Gemstone
The GEnomics of MusculoSkeletal traits TranslatiOnal Network (GEMSTONE) COST Action fosters collaboration among experts in genetics, epidemiology, bioinformatics, and clinical medicine across Europe. It aims to translate genomic discoveries in the musculoskeletal field into clinical applications. This initiative recognizes mobility as crucial for quality of life and independence, particularly as individuals age. Limited mobility in the elderly often precedes chronic conditions such as osteoporosis, diabetes, hypertension, and coronary heart disease.
Advancements in whole-exome sequencing and genome-wide association studies (GWAS) over the past decade have significantly enhanced our understanding of genetic factors influencing both monogenic and complex traits [1, 2]. In the musculoskeletal domain, these advancements have been pivotal in identifying genetic factors related to conditions like osteoporosis [3]. Comprehensive GWAS and studies of monogenic disorders affecting bone mass have provided insights into skeletal physiology, highlighting the need for a roadmap to guide musculoskeletal metabolism research and clinical applications, such as novel medication development.
Current treatments for osteoporosis and osteoarthritis have limited efficacy (reducing fracture risk by 25-50%) [9, 10], prompting the need for deeper understanding and new molecular pathways for innovative treatment options. Integrating genetic information to identify drug targets has shown promise in enhancing drug development success rates across phases, particularly in musculoskeletal and metabolic areas [11].
The GEMSTONE COST Action aims to unite diverse disciplines within musculoskeletal research to translate genetic discoveries into clinical applications, advancing personalized medicine (Figure 1). Studies on extreme phenotypes have uncovered molecular mechanisms underlying rare and common chronic diseases, revolutionizing treatments [12-15]. Genes implicated in familial bone disorders reveal overlaps in biological pathways affecting both monogenic and complex musculoskeletal conditions [16, 17].
Publications
The genetic overlap between osteoporosis and craniosynostosis
The genetic overlap between osteoporosis and craniosynostosis
Affiliations
1 School of Physiology, Pharmacology and Neuroscience, Biomedical Sciences, University of Bristol, Bristol, United Kingdom.
2 Department of Internal Medicine, Erasmus Medical Center (MC), University Medical Center Rotterdam, Rotterdam, Netherlands.
3 Department of Pediatrics, University of California, Davis, Sacramento, CA, United States.
4 Department of Oral and Maxillofacial Surgery, Erasmus Medical Center (MC), University Medical Center Rotterdam, Rotterdam, Netherlands.
Abstract
Osteoporosis is the most prevalent bone condition in the ageing population. This systemic disease is characterized by microarchitectural deterioration of bone, leading to increased fracture risk. In the past 15 years, genome-wide association studies (GWAS), have pinpointed hundreds of loci associated with bone mineral density (BMD), helping elucidate the underlying molecular mechanisms and genetic architecture of fracture risk. However, the challenge remains in pinpointing causative genes driving GWAS signals as a pivotal step to drawing the translational therapeutic roadmap. Recently, a skull BMD-GWAS uncovered an intriguing intersection with craniosynostosis, a congenital anomaly due to premature suture fusion in the skull. Here, we recapitulate the genetic contribution to both osteoporosis and craniosynostosis, describing the biological underpinnings of this overlap and using zebrafish models to leverage the functional investigation of genes associated with skull development and systemic skeletal homeostasis.
Keywords:bone mineral density; craniosynostosis; fractures; genome-wide association studies; osteoporosis; zebrafish.
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High Bone Mass Disorders: New Insights From Connecting the Clinic and the Bench
High Bone Mass Disorders: New Insights From Connecting the Clinic and the Bench
Affiliations
1 School of Physiology, Pharmacology, and Neuroscience, Faculty of Life Sciences, University of Bristol, Bristol, UK.
2 Musculoskeletal Research Unit, Translational Health Sciences, Bristol Medical School, Faculty of Health Sciences, University of Bristol, Bristol, UK.
3 Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, L’Aquila, Italy.
4 Department of Applied Biomedical Science, Faculty of Health Sciences, University of Malta, Msida, Malta.
5 Center for Molecular Medicine and Biobanking, University of Malta, Msida, Malta.
6 Department of Paediatrics and Adolescent Medicine, Johannes Kepler University Linz, Linz, Austria.
7 Italian Bone Disease Research Foundation (FIRMO), Florence, Italy.
8 Department of Internal Medicine, Hospital U M Valdecilla, University of Cantabria, IDIVAL, Santander, Spain.
9 Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands.
10 Department of Hygiene and Epidemiology, Medical School, University of Ioannina, Ioannina, Greece.
11 Center for Evidence Synthesis in Health, Policy and Practice, Center for Research Synthesis in Health, School of Public Health, Brown University, Providence, RI, USA.
12 institute of Biosciences, University Research Center of loannina, University of Ioannina, Ioannina, Greece.
13 Department of Twin Research & Genetic Epidemiology, School of Life Course Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, UK.
14 Department of Endocrinology, Guy’s and St Thomas’ NHS Foundation Trust, London, UK.
15 Marcus Institute for Aging Research, Hebrew SeniorLife and Department of Medicine Beth Israel Deaconess Medical Center and Harvard Medical School, Broad Institute of MIT & Harvard, Cambridge, MA, USA.
16 Department of Rare Skeletal Diseases, IRCCS Rizzoli Orthopaedic Institute, Bologna, Italy.
17 Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK.
18 University of Malaga, Malaga, Spain.
19 Children’s Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
20 Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
21 folkhälsan Research Centre, Folkhälsan Institute of Genetics, Helsinki, Finland.
22 Department of Medical Genetics, University of Antwerp, Antwerp, Belgium.
23 Department of Human Genetics, KU Leuven, Leuven, Belgium.
- DOI: 10.1002/jbmr.4715
Abstract
Monogenic high bone mass (HBM) disorders are characterized by an increased amount of bone in general, or at specific sites in the skeleton. Here, we describe 59 HBM disorders with 50 known disease-causing genes from the literature, and we provide an overview of the signaling pathways and mechanisms involved in the pathogenesis of these disorders. Based on this, we classify the known HBM genes into HBM (sub)groups according to uniform Gene Ontology (GO) terminology. This classification system may aid in hypothesis generation, for both wet lab experimental design and clinical genetic screening strategies. We discuss how functional genomics can shape discovery of novel HBM genes and/or mechanisms in the future, through implementation of omics assessments in existing and future model systems. Finally, we address strategies to improve gene identification in unsolved HBM cases and highlight the importance for cross-laboratory collaborations encompassing multidisciplinary efforts to transfer knowledge generated at the bench to the clinic. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
Keywords:ANABOLICS; ANIMAL MODELS; CELL/TISSUE SIGNALING; DISEASES AND DISORDERS OF/RELATED TO BONE; GENETIC ANIMAL MODELS; GENETIC RESEARCH; PARACRINE PATHWAYS; THERAPEUTICS.
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Perspective of the GEMSTONE Consortium on Current and Future Approaches to Functional Validation for Skeletal Genetic Disease Using Cellular, Molecular and Animal-Modeling Techniques
Perspective of the GEMSTONE Consortium on Current and Future Approaches to Functional Validation for Skeletal Genetic Disease Using Cellular, Molecular and Animal-Modeling Techniques
Affiliations
1 Department of Medicine III, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany.
2 University Hospital Carl Gustav Carus, Dresden, Germany.
3 Department of Internal Medicine, Division of Endocrinology and Diabetology, Endocrine Lab Platform, Medical University of Graz, Graz, Austria.
4 Department of Applied Biomedical Science, Faculty of Health Sciences, University of Malta, Msida, Malta.
5 Centre for Molecular Medicine and Biobanking, University of Malta, Msida, Malta.
6 School of Physiology, Pharmacology, and Neuroscience, Faculty of Life Sciences, University of Bristol, Bristol, United Kingdom.
7 Department of Oral and Maxillofacial Surgery, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands.
8 Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands.
9 The Generation R Study, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands.
10 Rheumatology and Bone Disease Unit, CGEM, Institute of Genetics and Cancer (IGC), Edinburgh, United Kingdom.
11 Musculoskeletal Genetics Laboratory, Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel.
12 Musculoskeletal Research Unit, Translational Health Sciences, Bristol Medical School, Faculty of Health Sciences, University of Bristol, Bristol, United Kingdom.
13 Department of Osteology and Biomechanics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
14 Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Centro Académico de Medicina de Lisboa, Lisbon, Portugal.
15 Department of Biotechnology, Agricultural University of Athens, Athens, Greece.
16 Institute for Bioinnovation, B.S.R.C. “Alexander Fleming”, Vari, Greece.
17 Department of Anatomy & Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
18 Musculoskeletal Research Group, IMIM (Hospital del Mar Medical Research Institute), Centro de Investigación Biomédica en Red en Fragilidad y Envejecimiento Saludable (CIBERFES), ISCIII, Barcelona, Spain.
19 Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, CIBERER, IBUB, IRSJD, Barcelona, Spain.
20 Department of Clinical Biochemistry, Faculty of Pharmacy, University of Ljubljana, Ljubljana, Slovenia.
21 Bone Biology & Disease Laboratory, School of Biomedical Sciences, The University of Western Australia, Nedlands, WA, Australia.
22 Department of Internal Medicine, Erasmus MC, Rotterdam, Netherlands.
23 Institute of Medical Statistics and Informatic, Faculty of Medicine, University of Belgrade, Belgrade, Serbia.
24 Department of Medical Genetics, University of Antwerp, Antwerp, Belgium.
25 Unger-Vetlesen Institute, Lovisenberg Diaconal Hospital, Oslo, Norway.
26 Department of Plastic and Reconstructive Surgery, Oslo University Hospital, Oslo, Norway.
27 Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway.
28 Clinical Cell Biology, Department of Pathology, Odense University Hospital, Odense, Denmark.
29 Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
30 Department of Molecular Medicine, University of Southern Denmark, Odense, Denmark.
31 Azrieli Faculty of Medicine, Bar-Ilan University, Ramat Gan, Israel.
32 Marcus Research Institute, Hebrew SeniorLife, Boston, MA, United States.
Abstract
The availability of large human datasets for genome-wide association studies (GWAS) and the advancement of sequencing technologies have boosted the identification of genetic variants in complex and rare diseases in the skeletal field. Yet, interpreting results from human association studies remains a challenge. To bridge the gap between genetic association and causality, a systematic functional investigation is necessary. Multiple unknowns exist for putative causal genes, including cellular localization of the molecular function. Intermediate traits (“endophenotypes”), e.g. molecular quantitative trait loci (molQTLs), are needed to identify mechanisms of underlying associations. Furthermore, index variants often reside in non-coding regions of the genome, therefore challenging for interpretation. Knowledge of non-coding variance (e.g. ncRNAs), repetitive sequences, and regulatory interactions between enhancers and their target genes is central for understanding causal genes in skeletal conditions. Animal models with deep skeletal phenotyping and cell culture models have already facilitated fine mapping of some association signals, elucidated gene mechanisms, and revealed disease-relevant biology. However, to accelerate research towards bridging the current gap between association and causality in skeletal diseases, alternative in vivo platforms need to be used and developed in parallel with the current -omics and traditional in vivo resources. Therefore, we argue that as a field we need to establish resource-sharing standards to collectively address complex research questions. These standards will promote data integration from various -omics technologies and functional dissection of human complex traits. In this mission statement, we review the current available resources and as a group propose a consensus to facilitate resource sharing using existing and future resources. Such coordination efforts will maximize the acquisition of knowledge from different approaches and thus reduce redundancy and duplication of resources. These measures will help to understand the pathogenesis of osteoporosis and other skeletal diseases towards defining new and more efficient therapeutic targets.
Keywords: animal models; data integration analysis; gene regulation; genome-wide association study; musculoskeletal disease.
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