Abstract
Complexities in degenerative disorders, such as osteoarthritis, arise from multiscale biological, environmental, and temporal perturbations. Animal models serve to provide controlled representations of the natural history of degenerative disorders, but in themselves represent an additional layer of complexity. Comparing transcriptomic networks arising from gene co-expression data across species can facilitate an understanding of the preservation of functional gene modules and establish associations with disease phenotypes. This study demonstrates the preservation of osteoarthritis-associated gene modules, described by immune system and system development processes, across human and rat studies. Class prediction analysis establishes a minimal gene signature, including the expression of the Rho GDP dissociation inhibitor ARHGDIB, which consistently defined healthy human cartilage from osteoarthritic cartilage in an independent data set. The age of human clinical samples remains a strong confounder in defining the underlying gene regulatory mechanisms in osteoarthritis; however, defining preserved gene models across species may facilitate standardization of animal models of osteoarthritis to better represent human disease and control for ageing phenomena.
Highlights
Disorders of cartilage and joints account for a high incidence of disability[1] and are prevalent co-morbidities of the ageing population.[2]
Together with the description of the C4 module, these findings suggested that expression of genes in the M2 meta-module was consistent with a degenerate or dysregulated chondrocyte phenotype in both rat and human whole cartilage
This study demonstrates that added value may be gained from reanalysis of small transcriptomic studies using a network-based systems biology approach to establish conservation and divergence of transcriptional subnetworks between chondrocyte phenotypes in humans and a rodent model species
Summary
Disorders of cartilage and joints account for a high incidence of disability[1] and are prevalent co-morbidities of the ageing population.[2]. OA is a complex disease because it involves multiple tissues, environmental factors, behaviors, signaling pathways and genes. Numerous genetic risk loci, epigenetic effects, inflammation associated with ageing[7] and obesity[8] and biomechanical factors contribute to joint degeneration. Heritable factors account for 50% of an individual’s risk of developing OA, only 16 disease risk loci have been consistently identified[9] with candidate genes such as GDF5 and SMAD3 harboring the most promising risk alleles;[10] overall, multiple risk alleles are likely to contribute to OA susceptibility. As with other multifactorial diseases (e.g., neurological disorders), analysis of individual components cannot adequately explain the properties of the whole system (the contributing tissues) as novel properties emerge with increasing complexity of the system.[11]
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