Epigenetic analysis of Paget’s disease of bone identifies differentially methylated loci that predict disease status

  1. Ilhame Diboun
  2. Sachin Wani
  3. Stuart H Ralston
  4. Omar ME Albagha

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    Evaluation Summary:

    Paget disease of bone (PDB) results in focal areas of disorganized bone, leading to bone deformities and fragility. There is substantial interest in finding circulating biomarkers that might be of use for possible diagnostic applications and towards this end, these authors identified novel DNA methylation patterns in peripheral blood mononuclear cells that are able to differentiate PDB cases from controls with a high level of accuracy. This prediction model has functional relevance as these candidate methylation sites and regions are associated with osteological and immunologic processes and in the longer term, has future clinical potential.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

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Abstract

Paget’s disease of bone (PDB) is characterized by focal increases in disorganized bone remodeling. This study aims to characterize PDB-associated changes in DNA methylation profiles in patients’ blood. Meta-analysis of data from the discovery and cross-validation set, each comprising 116 PDB cases and 130 controls, revealed significant differences in DNA methylation at 14 CpG sites, 4 CpG islands, and 6 gene-body regions. These loci, including two characterized as functional through expression quantitative trait-methylation analysis, were associated with functions related to osteoclast differentiation, mechanical loading, immune function, and viral infection. A multivariate classifier based on discovery samples was found to discriminate PDB cases and controls from the cross-validation with a sensitivity of 0.84, specificity of 0.81, and an area under curve of 92.8%. In conclusion, this study has shown for the first time that epigenetic factors contribute to the pathogenesis of PDB and may offer diagnostic markers for prediction of the disease.

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  1. Version published to 10.7554/elife.65715 on eLife
  2. eLife

    Author Response:

    Reviewer #1 (Public Review):

    The study by Diboun et al. aims to investigate methylation profiles in Paget's disease of bone patients. Many of the genes identified near areas of differentially methylated sites were known to be involved in osteoclast differentiation, viral infection and mechanical loading. These gene pathways are known to play a role in the pathogenesis of PDB. The strength of this study is that it is the first study to look at changes in methylation profiles in Paget's disease of bone patients. Additionally, the genes identified as having differentially methylated sites suggest that environmental factors such as host immune responses may be altered and play a role in the pathogenesis of PBD. The main weakness of this study is that the cells that were analyzed for changes in methylation sites were not osteoclasts the cells of interest in PBD. While many of the genes identified have been shown to play a role in regulation of the skeletal system, results should be interpreted with caution until they are validated in bone tissue.

    We thank the reviewers and the editors for this thoughtful comment. Ebrahimi et al (EPIGENETICS; 2021, 16(1): 92–105) investigated correlation in methylation profiles between blood and bone tissue in 12 subjects using Illumina MethylationEPIC BeadChip array. Bone samples were taken from the exposed proximal femur after removal of the femoral head from osteoarthritis patients. After quality control, Ebrahimi et al focused the correlation analysis on 64,349 probes that fit their analysis criteria (to define the most highly correlated positions), of which 30,607 sites showed significant (FDR < 0.05) high correlation (r2 > 0.74) between bone and blood.

    Additional filter was applied to these sites to include those with at least 80% similar methylation profile between bone and blood (n = 28,549) which were reported as supplementary table in their paper. We assessed if CpG sites annotated to genes identified from our DMS and DMR analyses (Table 2 and 3) showed high correlation between bone and blood as reported by Ebrahimi et al. Results showed that CpGs annotated to 8 out of the 14 genes from our DMS analysis were among the highly correlated sites between blood and bone (r2 > 0.74; FDR <0.05; Supplementary File 6). For DMRs, out of the 10 genes reported in our study (Table 3), 6 had at least one CpG with high correlation between blood and bone (Supplementary File 6). It is important to note that, in the study by Ebrahimi et al, only 64,349 CpG sites were tested for correlation, owing to the stringent criteria adopted by the authors to identify the list of highly concordant sites. Therefore, our DMS/DMR sites that did not feature in the list are not necessarily uncorrelated. Unfortunately, these sites cannot be investigated further since Ebrahimi et al did not make their entire dataset available in public domain. To address this point, A table has been added to the manuscript (Supplementary File 6) listing the sites with high correlation and the text has been modified to include and discuss these results.

    Reviewer #2 (Public Review):

    This unique study has shown that epigenetic (therefore, potentially environment-driven) factors contribute to the pathogenesis of Paget's Disease of Bone (PDB). Although PDB is not very rare condition, its early diagnosis is problematic. The bone tissue is not easily accessible, thus many cases are not diagnosed till later in life. Thus, having diagnostic markers measured in blood, normalized to cell type count, might be of use for possible diagnostic applications.

    The PRISM trial's sample, comprising 232 cases and 260 controls from UK, was divided in two - discovery and replication sets - based on power calculations for EWAS. Meta-analysis of data from the discovery and replication sets revealed significant differences in DNA methylation. Among gene-body regions/loci, many associated with functions related to osteoclast differentiation, mechanical loading, immune function, etc. two loci were suggested as functional through expression quantitative trait methylation (eQTM) analysis. Further, there was some value in assessing the risk of developing PDB. The AUC of 82.5%, based on the 95 discriminatory sites from the "best subset" analysis, is promising for clinical applicability. If confirmed in independent samples and further studies, chromosomal loci found in this study may offer diagnostic markers for prediction of the disease.

    We would like to draw the reviewer’s attention to the fact that the original cohort comprised of 232 PDB cases and 260 controls (that is 116 cases and 130 controls in each of the discovery and cross validation set). The abstract has been slightly modified to make the text clearer.

    Reviewer #3 (Public Review):

    Diboun et al used a case-control study design to identify DNA methylation sites and regions that differ between individuals with Paget's Disease of Bone (PDB) and controls. Cases were identified from an ongoing PDB clinical trial. Spouses of cases were used as controls. Candidate methylation sites were identified in a discovery set and then tested in a validation set to confirm association with PDB. Meta-analysis was used to combine effects from the discovery and validation sets. A machine learning approach was then used to prioritize candidates and build a prediction model capable of differentiating PDB cases from controls. The model was associated with high level of accuracy (AUC >0.90) in the discovery and validation sets.

    A major strength of the study is the collection of a large population of individuals with a rare bone disease. Epigenetic features are appealing for building prediction models as they may represent interplay between genetics and environment. Using this approach, the authors built a prediction model with a high level of accuracy. The results advance our understanding of the etiology of PDB.

    Overall, the primary conclusions are generally well supported. However, there are several aspects of the paper that will require additional clarification.

    I commend the authors for using a split sample cross validation approach to maximize experimental rigor. However, this approach is distinct from a true external replication. Given that the 'training' and the 'test' sets come from the same overall population, we expect the 'replication' results to be optimistic relative to results from a true, external replication population. Given the absence of a suitable external replication population due the unique nature of the disease, this limitation is acceptable. However, I expect the authors to discuss the potential limitations of this approach in their discussion section and I encourage the authors to refer to the 'replication' set as a 'cross-validation' set to more appropriately convey their experimental approach to the broader scientific community.

    We have referred to the replication set as “cross-validation” as suggested by the reviewer. However, the study subjects were recruited from over 27 medical centres across the United Kingdom (UK) representing most major cities. We have also added text to discuss this point.

    The authors look for functional validation using the BIOS qTL database. This reference provides valuable information about functional role of methylation in gene expression in whole blood (eQTM). We know that eQTMs are tissue specific. Do the authors have any evidence whether the methylation plays a similar role in bone tissue?

    We agree that eQTMs tend to be tissue specific and although we were able to gather some confidence about concordance in methylation levels between blood and bone tissue samples using the Ebrahimi study, it is rather difficult to speculate about the concordance in the effect on gene expression. We therefore raise this issue in the study limitation section of the paper.

    The authors report the markers from their 'best set' for prediction have potential functional relevance. The potential clinical relevance, however, requires additional context. The data were obtained after onset of PDB. The potential for reverse causation cannot be overlooked. Do the authors have any evidence that the methylation markers precede clinical diagnosis? Appropriate temporality is an essential requisite for an effective clinical prediction model.

    We agree with the reviewers that this is an issue with most EWAS studies. The observed methylation changes reported in the study may exist as a consequence of the disease. We therefore updated our discussion of study limitations to reflect the potential issue of reverse causation (page 11). We also discussed the design of future experiments when the predictive value of our best subset set could be properly validated with appropriate temporality. Specifically, how individuals with a genetic predisposition or/and family history of PDB could be measured routinely for changes in the methylation patterns of the best subset identified in this study in an attempt to draw possible associations with future disease onset.

  3. eLife

    Reviewer #3 (Public Review):

    Diboun et al used a case-control study design to identify DNA methylation sites and regions that differ between individuals with Paget's Disease of Bone (PDB) and controls. Cases were identified from an ongoing PDB clinical trial. Spouses of cases were used as controls. Candidate methylation sites were identified in a discovery set and then tested in a validation set to confirm association with PDB. Meta-analysis was used to combine effects from the discovery and validation sets. A machine learning approach was then used to prioritize candidates and build a prediction model capable of differentiating PDB cases from controls. The model was associated with high level of accuracy (AUC >0.90) in the discovery and validation sets.

    A major strength of the study is the collection of a large population of individuals with a rare bone disease. Epigenetic features are appealing for building prediction models as they may represent interplay between genetics and environment. Using this approach, the authors built a prediction model with a high level of accuracy. The results advance our understanding of the etiology of PDB.

    Overall, the primary conclusions are generally well supported. However, there are several aspects of the paper that will require additional clarification.

    I commend the authors for using a split sample cross validation approach to maximize experimental rigor. However, this approach is distinct from a true external replication. Given that the 'training' and the 'test' sets come from the same overall population, we expect the 'replication' results to be optimistic relative to results from a true, external replication population. Given the absence of a suitable external replication population due the unique nature of the disease, this limitation is acceptable. However, I expect the authors to discuss the potential limitations of this approach in their discussion section and I encourage the authors to refer to the 'replication' set as a 'cross-validation' set to more appropriately convey their experimental approach to the broader scientific community.

    The authors look for functional validation using the BIOS qTL database. This reference provides valuable information about functional role of methylation in gene expression in whole blood (eQTM). We know that eQTMs are tissue specific. Do the authors have any evidence whether the methylation plays a similar role in bone tissue?

    The authors report the markers from their 'best set' for prediction have potential functional relevance. The potential clinical relevance, however, requires additional context. The data were obtained after onset of PDB. The potential for reverse causation cannot be overlooked. Do the authors have any evidence that the methylation markers precede clinical diagnosis? Appropriate temporality is an essential requisite for an effective clinical prediction model.

  4. eLife

    Reviewer #2 (Public Review):

    This unique study has shown that epigenetic (therefore, potentially environment-driven) factors contribute to the pathogenesis of Paget's Disease of Bone (PDB). Although PDB is not very rare condition, its early diagnosis is problematic. The bone tissue is not easily accessible, thus many cases are not diagnosed till later in life. Thus, having diagnostic markers measured in blood, normalized to cell type count, might be of use for possible diagnostic applications.

    The PRISM trial's sample, comprising 232 cases and 260 controls from UK, was divided in two - discovery and replication sets - based on power calculations for EWAS. Meta-analysis of data from the discovery and replication sets revealed significant differences in DNA methylation. Among gene-body regions/loci, many associated with functions related to osteoclast differentiation, mechanical loading, immune function, etc. two loci were suggested as functional through expression quantitative trait methylation (eQTM) analysis. Further, there was some value in assessing the risk of developing PDB. The AUC of 82.5%, based on the 95 discriminatory sites from the "best subset" analysis, is promising for clinical applicability. If confirmed in independent samples and further studies, chromosomal loci found in this study may offer diagnostic markers for prediction of the disease.

  5. eLife

    Reviewer #1 (Public Review):

    The study by Diboun et al. aims to investigate methylation profiles in Paget's disease of bone patients. Many of the genes identified near areas of differentially methylated sites were known to be involved in osteoclast differentiation, viral infection and mechanical loading. These gene pathways are known to play a role in the pathogenesis of PDB. The strength of this study is that it is the first study to look at changes in methylation profiles in Paget's disease of bone patients. Additionally, the genes identified as having differentially methylated sites suggest that environmental factors such as host immune responses may be altered and play a role in the pathogenesis of PBD. The main weakness of this study is that the cells that were analyzed for changes in methylation sites were not osteoclasts the cells of interest in PBD. While many of the genes identified have been shown to play a role in regulation of the skeletal system, results should be interpreted with caution until they are validated in bone tissue.

  6. eLife

    Evaluation Summary:

    Paget disease of bone (PDB) results in focal areas of disorganized bone, leading to bone deformities and fragility. There is substantial interest in finding circulating biomarkers that might be of use for possible diagnostic applications and towards this end, these authors identified novel DNA methylation patterns in peripheral blood mononuclear cells that are able to differentiate PDB cases from controls with a high level of accuracy. This prediction model has functional relevance as these candidate methylation sites and regions are associated with osteological and immunologic processes and in the longer term, has future clinical potential.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

  7. Version published to 10.1101/2021.01.04.425216v1 on bioRxiv