How rare bone diseases have informed our knowledge of complex diseases

Johnson, Mark

doi:10.1038/bonekey.2016.69

Cited by 8 publications

(8 citation statements)

References 97 publications

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“…The purple module was the most significantly enriched (OR=5.4, P adj = 1.6 × 10 −34 ). We also compiled a list of 35 known drivers of monogenic bone diseases associated with osteoblast dysfunction, including osteogenesis imperfecta, hyperostosis, and osteosclerosis ( Supplemental File 8 ) 30–34 . Again, the purple module, containing 11 of 35 (31.4%) monogenic disease genes, was the most significantly enriched (OR = 21.3, Padj = 6.9 × 10 −9 ) ( Figure 2E ).…”

Section: Resultsmentioning

confidence: 99%

“…We also curated a list of genes associated with monogenic bone disorders using a literature review, specifically focusing on genes that disrupt osteoblast function, leading to monogenic bone disorders 30–34 ( Supplemental File 8 ). We used a fisher’s exact test to measure the statistical significance of the representation of genes with associated mouse knockout bone phenotypes and monogenic bone disease in each module.…”

Section: Methodsmentioning

confidence: 99%

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Identification of a core module for bone mineral density through the integration of a co-expression network and GWAS data

Sabik

Calabrese

Taleghani

et al. 2019

Preprint

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45Recently, the "omnigenic" model of the genetic architecture of complex traits proposed two 46 general categories of causal genes, core and peripheral. Core genes are hypothesized to play a 47 direct role in regulating disease; thus, their identification has the potential to reveal critical 48 regulators and novel therapeutic targets. Here, we sought to identify genes with "core-like" 49 characteristics for bone mineral density (BMD), one of the most significant predictors of 50 osteoporotic fracture. This was accomplished by analyzing genome-wide association study 51 (GWAS) data through the lens of a cell-type and timepoint-specific gene co-expression network 52 for mineralizing osteoblasts. We identified a single co-expression network module that was 53 enriched for genes implicated by GWAS and partitioned BMD heritability, correlated with in vitro 54 osteoblast mineralization, and enriched for genes, which when mutated in humans or mice, led 55 to a skeletal phenotype. Further characterization of this module identified four novel genes 56 (B4GALNT3, CADM1, DOCK9, and GPR133) located within BMD GWAS loci with colocalizing 57 expression quantitative trait loci (eQTL) and altered BMD in mouse knockouts, suggesting they 58 are causal genetic drivers of BMD in humans. Our network-based approach identified a "core" 59 module for BMD and provides a resource for expanding our understanding of the genetics of 60 bone mass. 61 62 63 64 65 66 67 68 69 70 3

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Identification of a core module for bone mineral density through the integration of a co-expression network and GWAS data

Sabik

Calabrese

Taleghani

et al. 2019

Preprint

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“…The latter are mechanosensing cells, which coordinate the function and differentiation of osteoblasts and osteoclasts, depending on their exposure to mechanical loading (21). Considering the closely interconnected relation of these cells, it is easy to deduce that defects in osteoblast differentiation or function can cause disease by influencing the net effect on bone mass development (22,23). Thus, models allowing the study of osteoblast biology can be insightful in delineating the disease mechanism.…”

Section: Bone Biologymentioning

confidence: 99%

Human Fibroblasts as a Model for the Study of Bone Disorders

et al. 2020

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Bone tissue degeneration is an urgent clinical issue, making it a subject of intensive research. Chronic skeletal disease forms can be prevalent, such as the age-related osteoporosis, or rare, in the form of monogenetic bone disorders. A barrier in the understanding of the underlying pathological process is the lack of accessibility to relevant material. For this reason, cells of non-bone tissue are emerging as a suitable alternative for models of bone biology. Fibroblasts are highly suitable for this application; they populate accessible anatomical locations, such as the skin tissue. Reports suggesting their utility in preclinical models for the study of skeletal diseases are increasingly becoming available. The majority of these are based on the generation of an intermediate stem cell type, the induced pluripotent stem cells, which are subsequently directed to the osteogenic cell lineage. This intermediate stage is circumvented in transdifferentiation, the process regulating the direct conversion of fibroblasts to osteogenic cells, which is currently not well-explored. With this mini review, we aimed to give an overview of existing osteogenic transdifferentiation models and to inform about their applications in bone biology models.

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“…Knowledge of the cell signalling pathways that regulate bone metabolism has emerged from studies of rare monogenetic bone disorders [1]. The canonical Wnt pathway was linked to bone development and homeostasis in individuals with sclerosteosis due to a loss-of-function variant in SOST in 2001, which encodes sclerostin that acts as an endogenous inhibitor of Wnt signalling [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…Knowledge of the cell signaling pathways that regulate bone metabolism has emerged from studies of rare monogenetic bone disorders. ( 1 ) The canonical Wnt pathway was linked to bone development and homeostasis in individuals with sclerosteosis due to a loss‐of‐function variant in SOST in 2001, which encodes sclerostin that acts as an endogenous inhibitor of Wnt signaling. ( 2 , 3 , 4 ) Later, pathogenetic variants in the low‐density lipoprotein (LDL) receptor‐related protein 5 ( LRP5 ), a Wnt co‐receptor, were linked with very high or low bone mass.…”

Section: Introductionmentioning

confidence: 99%