An evolutionary perspective of DNA methylation patterns in skeletal tissues using a baboon model of osteoarthritis

Development of an epigenetic age predictor for costal cartilage with a simultaneous somatic tissue differentiation system

Age prediction from DNA has been a topic of interest in recent years due to the promising results obtained when using epigenetic markers. Since DNA methylation gradually changes across the individual's lifetime, prediction models have been developed accordingly for age estimation. The tissue-dependence for this biomarker usually necessitates the development of tissue-specific age prediction models, in this way, multiple models for age inference have been constructed for the most commonly encountered forensic tissues (blood, oral mucosa, semen). The analysis of skeletal remains has also been attempted and prediction models for bone have now been reported. Recently, the VISAGE Enhanced Tool was developed for the simultaneous DNA methylation analysis of 8 age-correlated loci using targeted high-throughput sequencing. It has been shown that this method is compatible with epigenetic age estimation models for blood, buccal cells, and bone. Since when dealing with decomposed cadavers or postmortem samples, cartilage samples are also an important biological source, an age prediction model for cartilage has been generated in the present study based on methylation data collected using the VISAGE Enhanced Tool. In this way, we have developed a forensic cartilage age prediction model using a training set composed of 109 samples (19-74 age range) based on DNA methylation levels from three CpGs in FHL2, TRIM59 and KLF14, using multivariate quantile regression which provides a mean absolute error (MAE) of ± 4.41 years. An independent testing set composed of 72 samples (19-75 age range) was also analyzed and provided an MAE of ± 4.26 years. In addition, we demonstrate that the 8 VISAGE markers, comprising EDARADD, TRIM59, ELOVL2, MIR29B2CHG, PDE4C, ASPA, FHL2 and KLF14, can be used as tissue prediction markers which provide reliable blood, buccal cells, bone, and cartilage differentiation using a developed multinomial logistic regression model. A training set composed of 392 samples (n = 87 blood, n = 86 buccal cells, n = 110 bone and n = 109 cartilage) was used for building the model (correct classifications: 98.72%, sensitivity: 0.988, specificity: 0.996) and validation was performed using a testing set composed of 192 samples (n = 38 blood, n = 36 buccal cells, n = 46 bone and n = 72 cartilage) showing similar predictive success to the training set (correct classifications: 97.4%, sensitivity: 0.968, specificity: 0.991). By developing both a new cartilage age model and a tissue differentiation model, our study significantly expands the use of the VISAGE Enhanced Tool while increasing the amount of DNA methylation-based information obtained from a single sample and a single forensic laboratory analysis. Both models have been placed in the open-access Snipper forensic classification website.

A Pilot Analysis of Genome-Wide DNA Methylation Patterns in Mouse Cartilage Reveals Overlapping Epigenetic Signatures of Aging and Osteoarthritis

OBJECTIVE

Cartilage epigenetic changes are strongly associated with human osteoarthritis (OA). However, the influence of individual environmental OA risk factors on these epigenetic patterns has not been determined; herein we characterize cartilage DNA methylation patterns associated with aging and OA in a mouse model.

METHODS

Murine knee cartilage DNA was extracted from healthy young (16-week, n = 6), old (82-week, n = 6), and young 4-week post-destabilization of the medial meniscus (DMM) OA (n = 6) C57BL6/J mice. Genome-wide DNA methylation patterns were determined via Illumina BeadChip. Gene set enrichment analysis was performed by Ingenuity Pathway Analysis. The top seven most differentially methylated positions (DMPs) were confirmed by pyrosequencing in an independent animal set. Results were compared to previously published human OA methylation data.

RESULTS

Aging was associated with 20,940 DMPs, whereas OA was associated with 761 DMPs. Merging these two conditions revealed 279 shared DMPs. All demonstrated similar directionality and magnitude of change (Δβ 1.0% ± 0.2%, mean methylation change ± SEM). Shared DMPs were enriched in OA-associated pathways, including RhoA signaling (P = 1.57 × 10^-4 ), protein kinase A signaling (P = 3.38 × 10^-4 ), and NFAT signaling (P = 6.14 × 10^-4 ). Upstream regulators, including TET3 (P = 6.15 × 10^-4 ), immunoglobulin (P = 6.14 × 10^-4 ), and TLR7 (P = 7.53 × 10^-4 ), were also enriched. Pyrosequencing confirmed six of the seven top DMPs in an independent cohort.

CONCLUSION

Aging and early OA following DMM surgery induce similar DNA methylation changes within a murine OA model, suggesting that aging may induce pro-OA epigenetic "poising" within articular cartilage. Future research should focus on confirming and expanding these findings to other environmental OA risk factors, including obesity, as well as determining late OA changes in mice.

DNA methylation and noncoding RNA in OA: Recent findings and methodological advances

Introduction:

Osteoarthritis (OA) is a chronic musculoskeletal disease characterized by progressive loss of joint function. Historically, it has been characterized as a disease caused by mechanical trauma, so-called ‘wear and tear’. Over the past two decades, it has come to be understood as a complex systemic disorder involving gene-environmental interactions. Epigenetic changes have been increasingly implicated. Recent improvements in microarray and next-generation sequencing (NGS) technologies have allowed for ever more complex evaluations of epigenetic aberrations associated with the development and progression of OA.

Methods:

A systematic review was conducted in the Pubmed database. We curated studies that presented the results of DNA methylation and noncoding RNA research in human OA and OA animal models since 1985.

Results:

Herein, we discuss recent findings and methodological advancements in OA epigenetics, including a discussion of DNA methylation, including microarray and NGS studies, and noncoding RNAs. Beyond cartilage, we also highlight studies in subchondral bone and peripheral blood mononuclear cells, which highlight widespread and potentially clinically important alterations in epigenetic patterns seen in OA patients. Finally, we discuss epigenetic editing approaches in the context of OA.

Conclusions:

Although a substantial body of literature has already been published in OA, much is still unknown. Future OA epigenetics studies will no doubt continue to broaden our understanding of underlying pathophysiology and perhaps offer novel diagnostics and/or treatments for human OA.

Evolutionary insights into primate skeletal gene regulation using a comparative cell culture model

The evolution of complex skeletal traits in primates was likely influenced by both genetic and environmental factors. Because skeletal tissues are notoriously challenging to study using functional genomic approaches, they remain poorly characterized even in humans, let alone across multiple species. The challenges involved in obtaining functional genomic data from the skeleton, combined with the difficulty of obtaining such tissues from nonhuman apes, motivated us to consider an alternative in vitro system with which to comparatively study gene regulation in skeletal cell types. Specifically, we differentiated six human (Homo sapiens) and six chimpanzee (Pan troglodytes) induced pluripotent stem cell lines (iPSCs) into mesenchymal stem cells (MSCs) and subsequently into osteogenic cells (bone cells). We validated differentiation using standard methods and collected single-cell RNA sequencing data from over 100,000 cells across multiple samples and replicates at each stage of differentiation. While most genes that we examined display conserved patterns of expression across species, hundreds of genes are differentially expressed (DE) between humans and chimpanzees within and across stages of osteogenic differentiation. Some of these interspecific DE genes show functional enrichments relevant in skeletal tissue trait development. Moreover, topic modeling indicates that interspecific gene programs become more pronounced as cells mature. Overall, we propose that this in vitro model can be used to identify interspecific regulatory differences that may have contributed to skeletal trait differences between species.

Primates display a range of skeletal morphologies and susceptibilities to skeletal diseases, but the molecular basis of these phenotypic differences is unclear. Studies of gene expression variation in primate skeletal tissues are extremely restricted due to the ethical and practical challenges associated with collecting samples. Nevertheless, the ability to study gene regulation in primate skeletal tissues is crucial for understanding how the primate skeleton has evolved. We therefore developed a comparative primate skeletal cell culture model that allows us to access a spectrum of human and chimpanzee cell types as they differentiate from stem cells into bone cells. While most gene expression patterns are conserved across species, we also identified hundreds of differentially expressed genes between humans and chimpanzees within and across stages of differentiation. We also classified cells by osteogenic stage and identified additional interspecific differentially expressed genes which may contribute to skeletal trait differences. We anticipate that this model will be extremely useful for exploring questions related to gene regulation variation in primate bone biology and development.