortical thickness of the symphysis taken in the coronal plane at 25% of the distance from the posterior border of the main body of the symphysis
Palate
Height and biomineralization of the cortical bone along the oral lamina of the palate as measured perpendicular to the curve of the palatal arch in the coronal plane at the level of P3
Frontal
Thicknesses and biomineralization of the two cortical bone layers in the mid-sagittal plane just anterior to the olfactory bulb
Parietal
Thicknesses and biomineralization of the two cortical bone layers in the mid-sagittal plane at the midpoint between the olfactory bulb and the opistocranion
Occipital
Thicknesses and biomineralization of the two cortical bone layers in the mid-sagittal plane at the opistocranion
Cranial length
Distance from the most posterior point on the neurocranium to the most anterior point on the premaxilla between the maxillary central incisors, taken in the sagittal plane
Table 2. Within-group comparisons of cortical thicknesses for the control cohort. Variable
Means Week 0
Week 24
Week 48
Masticatory region Lateral corpus 0.6600.09 0.9430.07 0.9460.04 Medial corpus 0.7610.09 1.1760.14 1.2050.15 Inferior corpus 0.6900.13 0.9810.12 1.0110.25 Lateral symphysis 0.0460.01 0.0590.005 0.0610.007 Medial symphysis 0.1590.02 0.2230.02 0.2350.02 Superior symphysis 0.0540.006 0.0960.004 0.0960.004 Hard palate 0.0720.01 0.0860.005 0.0880.007 Neurocranial region Frontal Superior 0.0450.01 0.0920.02 0.0930.02 Inferior 0.0710.01 0.1240.02 0.1250.02 Parietal Superior 0.0500.01 0.0880.01 0.0910.01 Inferior 0.0220.007 0.0700.01 0.0740.01 Occipital Superior 0.0780.02 0.2250.03 0.2250.03 Inferior 0.0310.01 0.0740.008 0.0740.006 Asterisks denote significant differences as determined by Bonferroni correction, * p < 0.0038
T-test p-value Weeks 0 vs. 24
Weeks 24 vs. 48
<0.0001* <0.0001* <0.0001* 0.0023* <0.0001* <0.0001* 0.0032*
0.933 0.674 0.746 0.453 0.163 0.822 0.394
<0.0001* <0.0001* <0.0001* <0.0001* <0.0001* <0.0001*
0.804 0.938 0.453 0.528 >0.999 0.790
Table 3. Within-group comparisons of cortical thicknesses for the over-use cohort. Variable
Means Week 0
Week 24
Week 48
Masticatory region Lateral corpus 0.6310.15 1.0720.05 1.1280.07 Medial corpus 0.7920.12 1.2410.15 1.3160.15 Inferior corpus 0.7020.07 1.1840.10 1.2330.10 Lateral symphysis 0.0520.01 0.0740.004 0.0810.005 Medial symphysis 0.1610.03 0.2290.04 0.2410.05 Superior symphysis 0.0570.003 0.1020.01 0.1080.01 Hard palate 0.0730.01 0.0980.01 0.1010.008 Neurocranial region Frontal Superior 0.0500.008 0.0890.03 0.0920.02 Inferior 0.0660.01 0.1230.01 0.1230.01 Parietal Superior 0.0540.01 0.0840.005 0.0840.005 Inferior 0.0320.01 0.0670.01 0.0670.01 Occipital Superior 0.0810.02 0.2150.03 0.2170.03 Inferior 0.0300.01 0.0760.01 0.0760.01 Asterisks denote significant differences as determined by Bonferroni correction, * p < 0.0038
T-test p-value Weeks 0 vs. 24
Weeks 24 vs. 48
<0.0001* <0.0001* <0.0001* <0.0001* 0.0004* <0.0001* <0.0001*
0.075 0.310 0.302 0.003* 0.555 0.211 0.385
0.0002* <0.0001* <0.0001* <0.0001* <0.0001* <0.0001*
0.846 0.963 0.818 >0.999 0.842 >0.999
Table 4. P-values of t-tests comparing cortical thicknesses between dietary groups. Variable Week 0 Week 12 Week 24 Week 48 Masticatory region Lateral corpus 0.461 0.092 0.0006* <0.0001* Medial corpus 0.682 0.476 0.306 0.056 Inferior corpus 0.957 0.217 0.020 0.019 Lateral symphysis 0.798 0.026 <0.0001* <0.0001* Medial symphysis 0.905 0.406 0.524 0.527 Superior symphysis 0.417 0.068 0.030 0.011 Hard palate 0.743 0.236 0.0002* 0.0035* Neurocranial region Frontal Superior 0.323 0.814 0.790 0.997 Inferior 0.337 0.949 0.944 0.854 Parietal Superior 0.541 0.284 0.356 0.393 Inferior 0.026 0.837 0.454 0.287 Occipital Superior 0.814 0.984 0.731 0.746 Inferior 0.802 0.253 0.733 0.910 Asterisks denote significant differences as determined by Bonferroni correction, * p < 0.0038
Table 5. Means standard deviations of size-adjusted cortical thicknesses, weeks 0 and 12. Variable Masticatory region Lateral corpus Medial corpus Inferior corpus Lateral symphysis Medial symphysis Superior symphysis Hard palate Neurocranial region Frontal Superior Inferior Parietal Superior Inferior Occipital Superior Inferior Cranial length
Week 0 Control
Over-use
Week 12 Control
Over-use
0.010.001 0.0120.001 0.0110.002 0.00060.0002 0.00220.0003 0.00080.0001 0.00090.0002
0.010.002 0.0120.002 0.0110.002 0.00080.0001 0.00240.0004 0.00090.00005 0.0010.0002
0.010.0008 0.0110.002 0.0110.002 0.00060.00007 0.00220.0003 0.00100.00008 0.00090.0001
0.0110.001 0.0110.002 0.0120.002 0.00080.0001 0.00240.0003 0.00110.0001 0.0010.0001
0.00070.0002 0.00110.0002 0.00080.0002 0.00030.0001 0.00120.0003 0.00050.0002 64.660.48
0.00080.0001 0.00100.0002 0.00080.0002 0.00050.0002 0.00120.0002 0.00050.0002 65.411.75
0.00090.0002 0.00130.0002 0.00090.0002 0.00060.0001 0.00180.0003 0.00070.00008 89.931.97
0.00090.0003 0.00130.0002 0.00090.0001 0.00060.0004 0.00180.0003 0.00070.0001 88.332.69
Table 6. Means standard deviations of size-adjusted cortical thicknesses, weeks 24 and 48. Variable Masticatory region Lateral corpus Medial corpus Inferior corpus Lateral symphysis Medial symphysis Superior symphysis Hard palate Neurocranial region Frontal Superior Inferior Parietal Superior Inferior Occipital Superior Inferior Cranial length
Week 24 Control 0.010.0006 0.0120.002 0.0110.001 0.00060.0001 0.00230.0002 0.00100.00005 0.00090.00006
Over-use 0.0110.0008 0.0130.002 0.0130.001 0.00080.00003 0.00240.0004 0.00110.00008 0.0010.00007
Week 48 Control 0.010.0008 0.0120.002 0.0110.002 0.00060.0001 0.00240.0002 0.00100.00005 0.00090.00008
Over-use 0.0120.001 0.0140.001 0.0130.001 0.00090.0001 0.00250.0004 0.00110.0001 0.0010.0001
0.0010.0002 0.00130.0002 0.00090.0001 0.00070.0001 0.00230.0003 0.00080.00009 94.632.44
0.00090.0003 0.00130.0002 0.00090.00005 0.00070.0001 0.00230.0003 0.00080.0001 93.742.28
0.0010.0002 0.00130.0002 0.00090.0001 0.00080.0001 0.00230.0003 0.00080.00006 96.643.01
0.0010.0003 0.00130.0001 0.00090.00005 0.00070.0001 0.00230.0003 0.00080.0001 94.903.05
Table 7. Within-group comparisons of biomineralization for the control cohort. Variable
Means Week 0
Week 24
Week 48
Masticatory region Lateral corpus 2475366 1887288 1609287 Medial corpus 2154325 1370192 1446296 Inferior corpus 3120333 2783306 2803301 Lateral symphysis 2622263 2872142 2815342 Medial symphysis 2437154 2754202 2569225 Superior symphysis 2906307 374128 3359468 Hard palate 1719140 138865 126988 Neurocranial region Frontal Superior 1960227 1426189 1449406 Inferior 1526112 1246192 1239380 Parietal Superior 2938260 2430233 1846255 Inferior 2024186 1348220 1214191 Occipital Superior 1812213 110477 104270 Inferior 1223111 819103 720114 Asterisks denote significant differences as determined by Bonferroni correction, * p < 0.0038
T-test p-value Weeks 0 vs. 24
Weeks 24 vs. 48
0.0019* <0.0001* 0.0035* 0.0030* 0.0018* <0.0001* <0.0001*
0.0739 0.5509 0.7635 0.6356 0.0863 0.0417 0.0744
0.0002* 0.0035* 0.0021* <0.0001* <0.0001* <0.0001*
0.8911 0.7316 0.0111 0.2127 0.1174 0.0951
Table 8. Within-group comparisons of biomineralization for the over-use cohort. Variable
Means Week 0
Week 24
Week 48
Masticatory region Lateral corpus 2665478 1955202 1548 200 Medial corpus 2291324 1459201 134063 Inferior corpus 3010136 2557442 2345248 Lateral symphysis 2715245 3031132 2601289 Medial symphysis 2463192 2812227 2087354 Superior symphysis 3086287 3670357 2941449 Hard palate 189490 1509156 143369 Neurocranial region Frontal Superior 2043196 1650224 1731320 Inferior 1505115 1294143 1438153 Parietal Superior 2967254 2505234 1940300 Inferior 2025225 1459188 1292199 Occipital Superior 1827190 1165201 102599 Inferior 114391 769164 74188 Asterisks denote significant differences as determined by Bonferroni correction, * p < 0.0038
T-test p-value Weeks 0 vs. 24
Weeks 24 vs. 48
0.0008* <0.0001* 0.0027* 0.0036* 0.0029* 0.0015* <0.0001*
0.0006* 0.1099 <0.0001* 0.0009* <0.0001* 0.0015* 0.1998
0.0033* 0.0025* 0.0035* <0.0001* <0.0001* <0.0001*
0.8305 0.0561 0.0016* 0.0854 0.0793 0.6612
Table 9. P-values of t-tests comparing biomineralization levels between dietary groups. Variable Week 0 Week 12 Week 24 Week 48 Masticatory region Lateral corpus 0.340 0.905 0.580 0.614 Medial corpus 0.370 0.536 0.370 0.307 Inferior corpus 0.057 0.291 0.159 0.003* Lateral symphysis 0.449 0.711 0.185 0.171 Medial symphysis 0.753 0.408 0.570 0.082 Superior symphysis 0.215 0.186 0.622 0.071 Hard palate 0.158 0.528 0.129 0.0004* Neurocranial region Frontal Superior 0.433 0.140 0.067 0.121 Inferior 0.706 0.153 0.071 0.121 Parietal Superior 0.813 0.528 0.921 0.484 Inferior 0.997 0.754 0.295 0.408 Occipital Superior 0.874 0.231 0.467 0.689 Inferior 0.121 0.134 0.492 0.669 Asterisks denote significant differences as determined by Bonferroni correction, * p < 0.0038
Table 10. Means standard deviations of biomineralization values (Hounsfield units). Variable
Week 0
Week 12
Week 24
Week 48
Control
Over-use
Control
Over-use
Control
Over-use
Control
Over-use
2665478 2291324
2249309
2265281
1887288
1955202
1609287
1548 200
Medial corpus
2475366 2154325
1666200
1598271
1370192
1459201
1446296
134063
Inferior corpus
3120333
2657625
2699250
2833288
2821356
3010136
2803301
2379287
Lateral symphysis
2622263
2715245
3061269
3104222
2872142
3031132
2815342
2601289
Medial symphysis
2437154
2463192
2645186
2557265
275420
2812227
2358257
2087354
Superior symphysis
2906307
3086287
3558174
3442194
374128
3670357
3359468
2941449
Hard palate
Masticatory region Lateral corpus
1719140
189490
1440142
147699
138865
1509156
126988
143369
Neurocranial region Frontal Superior
1960227
2043196
172670
1818142
1426189
1650224
1449406
1731320
Inferior
1526112
1505115
1272119
1364122
1246192
1416158
1239380
1471192
Superior
2938260
2967254
2634395
2799572
2530233
2505234
1846255
1940300
Inferior
2024186
2025225
1654201
1691256
1348220
1459188
1214191
1292199
1812213
1827190
1326290
1429297
110477
1165201
104270
102599
1223111
114391
1056139
942134
819103
769164
720114
74188
Parietal
Occipital Superior Inferior
S0944-2006(16)30128-3 http://dx.doi.org/doi:10.1016/j.zool.2017.03.003 ZOOL 25562
To appear in: Received date: Revised date: Accepted date:
21-10-2016 10-3-2017 10-3-2017
Please cite this article as: Franks, Erin M., Scott, Jeremiah E., McAbee, Kevin R., Scollan, Joseph P., Eastman, Meghan M., Ravosa, Matthew J., Intracranial and hierarchical perspective on dietary plasticity in mammals.Zoology http://dx.doi.org/10.1016/j.zool.2017.03.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Intracranial and hierarchical perspective on dietary plasticity in mammals
Erin M. Franksa, Jeremiah E. Scottb, Kevin R. McAbeea, Joseph P. Scollana, Meghan M. Eastmana, Matthew J. Ravosaa,c,d, *
a
Department of Biological Sciences, 100 Galvin Life Science Center, University of Notre Dame, Notre Dame, IN 46556, USA b Department of Anthropology, Southern Illinois University, 1000 Faner Drive, Carbondale, IL 62901, USA c
Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
d
Department of Anthropology, University of Notre Dame, Notre Dame, IN 46556, USA
* Corresponding author. Phone: +1-574-631-2556; Fax: +1-574-631-7413 Email address: [email protected]
Email addresses of co-authors: Erin M. Franks - [email protected] Jeremiah E. Scott – [email protected]; Kevin R. McAbee – [email protected]; Joseph P. Scollan – [email protected]; Meghan E. Eastman – [email protected]
HIGHLIGHTS
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Bony responses to diet varied in the skull of rabbits by region and level of tissue organization. Cortical bone formation was positively affected in masticatory elements.
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Biomineralization patterns did not track those of bone formation in the oral cavity.
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Non-masticatory sites did not develop increased bone formation or quality.
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Findings inform the inference of behavior and performance from skeletal structures.
ABSTRACT The effect of dietary properties on craniofacial form has been the focus of numerous functional studies, with increasingly more work dedicated to the importance of phenotypic plasticity. As bone is a dynamic tissue, morphological variation related to differential loading is well established for many masticatory structures. However, the adaptive osteogenic response of several cranial sites across multiple levels of bony organization remains to be investigated. Here, rabbits were obtained at weaning and raised for 48 weeks until adulthood in order to address the naturalistic influence of altered loading on the long-term development of masticatory and non-masticatory elements. Longitudinal data from micro-computed tomography (μCT) scans were used to test the hypothesis that variation in cortical bone formation and biomineralization in masticatory structures is linked to increased stresses during oral processing of mechanically challenging foods. It was also hypothesized that similar parameters for neurocranial structures would be minimally affected by varying loads as this area is characterized by low strains during mastication and reduced hard-tissue mechanosensitivity. Hypotheses were supported regarding bone formation for maxillomandibular and neurocranial elements, although biomineralization trends of masticatory structures did not mirror macroscale findings. Varying osteogenic responses in masticatory elements suggest that physiological adaptation, and corresponding variation in skeletal performance, may reside differentially at one level of bony architecture, thus potentially affecting accurate behavioral and in silico reconstructions. Together, these findings underscore the complexity of bone adaptation and highlight functional and developmental variation in determinants of skull form. Keywords: Masticatory loading; Dietary plasticity; Skull, functional morphology; Food mechanical properties
1. Introduction Bone is a dynamic, hierarchically organized tissue capable of sensing and responding to mechanical loads in order to produce a structure better equipped to withstand routine loads. This capability implies that bone is a plastic structure whereby altered external conditions initiate an osteogenic cascade that modulates anatomical structure. Generally, these environmentally induced, postnatal responses reflect the functional or adaptive nature of an element or system of interest (Gotthard and Nylin, 1995; Agrawal, 2001; West-Eberhard, 2003). By fine-tuning the link between form and behavior, an organism can achieve a phenotype better matched to its surroundings. Adaptive plasticity in skeletal form is related to functional adaptation, or the dynamic coordinated series of cellular, tissue, and molecular
processes of skeletal modeling and remodeling that maintain a sufficient safety factor for routine peak and cyclical loads (Bouvier and Hylander, 1981, 1996a,b; Lanyon and Rubin, 1985; Biewener, 1993). Given increasing evidence that safety factors vary across the vertebrate skeleton (Hylander et al. 1991a,b; Ravosa et al., 2000a, 2010a), it is particularly important to evaluate if patterns of functional adaptation also differ regionally. Among mammals, the plasticity of masticatory elements to altered loading conditions has been well studied in lagomorphs, rodents, carnivorans, suids, hyracoids, and primates (Beecher and Corruccini, 1981; Bouvier and Hylander, 1981, 1982, 1984, 1996a.b; Beecher et al., 1983; Bouvier, 1988; Yamada and Kimmel, 1991; Ciochon et al., 1997; He and Kiliaridis, 2003; Lieberman et al., 2004; Larsson et al., 2005; Nicholson et al., 2006; Ravosa et al., 2007, 2008a,b, 2010b, 2016; Menegaz et al., 2009, 2010; Scott et al., 2014a,b; Menegaz and Ravosa, 2017). These prior studies, though limited primarily to the feeding apparatus, have generally demonstrated a significant response to elevated loading conditions in various skeletal parameters such as external dimensions, cortical bone thickness, cross-sectional area, and tissue mineral density. Limited research has been conducted on the plasticity of sites less directly involved with oral processing, though gross proportions of the neurocranium and calvarial cross-sectional thickness have been investigated in lagomorphs (Menegaz et al., 2010; Franks et al., 2016), with results suggesting a lack of a significant plastic response. As stated above, previous studies of diet-induced plasticity were largely focused on a single masticatory element or functional region or, to a lesser degree, the hard- and soft-tissue responses that maintain the integrity of composite structures such as the mandibular symphysis or temporomandibular joint (Ravosa et al., 2007, 2016; Ravosa and Kane, 2017). As such, a comprehensive regional analysis of adaptive plasticity of hard tissues across multiple craniomandibular sites in the same specimens is lacking. This is of critical importance as within-element variation in limb responses (Hsieh et al., 2001; Hamrick et al., 2006) suggests that similar regional variation may exist in the skull. It is also becoming increasingly evident that varying skeletal parameters respond differently to a given loading pattern (Kohn et al., 2009; Wallace et al., 2009; Scott et al., 2014a; Ravosa et al., 2015b, 2016) and that the hierarchical nature of bone allows it to respond adaptively to mechanical stimuli at multiple levels. To this end, the performance of skeletal elements is dictated by a number of factors including bone quality, bone quantity, and bone distribution and there are multiple mechanisms for increasing bone strength. Thus, it is possible that a functional signal may be differentially represented at one level of organization vs. another level, potentially posing an issue for accurate behavioral and functional characterizations. For example, in a prior study of the rabbit mandibular
symphysis, it was demonstrated that an internal dimension, cortical bone thickness, exhibited a greater disparity vs. gross external dimensions (Ravosa et al., 2007, 2008a). Furthermore, the disparity in cortical bone biomineralization between rabbit dietary groups was shown to be lower than that for cortical bone thickness (Ravosa et al., 2007, 2008b). These findings are of critical importance because gross skeletal dimensions are more commonly used to track diet-related variation in morphological studies (e.g., Ravosa, 1991, 1996; Ravosa and Hogue, 2004; Wright, 2005; Friscia et al., 2007) and, thus, it is unlikely that the singular use of external dimensions will furnish the requisite evidence for meaningful paleobiological reconstructions (Ravosa et al., 2016). Additionally, it further underscores the complexity of bony organization and adaptation. Currently, there is a significant gap in our understanding of the effects of varying diets on regional and hierarchical variation in hard tissues of the developing skull and feeding apparatus. To complement prior analyses, here we report the results of a long-term diet manipulation experiment conducted using an animal model (white rabbit) that examines adaptive plasticity at various levels of bony organization at multiple bony sites across the skull vis-à-vis variation in food mechanical properties. More specifically, we probe the relationship between masticatory loading and morphological plasticity in a number of representative bony regions in order to ascertain the effect of elevated loading on the overall craniomandibular unit. Moreover, to investigate how altered loads differentially affect varying levels of bony organization, each region was assessed on a macro- and microscale to document the dynamic cascade of coordinated adaptive events. We test the hypothesis that cortical bone formation and bone quality in the developing skull adapt postnatally to increased masticatory loading and the resulting elevated stresses via coordinated osteogenic processes. Given that maxillomandibular bone strain levels are higher than elsewhere along the mammalian skull during routine feeding behaviors (e.g., Hylander et al., 1991a,b; Ravosa et al., 2006, 2010a) and that neurocranial osteoblasts exhibit reduced mechanosensitivity vs. elsewhere in the skeleton (Rawlinson et al., 1995; Ravosa et al., 2015b), it is first predicted that masticatory regions will exhibit a more pronounced response to elevated loading, indicating the presence of regional variation in diet-induced plasticity. Second, it is predicted that long-term increased masticatory stresses will result in skeletal elements of the feeding complex with more robust proportions and increased bone quality. Thus, hard tissues in the masticatory region of rabbits raised on a more challenging diet should develop increased cortical bone thicknesses and elevated biomineralization. In contrast, non-masticatory regions (i.e., the neurocranium) should display a minimal plasticity response at both levels of analysis given that these regions
experience lower strains during oral processing.
2. Materials and methods 2.1. Animal model and experimental design To evaluate the long-term plasticity of cranial elements vis-à-vis altered loading levels, 20 genetically similar male New Zealand white rabbits (Oryctolagus cuniculus) were obtained at weaning (five weeks old) from Harlan Laboratories and housed at the University of Notre Dame’s animal care facility, Freimann Life Science Center. Both institutions are USDA-licensed and AAALAC-accredited and subject to periodic inspections. Day-to-day care of the animals, including periodic health evaluations, was handled by trained veterinary staff. Additionally, all procedures were approved by the University of Notre Dame’s Institutional Animal Care and Use Committee (IACUC). The animals were raised for 48 weeks immediately following weaning, making them 53 weeks old at the conclusion of the experimental period. In white rabbits weaning typically occurs at 4–5 weeks of age, while skeletal and sexual maturity are reached at ~26 weeks of age (Masoud et al., 1986; Isaksson et al., 2010). Previous experimental work has established that the masticatory apparatus of growing rabbits is sensitive to variation in food mechanical properties (Ravosa et al., 2007, 2008a, 2010b; Menegaz et al., 2009; Scott et al., 2014a,b), exhibiting levels of phenotypic diversity between treatment groups that mirror evolutionary variation between closely related species with different diets (Ravosa et al., 2016). Importantly, white rabbits resemble other mammalian herbivores in key features of the masticatory apparatus and bone biology that make them a suitable model organism. These features include: (i) the configuration of the skull, which is characterized by a vertically deep facial skeleton, tall mandibular ramus, and a temporomandibular joint (TMJ) situated high above the occlusal plane; (ii) mandibular kinematics, with a TMJ capable of rotational and translational movements, and transverse jaw movements during mastication; (iii) intracortical bone remodeling; and (iv) well-characterized patterns of covariation among dietary properties, jaw-adductor muscle activity, and jaw-loading regimes (Weijs and de Jongh, 1977; Weijs and Dantuma, 1981; Weijs et al., 1989; Hirano et al., 2000; Langenbach and van Eijden, 2001; Crompton et al., 2006; Ravosa et al., 2010a). Weaning was chosen as the starting point for dietary manipulation because it mitigates the effect of other dietary influences that may confound subsequent comparisons between groups. Additionally, because mammals begin to adopt adult diets and chewing behaviors around the onset of weaning (Weijs and Dantuma, 1981; Herring, 1985;
Weijs et al., 1989), commencement of diet manipulation at this early developmental stage facilitates a more naturalistic experiment regarding dietary plasticity (Ravosa et al., 2007). Upon arrival, the rabbits were divided equally into two cohorts (n = 10 each). Animals in the first group, the control rabbits, were fed an ad libitum diet of Purina rabbit pellets (Purina Mills, LLC, Gray Summit, MO, USA) throughout the experiment. Animals in the second group, the over-use rabbits, were fed two hay cubes (~3.21 cm ×1.9 cm ×1.9 cm) per day in addition to ad libitum pellets for the entirety of the experimental period, modeling a mechanically challenging diet. Data on the mechanical properties of pellets and hay cubes demonstrate that the latter are more mechanically challenging in terms of the effort required for oral breakdown. Hay has a higher elastic modulus (i.e., it is stiffer: pellets, E = 29.2 MPa; wet hay, E = 277.8 MPa; dry hay, E = 3335.6 MPa) and is tougher (Ravosa et al., 2007; Menegaz et al., 2009). Stiffness characterizes an object’s resistance to elastic deformation and is quantified via the stress/strain ratio at small deformations; this is known as the elastic, or Young’s modulus (E). Toughness is an energetic property describing the work that has to be performed to propagate a crack through an item. Indeed, rabbits use approximately three times more chewing cycles per unit food mass when processing hay vs. pellets (2.95 times more chews per g), which results in longer chewing durations, averaging 3.22 times longer (Ravosa et al., 2015a). The singular importance of cyclical loading for our analyses is further underscored by the similarity in mandibular peak-strain levels during hay and pellet processing (Weijs and de Jongh, 1977). Given the adaptive role of increased cyclical loading in bone formation (Bouvier and Hylander, 1981; Biewener et al., 1986), hay consumption should thus stimulate osteogenesis and result in larger jaw proportions in the over-use dietary group in comparison with the control rabbits (Ravosa et al., 2007, 2008a, 2016; Menegaz et al., 2009; Scott et al., 2014a,b).
2.2. μCT analysis and measurements Longitudinal skull growth was tracked in vivo using (μCT) (Bioscan/Mediso X-CT, Budapest, Hungary; settings: 70 kVp and 100 μA, with a 71 μm reconstructed isotropic voxel size). Sedated rabbits were scanned at the beginning of dietary manipulation upon arrival (week 0; 5 weeks of age) and then every 2 weeks thereafter until week 24, the end of the first half of the experimental period. Week 24 corresponds to a chronological age of 29 weeks, or 6.67 months (i.e., young adulthood). At this point in the experiment, the rabbits were too large to be scanned. We therefore lack longitudinal data for the second half of the experiment. All subjects were scanned a final time at the end of the experimental period (week 48, or 53 weeks of age) following death. Reconstructed scans were analyzed using the
program PMOD version 3.3 (PMOD Technologies Ltd, Zurich, Switzerland). Scans were oriented so that the sagittal plane was parallel to the computer monitor and the occlusal plane was horizontal. Following orientation, a series of measurements were collected including cortical bone thicknesses and biomineralization at two mandibular sites, one maxillary site, and three sites along the neurocranium (Fig. 1 and Table 1). Cortical thicknesses were linearly measured, whereas biomineralization was quantified using uniform size circular regions of interest (ROIs). These were sampled at the same locations of cortical thickness as described in Table 1. Biomineralization was measured in dimensionless Hounsfield units, which range from –1000 (air) to 3000 (dense cortical bone). Maximum cranial length was recorded for size adjustment of linear measures so that findings singularly reflect the direct effects of dietary manipulation.
2.3. Statistical analysis To confirm the presence of postnatal growth and the potential for a plasticity response, we examined within-group differences over the experimental period in raw measurements using t-tests. For each dietary group, we compared mean differences between week 0 and week 24 (before skeletal maturity) and between week 24 and week 48 (after skeletal maturity). To test for between-group (i.e., dietary) effects, we compared size-adjusted measurements using t-tests. We also examined differences in raw measurements between dietary groups, but the pattern of results differed little from the analysis of size-adjusted values. Size-adjusted measurements used cranial length as a proxy for overall skull size to correct for size-related variation unrelated to the diet protocol. To simplify the analysis, between-group analyses were conducted at four timepoints: week 0 (onset of dietary manipulation), week 12, week 24 (halfway through the experimental period) and week 48 (end of dietary manipulation). To reduce the chances of obtaining a type I error due to multiple comparisons, a Bonferroni correction was employed (α/n comparisons at each time point (0.05/13 = 0.0038). Thus, the p-value threshold for significance was 0.0038.
3. Results 3.1. Cortical thickness 3.1.1. Within-group differences For all variables within both dietary groups, there were significant mean differences between week 0 and week 24 (Tables 2 and 3), indicating that cortical thicknesses increased significantly throughout the first half of the
experimental period. Between week 24 and week 48, however, there were no additional changes in cortical thicknesses.
3.1.2. Between-group differences At the onset of dietary manipulation, there were no differences between groups, indicating that cohorts were similar prior to the experimental period. Groups remained similar at week 12 with significant differences not appearing until week 24; analyses revealed significant differences in three out of seven masticatory variables (lateral corpus, lateral symphysis, hard palate) with thicknesses being greater in the over-use group for each variable (Tables 4 and 6). No significant differences were present in any of the neurocranial thicknesses. Similarly, at week 48, the end of the experimental period, analyses indicated significant differences between groups in the same three masticatory elements (Table 4), whereas the other masticatory thicknesses and neurocranial measures remained nonsignificant. Trends of the mean differences were the same as in week 24, with the over-use group having the largest thicknesses (Table 6).
3.2. Biomineralization 3.2.1 Within-group differences For all variables within both dietary groups, there were significant mean differences between week 0 and week 24 (Tables 7 and 8). Except for the three symphyseal variables (of both groups), the means were greater at week 0 than week 24, indicating a decrease in mineral density over the first half of the experimental period. Between weeks 24 and 48, signficant changes occurred though they were not as uniform (Tables 7 and 8). For the neurocranial sites within both groups, there were no significant changes between weeks 24 and 48 except for the superior cortical thickness of the parietal bone in the over-use group, which had a further decrease in mineralization (Table 8; p = 0.0016). None of the masticatory variables in the control cohort changed significantly between week 24 and week 48 (Table 7). By contrast, five of seven masticatory sites (two of three corpus sites and all three symphyseal site) in the over-use group decreased significantly over the latter half of the experimental period (Table 8). In comparing patterns across the experimental period, patterns were similar between groups in neurocranial variables but differed at masticatory sites during the second half of the experimental period (after skeletal maturity).
3.2.2. Between-group differences There were no differences between groups at weeks 0, 12, or 24, with no discernable trend as to which dietary group and/or sites exhibited greater mineralization (Tables 9 and 10). At week 48, however, significant differences were found in two masticatory elements, the inferior corpus and the hard palate (Table 9). Mineralization in the inferior corpus was less in the over-use group; this pattern was reversed in the hard palate, with the over-use group having greater mineralization (Table 10). Despite a significant difference at only one of the mandibular sites, mineralization was lower in all six mandibular sites in the over-use group when compared to the control group. This is in contrast to the maxillary site (hard palate) where the over-use cohort had greater mineralization values.
4. Discussion 4.1. Masticatory region 4.1.1. Within-group trends Rabbits in both groups experienced significant cortical thickness increases in all of the masticatory variables between the onset of dietary manipulation and halfway through the experimental period (24 weeks). These macroscale increases were accompanied by microscale decreases in tissue mineral density except at the three symphyseal sites, which experienced an increase in biomineralization. Greater mineralization at the symphysis is consistent with previous findings that demonstrated elevated mineralization in the symphysis in association with symphyseal cartilage degradation (Ravosa et al., 2007, 2008a,b, 2016). As joints are composed of soft and hard tissues that respond individually and interactively, increased biomineralization in part represents a compensatory response to cartilage degradation that maintains the overall functional integrity of such composite joint systems (Ravosa and Kane, 2017). This may explain age-/load-related fusion of the symphysis in older and over-use rabbits, as this would represent a compensatory osteogenic response to degradation of the symphyseal cartilage. An inverse relationship between thickness and mineralization suggests that rabbits are born with thinner, yet more highly mineralized cortical bone and that as growth occurs the cortical bone becomes thicker without a corresponding increase in mineralization. Interestingly, this pattern yet again highlights the role of different aspects of skeletal architecture for maintaining the biomechanical integrity of masticatory structures throughout ontogeny. It likewise underscores the novel benefits of a multifactorial perspective to understanding skeletal form and function. Halfway through the experimental period roughly corresponds to the age at which skeletal maturity is reached in
rabbits (~26 weeks) (Masoud et al., 1986). Thus, such significant changes during the first half of dietary manipulation are expected given the dual role of bone modeling, which is diminishing, and remodeling. In this regard, the second half of the experimental period (from week 24 to week 48) corresponds to post-skeletal maturity, when only remodeling is occurring in response to loading environment. Significant changes should, therefore, reflect adaptation to dietary loading. Aside from an increase in one cortical thickness (lateral symphyseal thickness in the over-use group), there were no other significant changes among masticatory sites. The fact that the lateral symphyseal thickness increased in the over-use cohort is unsurprising given the high compressive loads experienced along the lateral symphysis during mastication coupled with the elevated loads induced by a more mechanically challenging diet (see Section 4.1.2). Changes in biomineralization between weeks 24 and 48 were not similar between dietary groups, suggesting that mineralization was more plastic over the second half of the dietary manipulation period. There were no further changes in any of the masticatory variables, whereas the over-use cohort underwent more significant changes whereby five of seven masticatory sites decreased in biomineralization. Further decreases in mineralization are likely due to elevated remodeling levels resulting from increased stresses as a function of a more mechanically challenging diet (see Section 4.1.2).
4.1.2. Between-group trends Between-group comparisons indicated that growing rabbits subjected to elevated masticatory loads demonstrated varying plasticity patterns across the maxillomandibular complex. The presence and magnitude of the reaction norm was dependent on the masticatory site and level of skeletal organization. For cortical bone thicknesses, sites responded significantly by developing increased thicknesses at three of seven sites. Postweaning differences appeared by week 24 and were maintained through week 48. Though nonsignificant when applying the Bonferroni correction, thicknesses of the inferior corpus and superior symphysis were close to significance, while the medial corpus and medial symphysis were mostly nonresponsive. Within the corpus and symphysis there appears to be a graded response, with the lateral dimensions displaying the greatest plastic response, superior/inferior dimensions demonstrating an intermediate response, and the medial dimensions exhibiting a minimal response. Indeed, the lateral dimensions of the corpus and symphysis showed the greatest osteogenic response of all sites, differing by as much as 30% between treatment groups. The pattern of thinner cortical bone along the lingual aspect of the postcanine corpus has been documented across
numerous anthropoid primates (Daegling and Holtzman, 2003, and references therein) and likely corresponds to the stress distribution along the mandible. In vivo bone strain analyses at the mandibular symphysis and corpus have indicated that the anthropoid mandible experiences lateral transverse bending or “wishboning” during the power stroke of mastication. This loading regime results in compressive strains along the labial symphysis and lateral corpus coupled with tensile strains along the lingual symphysis and medial corpus (Hylander 1984, 1985; Hylander et al., 1987; Dechow and Hylander, 2000). Wishboning occurs during the end of the power stroke of mastication and is due primarily to the late peak activity of the balancing-side deep masseter and posterior temporalis muscles (Hylander et al., 1987, 2000, 2005; Hylander and Johnson, 1994). Although a wishboning strain pattern has yet to be documented in rabbits, they are characterized by a balancing-side deep-masseter muscle recruitment pattern similar to that of anthropoids (Weijs and Dantuma, 1981; Weijs et al., 1989). This suggests that the pattern of forces experienced across the rabbit mandible is similar and may serve as an explanation for the cortical bone distribution noted herein. Though specific to masticatory structures, these results underscore the presence of among- and within-element variation in the distribution of cortical bone, supporting previous findings that suggested the presence of varying functional responses among masticatory elements in the same organisms (Scott et al., 2014a; Ravosa et al., 2015b, 2016). These results likewise correspond to numerous studies that have shown variability in the response of limb bones to mechanical stimuli, not only among skeletal elements but also among regions within the same bone (Jaworski et al., 1980; Mosley et al., 1997; Iwamoto et al., 1999; Hsieh et al., 2001; Hamrick et al., 2006). Taken together, this implies that functional adaptations are site-specific and that there is considerable heterogeneity in osteogenic responses across the skeleton (Rawlinson et al., 1995; Ravosa et al., 2010a, 2015b, 2016). Interestingly, biomineralization trends did not track the results seen in cortical bone thicknesses. No differences between groups were observed until the end of the experimental period, with differences present at only two of seven maxillomandibular sites (inferior corpus, hard palate). Though differences were found at these two sites at the same time point as in cortical bone thickness, the overall trends were different. At the inferior corpus, tissue mineral density was significantly decreased in the over-use group though it increased significantly at the hard palate. Despite a lack of significance in biomineralization at the five other masticatory sites, biomineralization was decreased in the over-use group at all sites, contradicting the hypothesis that increased cortical thickness would be coupled with increased mineralization throughout the experimental period.
This hierarchical disparity in plasticity responses of masticatory elements could be due to the interplay between two different mechanisms of bone formation. Bone modeling occurs largely from birth to skeletal maturity and is responsible for increases in skeletal mass and changes in form. In contrast, remodeling occurs during later stages of skeletal development (Goldman et al., 2009). Only at a year old, the final time point for the present study, would mineralization values reflect a more singular role of bone remodeling in the maintenance of skeletal integrity. As previously mentioned, bone is a dynamic tissue that continuously undergoes adaptive remodeling (i.e., osteonal resorption and formation) to meet the requirements of its functional environment (Lanyon and Rubin, 1985). Mechanical loading of bone is caused predominantly by muscle contractions and, thus, muscles provide an important stimulus for bone remodeling by inducing strains in the skeletal system (Turner, 2000). Therefore, the remodeling rate of bone is related to the magnitude and frequency of mechanical loading and the resulting strains in the tissue (Lisková and Hert, 1971; Turner, 1998). While this may suggest that bone subjected to elevated loading will adapt by increasing both bone quantity and quality to better withstand forces, it has been demonstrated that these parameters need not track one another. Remodeling rate is a major determinant of the degree of mineralization of bone, with a higher remodeling rate due to increased loads resulting in decreased time available for secondary mineralization and, in turn, bone with a lower degree of mineralization (Lanyon and Rubin, 1985; Reid and Boyde, 1987; Meunier and Boivin, 1997; Cullen et al., 2001; Boivin et al., 2009). In other words, more heavily loaded bone in organisms that have stopped growing has a higher remodeling rate, which reduces its average lifespan and consequently produces less mineralized tissue (as a whole) than less-stressed bone not undergoing such rapid turnover. In the context of diet-induced plasticity of the masticatory apparatus, a previous study compared the degree of mineralization in cortical bone of multiple mandibular sites in rabbits fed soft vs. control pellets (Grünheid et al., 2011). Soft pellets required significantly reduced peak loadings (10 N/cm2) to break the pellet in comparison to control pellets (120 N/cm2). Unlike the present study, however, that research found no significant differences between groups at any of the sites examined. This discrepancy can possibly be explained by the fact that Grünheid et al. (2011) only raised rabbits until 20 weeks of age, which may not have been a sufficient amount of time to generate differences between groups. Indeed, no differences were found in the present study until 48 weeks. However, the findings of Grünheid et al. (2011) also differ from the results of Ravosa et al. (2007) where rabbits were grown to the same age (20 weeks) and did demonstrate between-group differences in biomineralization along the symphysis
and condyle. Between-group differences present in Ravosa et al. (2007), but not Grünheid et al. (2011), may be the result of different dietary regimens. Ravosa et al. (2007) employed an over-use vs. under-use diet where the underuse diet consisted of ground pellets and over-use was standard pellets supplemented daily with 2-cm hay cubes. This regimen imposes a greater disparity in mechanical loads than a soft vs. control diet. Indeed, in Grünheid et al. (2011) a slighter disparity in food properties may not have imposed a great enough difference in masticatory functional loads to affect the remodeling rate and resulting degree of mineralization. Furthermore, these findings differ from the results of other studies, which showed that feeding rats a less mechanically challenging diet leads to a reduction in the rate of bone apposition (Yamada and Kimmel, 1991), resulting in lower bone mass (Bresin et al., 1999) and alveolar bone density (Mavropoulos et al., 2004, 2005), as well as a higher degree of mineralization in the mandible (Tanaka et al., 2007). Thus, the results of the present study are in agreement with the majority of past studies; increased functional loads generate a higher rate of bone remodeling and, ultimately, a less mineralized structure.
4.2. Non-masticatory region 4.2.1. Within-group trends Like the cortical thicknesses of the masticatory region, the thicknesses of all neurocranial sites in both groups increased across the first half of the experimental period. These increases were also accompanied by decreases in biomineralization. Again, this inverse pattern suggests that the neurocranium maintained mechanical integrity across ontogeny. It appears that at younger ages tissue strength is achieved through thinner, more mineralized bone; throughout the growth period, the bone becomes thicker without a corresponding increase in mineralization. Given the disparity in strain levels between masticatory and non-masticatory regions, shared biomineralization decreases and cortical bone increases may highlight a general postnatal pattern of bone formation in the skull (and perhaps the skeleton). Aside from one neurocranial site, there were no additional changes in any of the thicknesses between weeks 24 and 48. The exception was a further decrease in the mineralization of the superior thickness of the parietal bone, which occurred in the over-use group during the latter half of the study. Overall, these results suggest that most mineralization values become stable around skeletal maturity (~26 weeks). Similar patterning of cortical bone parameters within groups lends credence to the notion that growth of the neurocranium may be more genetically canalized (see more in Section 4.2.2).
4.2.2. Between-group trends There were no differences between groups in cortical thickness or biomineralization at any of the neurocranial sites throughout the experimental period. Together, these results indicate that the neurocranial region did not respond to heightened masticatory loads. This finding can potentially be explained by multiple factors. First, there is a considerable body of in vivo experimental work that has established the presence of a strain gradient in the skull of primates and other tetrapods. These examinations have unequivocally demonstrated that peak-strain magnitudes are highest along the maxilla and mandible, and markedly lower along the neurocranium and circumorbital region (Buckland-Wright, 1978; Hylander et al., 1991a,b; Ross and Hylander, 1996; Hylander and Johnson, 1997; Herring and Teng, 2000; Ravosa et al., 2000a, b, c, 2006, 2010a; Thomason et al., 2001; Lieberman et al., 2004; Ross and Metzger, 2004). In this model, relatively high strains associated with the feeding complex are due to its direct involvement in resisting loads during biting and chewing. In contrast, low strains in the cranial vault suggest that it is overbuilt for resisting chewing stresses. Such low strains could perhaps be due to a larger distance from the application of bite, muscle, and reaction forces; however, this would be contrary to models regarding optimal strain levels and similar safety factors across skeletal elements (Lanyon and Rubin, 1985; Biewener et al., 1986). Alternatively, bone formation in the neurocranium and circumorbital region may be less labile and, thus, under greater intrinsic genetic control to ensure that sufficient bone exists to resist rare, traumatic loads (Hylander and Johnson, 1997; Ravosa et al., 2000a,b,c, 2010a). This implies the presence of intracranial variation in tissue sensitivity to mechanical stimuli and related osteogenic responses. Such site-specific phenomena characterize the disparate responses of calvarial and ulnar bone cells to similar loads (Rawlinson et al., 1995). This suggests that the reduced osteogenic response of calvarial bone cells may not be associated with extrinsic factors such as lower stresses due to a more distant position from the oral cavity. Rather, it is possible that osteoblast activity further varies according to craniomandibular region (Quarto et al., 2010; Li et al., 2013; Ravosa et al., 2015b, 2016). While it may be reasonable to infer that negligible levels of diet-induced plasticity in the neurocranium are due to the presence of a strain gradient and reduced hard-tissue mechanosensitivity, several lines of evidence provide added support for the latter interpretation. A low strain environment should, according to the functional adaptation paradigm (Lanyon and Rubin, 1985), lead to a decrease in the amount of bone, yet this does not occur. Moreover, avian ulnae subjected to low-level dynamic strains have demonstrated bone formation (Rubin and Lanyon, 1984), a
result not witnessed in our analyses despite the presence of dynamic high-frequency masticatory loads (Ravosa et al., 2015a). Thus, regardless of whether one accepts the claim that the neurocranium is overbuilt to resist traumatic loads, the lack of plasticity in this region coupled with lower mechanosensitivity of calvarial osteoblasts strongly implicates the presence of a mechanism that maintains skeletal mass and structural integrity in the neurocranium independent of the osteogenic stimuli associated with feeding.
4.3. Dietary plasticity and bone formation While cortical bone thicknesseses and biomineralization of masticatory regions revealed disparate adaptive signals, a related paper (Franks et al., 2016) investigated diet-induced plasticity in the zygomatic arch, an intervening structure connecting the neurocranium to the feeding apparatus. It was demonstrated that elevated masticatory loading did not yield differences in cortical bone thickness, though biomineralization was positively affected (Franks et al., 2016), an arrangement not seen in the present study. Taken together, the findings of the two studies reveal that there are multiple ways of increasing bone strength and, ultimately, functional performance. In the present study, differences in bone quality were always accompanied by differences in bone quantity, suggesting the coupling of these two parameters. The reverse was not true, however; macroscale differences in bone quantity were not always accompanied by a microscale difference in bone quality. As evidenced in related work on the plasticity of the zygomatic arch in the same experimental subjects (Franks et al., 2016), changes in bone quality are possible without corresponding changes in bone quantity. Together, these findings highlight the various ways of maintaining the structural integrity of bony elements that are subjected to elevated stresses and underscore the complexity of inferring form from function as bone quantity and quality can be positively related, inversely related (Kohn et al., 2009; Wallace et al., 2009, 2010), or potentially unrelated. More broadly, observations from the present study have considerable implications for interpreting the behavior and evolutionary history of fossil (and living) mammals. As hard tissues are highly mineralized, the remains of bony elements represent the primary means by which we infer the paleobiology of extinct organism, with skeletal anatomy used to infer a given behavior. Analysis of cranial remains typically focuses on external gross dimensions (e.g., lengths, widths) of phenotypic variation and less frequently employs a hierarchical or regional perspective that incorporates information on variation in internal anatomy from multiple sites. The present findings suggest that standard, morphology-based analyses of single cranial (and postcranial) variables likely hinder accurate
paleobiological inference as the adaptive signal can vary hierarchically within an element and across skeletal sites, conveying different signals for behavioral reconstructions using fossil remains (Ravosa et al., 2016). The present study thus echoes previous notions that certain cranial sites (e.g., the neurocranium) may be under greater genetic control, with the phenotype expression driven primarily by selective forces (Hylander and Johnson, 1997). In contrast, other elements, such as the mandible, may be more labile, with morphological variation resulting from a combination of adaptive plasticity and selection. This poses a challenge when attempting to interpret the functional signal of a given feature, as one must partition how much of an observed phenotype is due to the individual loading history of a specific organism. This is also of relevance to the process of evolution in that altered reaction norms change the heritable variation upon which natural selection acts. Because plasticity affects phenotypic covariance structure, this can alter the manner in which hereditary variation and selection interact to generate evolutionary change (Gupta and Lewontin, 1982; Cheverud et al., 1983; Stearns, 1989). This means that the influence of diet-induced plasticity on cranial variance and covariance patterns will, in turn, modulate the phenotypic response to natural selection. Findings from the present study are also relevant to in silico modeling, such as the use of finite element analysis (Richmond et al., 2005; Strait et al., 2005, 2009; Rayfield, 2007; Gill et al., 2014). This technique has been increasingly employed in functional analyses of fossil skeletal remains and requires the incorporation of realistic tissue properties to ensure biologically meaningful results. Consequently, our results highlight the potential limitations of computer modeling as currently practiced by illustrating the hierarchical and site-specific variation of bone material properties present in a morphologically complex structure such as the skull (see also Thompson et al., 2017). Moreover, if tissue mineral density indeed tracks bone mechanical properties, the presence of intracranial variation in biomineralization (within treatment groups) implies the presence of considerable anisotropy in hardtissue properties. That bone is typically assumed to be isotropic in most computer models of extant and extinct organisms (e.g., Richmond et al., 2005) suggests that further functional accuracy will be attained when site-specific data regarding bone anisotropy are fully incorporated (Ravosa et al., 2016). As bony structures that appear similar based on external form can have a highly disparate internal organization (e.g., anterior pillar in australopiths – Villmoare and Kimbel, 2011), this, too, may undermine the accuracy of an in silico model.
5. Conclusion
In summary, this study presents an examination of the effects of varying diets on regional and hierarchical variation in hard tissues of the developing skull and feeding apparatus. The data presented here illustrate diet-related differences in both cortical bone thicknesses and the degree of mineralization, with differences found between masticatory and non-masticatory regions as well as within regions. However, the functional signals at these two levels of bony organization did not mirror one another and initially seemed contradictory. While increased cortical bone thicknesses were expected at maxillomandibular sites, decreased biomineralization was not predicted. This discrepancy can ultimately be explained by considering the process of adaptive remodeling as it relates to loading environment, whereby greater mechanical loads induce a higher rate of remodeling, resulting in less mineralized tissue. Crucially, this pattern further speaks to the hierarchical organization of bone and the potential for variation in the functional signal depending on the level of analysis. Additionally, it highlights the importance of incorporating multiple architectural parameters into the investigation of morphological variation and functional interpretation, while cautioning against the use of a single variable. For example, if the degree of mineralization were used in the present study as the sole variable for assessing the form and function of these cranial sites, it would yield a much different assessment than when used in combination with cortical thickness. Coupling parameters that describe both the quantity and quality of the bone allows for a more thorough understanding of adaptive plasticity across the skull (and postcranium). Moreover, it allows for the development of a more comprehensive analysis of form–function relationships that may reveal novel patterns of bony organization and biomechanical performance throughout ontogeny.
Acknowledgments Olga Panagiotopoulou and Pepe Iriarte-Diaz kindly invited us to contribute to the Special Issue on “Determinants of mammalian feeding system design”. We also thank the Notre Dame Integrated Imaging Facility and the staff of the Freimann Life Science Center. Funding was obtained from the Wenner-Gren and Leakey Foundations to E.M.F. and the NSF to M.J.R. (BCS-1029149/1214767).
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Fig. 1. μCT coronal cross-sections depicting sites of interest on a rabbit skull.
Table 1. Description of collected measurements on rabbit skulls. Variable Corpus
Location Lateral
Medial
Symphysis
Description Thickness and biomineralization of the lateral cortical bone of the corpus, taken approximately midway between the superior and inferior borders of the corpus at P4 in the coronal plane Thickness and biomineralization of the medial cortical bone of the corpus, taken approximately midway between the superior and inferior border of the corpus at P4 in the coronal plane
Inferior
Thickness and biomineralization of the inferior cortical bone of the corpus taken at P4 in the coronal plane
Lateral
Thickness and biomineralization of the lateral cortical thickness of the symphysis taken in the coronal plane at 25% of the distance from the posterior border of the main body of the symphysis
Medial
Thickness and biomineralization of the medial cortical thickness of the symphysis taken in the coronal plane at 25% of the distance from the posterior border of the main body of the symphysis
Superior
Thickness and biomineralization of the medial cortical thickness of the symphysis taken in the coronal plane at 25% of the distance from the posterior border of the main body of the symphysis
Palate
Height and biomineralization of the cortical bone along the oral lamina of the palate as measured perpendicular to the curve of the palatal arch in the coronal plane at the level of P3
Frontal
Thicknesses and biomineralization of the two cortical bone layers in the mid-sagittal plane just anterior to the olfactory bulb
Parietal
Thicknesses and biomineralization of the two cortical bone layers in the mid-sagittal plane at the midpoint between the olfactory bulb and the opistocranion
Occipital
Thicknesses and biomineralization of the two cortical bone layers in the mid-sagittal plane at the opistocranion
Cranial length
Distance from the most posterior point on the neurocranium to the most anterior point on the premaxilla between the maxillary central incisors, taken in the sagittal plane
Table 2. Within-group comparisons of cortical thicknesses for the control cohort. Variable
Means Week 0
Week 24
Week 48
Masticatory region Lateral corpus 0.6600.09 0.9430.07 0.9460.04 Medial corpus 0.7610.09 1.1760.14 1.2050.15 Inferior corpus 0.6900.13 0.9810.12 1.0110.25 Lateral symphysis 0.0460.01 0.0590.005 0.0610.007 Medial symphysis 0.1590.02 0.2230.02 0.2350.02 Superior symphysis 0.0540.006 0.0960.004 0.0960.004 Hard palate 0.0720.01 0.0860.005 0.0880.007 Neurocranial region Frontal Superior 0.0450.01 0.0920.02 0.0930.02 Inferior 0.0710.01 0.1240.02 0.1250.02 Parietal Superior 0.0500.01 0.0880.01 0.0910.01 Inferior 0.0220.007 0.0700.01 0.0740.01 Occipital Superior 0.0780.02 0.2250.03 0.2250.03 Inferior 0.0310.01 0.0740.008 0.0740.006 Asterisks denote significant differences as determined by Bonferroni correction, * p < 0.0038
T-test p-value Weeks 0 vs. 24
Weeks 24 vs. 48
<0.0001* <0.0001* <0.0001* 0.0023* <0.0001* <0.0001* 0.0032*
0.933 0.674 0.746 0.453 0.163 0.822 0.394
<0.0001* <0.0001* <0.0001* <0.0001* <0.0001* <0.0001*
0.804 0.938 0.453 0.528 >0.999 0.790
Table 3. Within-group comparisons of cortical thicknesses for the over-use cohort. Variable
Means Week 0
Week 24
Week 48
Masticatory region Lateral corpus 0.6310.15 1.0720.05 1.1280.07 Medial corpus 0.7920.12 1.2410.15 1.3160.15 Inferior corpus 0.7020.07 1.1840.10 1.2330.10 Lateral symphysis 0.0520.01 0.0740.004 0.0810.005 Medial symphysis 0.1610.03 0.2290.04 0.2410.05 Superior symphysis 0.0570.003 0.1020.01 0.1080.01 Hard palate 0.0730.01 0.0980.01 0.1010.008 Neurocranial region Frontal Superior 0.0500.008 0.0890.03 0.0920.02 Inferior 0.0660.01 0.1230.01 0.1230.01 Parietal Superior 0.0540.01 0.0840.005 0.0840.005 Inferior 0.0320.01 0.0670.01 0.0670.01 Occipital Superior 0.0810.02 0.2150.03 0.2170.03 Inferior 0.0300.01 0.0760.01 0.0760.01 Asterisks denote significant differences as determined by Bonferroni correction, * p < 0.0038
T-test p-value Weeks 0 vs. 24
Weeks 24 vs. 48
<0.0001* <0.0001* <0.0001* <0.0001* 0.0004* <0.0001* <0.0001*
0.075 0.310 0.302 0.003* 0.555 0.211 0.385
0.0002* <0.0001* <0.0001* <0.0001* <0.0001* <0.0001*
0.846 0.963 0.818 >0.999 0.842 >0.999
Table 4. P-values of t-tests comparing cortical thicknesses between dietary groups. Variable Week 0 Week 12 Week 24 Week 48 Masticatory region Lateral corpus 0.461 0.092 0.0006* <0.0001* Medial corpus 0.682 0.476 0.306 0.056 Inferior corpus 0.957 0.217 0.020 0.019 Lateral symphysis 0.798 0.026 <0.0001* <0.0001* Medial symphysis 0.905 0.406 0.524 0.527 Superior symphysis 0.417 0.068 0.030 0.011 Hard palate 0.743 0.236 0.0002* 0.0035* Neurocranial region Frontal Superior 0.323 0.814 0.790 0.997 Inferior 0.337 0.949 0.944 0.854 Parietal Superior 0.541 0.284 0.356 0.393 Inferior 0.026 0.837 0.454 0.287 Occipital Superior 0.814 0.984 0.731 0.746 Inferior 0.802 0.253 0.733 0.910 Asterisks denote significant differences as determined by Bonferroni correction, * p < 0.0038
Table 5. Means standard deviations of size-adjusted cortical thicknesses, weeks 0 and 12. Variable Masticatory region Lateral corpus Medial corpus Inferior corpus Lateral symphysis Medial symphysis Superior symphysis Hard palate Neurocranial region Frontal Superior Inferior Parietal Superior Inferior Occipital Superior Inferior Cranial length
Week 0 Control
Over-use
Week 12 Control
Over-use
0.010.001 0.0120.001 0.0110.002 0.00060.0002 0.00220.0003 0.00080.0001 0.00090.0002
0.010.002 0.0120.002 0.0110.002 0.00080.0001 0.00240.0004 0.00090.00005 0.0010.0002
0.010.0008 0.0110.002 0.0110.002 0.00060.00007 0.00220.0003 0.00100.00008 0.00090.0001
0.0110.001 0.0110.002 0.0120.002 0.00080.0001 0.00240.0003 0.00110.0001 0.0010.0001
0.00070.0002 0.00110.0002 0.00080.0002 0.00030.0001 0.00120.0003 0.00050.0002 64.660.48
0.00080.0001 0.00100.0002 0.00080.0002 0.00050.0002 0.00120.0002 0.00050.0002 65.411.75
0.00090.0002 0.00130.0002 0.00090.0002 0.00060.0001 0.00180.0003 0.00070.00008 89.931.97
0.00090.0003 0.00130.0002 0.00090.0001 0.00060.0004 0.00180.0003 0.00070.0001 88.332.69
Table 6. Means standard deviations of size-adjusted cortical thicknesses, weeks 24 and 48. Variable Masticatory region Lateral corpus Medial corpus Inferior corpus Lateral symphysis Medial symphysis Superior symphysis Hard palate Neurocranial region Frontal Superior Inferior Parietal Superior Inferior Occipital Superior Inferior Cranial length
Week 24 Control 0.010.0006 0.0120.002 0.0110.001 0.00060.0001 0.00230.0002 0.00100.00005 0.00090.00006
Over-use 0.0110.0008 0.0130.002 0.0130.001 0.00080.00003 0.00240.0004 0.00110.00008 0.0010.00007
Week 48 Control 0.010.0008 0.0120.002 0.0110.002 0.00060.0001 0.00240.0002 0.00100.00005 0.00090.00008
Over-use 0.0120.001 0.0140.001 0.0130.001 0.00090.0001 0.00250.0004 0.00110.0001 0.0010.0001
0.0010.0002 0.00130.0002 0.00090.0001 0.00070.0001 0.00230.0003 0.00080.00009 94.632.44
0.00090.0003 0.00130.0002 0.00090.00005 0.00070.0001 0.00230.0003 0.00080.0001 93.742.28
0.0010.0002 0.00130.0002 0.00090.0001 0.00080.0001 0.00230.0003 0.00080.00006 96.643.01
0.0010.0003 0.00130.0001 0.00090.00005 0.00070.0001 0.00230.0003 0.00080.0001 94.903.05
Table 7. Within-group comparisons of biomineralization for the control cohort. Variable
Means Week 0
Week 24
Week 48
Masticatory region Lateral corpus 2475366 1887288 1609287 Medial corpus 2154325 1370192 1446296 Inferior corpus 3120333 2783306 2803301 Lateral symphysis 2622263 2872142 2815342 Medial symphysis 2437154 2754202 2569225 Superior symphysis 2906307 374128 3359468 Hard palate 1719140 138865 126988 Neurocranial region Frontal Superior 1960227 1426189 1449406 Inferior 1526112 1246192 1239380 Parietal Superior 2938260 2430233 1846255 Inferior 2024186 1348220 1214191 Occipital Superior 1812213 110477 104270 Inferior 1223111 819103 720114 Asterisks denote significant differences as determined by Bonferroni correction, * p < 0.0038
T-test p-value Weeks 0 vs. 24
Weeks 24 vs. 48
0.0019* <0.0001* 0.0035* 0.0030* 0.0018* <0.0001* <0.0001*
0.0739 0.5509 0.7635 0.6356 0.0863 0.0417 0.0744
0.0002* 0.0035* 0.0021* <0.0001* <0.0001* <0.0001*
0.8911 0.7316 0.0111 0.2127 0.1174 0.0951
Table 8. Within-group comparisons of biomineralization for the over-use cohort. Variable
Means Week 0
Week 24
Week 48
Masticatory region Lateral corpus 2665478 1955202 1548 200 Medial corpus 2291324 1459201 134063 Inferior corpus 3010136 2557442 2345248 Lateral symphysis 2715245 3031132 2601289 Medial symphysis 2463192 2812227 2087354 Superior symphysis 3086287 3670357 2941449 Hard palate 189490 1509156 143369 Neurocranial region Frontal Superior 2043196 1650224 1731320 Inferior 1505115 1294143 1438153 Parietal Superior 2967254 2505234 1940300 Inferior 2025225 1459188 1292199 Occipital Superior 1827190 1165201 102599 Inferior 114391 769164 74188 Asterisks denote significant differences as determined by Bonferroni correction, * p < 0.0038
T-test p-value Weeks 0 vs. 24
Weeks 24 vs. 48
0.0008* <0.0001* 0.0027* 0.0036* 0.0029* 0.0015* <0.0001*
0.0006* 0.1099 <0.0001* 0.0009* <0.0001* 0.0015* 0.1998
0.0033* 0.0025* 0.0035* <0.0001* <0.0001* <0.0001*
0.8305 0.0561 0.0016* 0.0854 0.0793 0.6612
Table 9. P-values of t-tests comparing biomineralization levels between dietary groups. Variable Week 0 Week 12 Week 24 Week 48 Masticatory region Lateral corpus 0.340 0.905 0.580 0.614 Medial corpus 0.370 0.536 0.370 0.307 Inferior corpus 0.057 0.291 0.159 0.003* Lateral symphysis 0.449 0.711 0.185 0.171 Medial symphysis 0.753 0.408 0.570 0.082 Superior symphysis 0.215 0.186 0.622 0.071 Hard palate 0.158 0.528 0.129 0.0004* Neurocranial region Frontal Superior 0.433 0.140 0.067 0.121 Inferior 0.706 0.153 0.071 0.121 Parietal Superior 0.813 0.528 0.921 0.484 Inferior 0.997 0.754 0.295 0.408 Occipital Superior 0.874 0.231 0.467 0.689 Inferior 0.121 0.134 0.492 0.669 Asterisks denote significant differences as determined by Bonferroni correction, * p < 0.0038
Table 10. Means standard deviations of biomineralization values (Hounsfield units). Variable
Week 0
Week 12
Week 24
Week 48
Control
Over-use
Control
Over-use
Control
Over-use
Control
Over-use
2665478 2291324
2249309
2265281
1887288
1955202
1609287
1548 200
Medial corpus
2475366 2154325
1666200
1598271
1370192
1459201
1446296
134063
Inferior corpus
3120333
2657625
2699250
2833288
2821356
3010136
2803301
2379287
Lateral symphysis
2622263
2715245
3061269
3104222
2872142
3031132
2815342
2601289
Medial symphysis
2437154
2463192
2645186
2557265
275420
2812227
2358257
2087354
Superior symphysis
2906307
3086287
3558174
3442194
374128
3670357
3359468
2941449
Hard palate
Masticatory region Lateral corpus
1719140
189490
1440142
147699
138865
1509156
126988
143369
Neurocranial region Frontal Superior
1960227
2043196
172670
1818142
1426189
1650224
1449406
1731320
Inferior
1526112
1505115
1272119
1364122
1246192
1416158
1239380
1471192
Superior
2938260
2967254
2634395
2799572
2530233
2505234
1846255
1940300
Inferior
2024186
2025225
1654201
1691256
1348220
1459188
1214191
1292199
1812213
1827190
1326290
1429297
110477
1165201
104270
102599
1223111
114391
1056139
942134
819103
769164
720114
74188
Parietal
Occipital Superior Inferior