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Du C, Liu J, Liu S, Xiao P, Chen Z, Chen H, Huang W, Lei Y. Bone and Joint-on-Chip Platforms: Construction Strategies and Applications. SMALL METHODS 2024:e2400436. [PMID: 38763918 DOI: 10.1002/smtd.202400436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 04/28/2024] [Indexed: 05/21/2024]
Abstract
Organ-on-a-chip, also known as "tissue chip," is an advanced platform based on microfluidic systems for constructing miniature organ models in vitro. They can replicate the complex physiological and pathological responses of human organs. In recent years, the development of bone and joint-on-chip platforms aims to simulate the complex physiological and pathological processes occurring in human bones and joints, including cell-cell interactions, the interplay of various biochemical factors, the effects of mechanical stimuli, and the intricate connections between multiple organs. In the future, bone and joint-on-chip platforms will integrate the advantages of multiple disciplines, bringing more possibilities for exploring disease mechanisms, drug screening, and personalized medicine. This review explores the construction and application of Organ-on-a-chip technology in bone and joint disease research, proposes a modular construction concept, and discusses the new opportunities and future challenges in the construction and application of bone and joint-on-chip platforms.
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Affiliation(s)
- Chengcheng Du
- Department of Orthopedics, Orthopedic Laboratory of Chongqing Medical University, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Jiacheng Liu
- Department of Orthopedics, Orthopedic Laboratory of Chongqing Medical University, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Senrui Liu
- Department of Orthopedics, Orthopedic Laboratory of Chongqing Medical University, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Pengcheng Xiao
- Department of Orthopedics, Orthopedic Laboratory of Chongqing Medical University, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Zhuolin Chen
- Department of Orthopedics, Orthopedic Laboratory of Chongqing Medical University, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Hong Chen
- Department of Orthopedics, Orthopedic Laboratory of Chongqing Medical University, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Wei Huang
- Department of Orthopedics, Orthopedic Laboratory of Chongqing Medical University, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Yiting Lei
- Department of Orthopedics, Orthopedic Laboratory of Chongqing Medical University, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
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Courties A. Osteoarthritis and diabetes: Is there a true link? Joint Bone Spine 2024; 91:105684. [PMID: 38181900 DOI: 10.1016/j.jbspin.2023.105684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 12/13/2023] [Accepted: 12/19/2023] [Indexed: 01/07/2024]
Affiliation(s)
- Alice Courties
- Inserm UMRS_938, Department of Rheumatology, Centre de Recherche Saint-Antoine, Saint-Antoine Hospital, Sorbonne Université, Assistance publique-Hôpitaux de Paris (AP-HP), 75012 Paris, France.
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Mei L, Zhang Z, Chen R, Li Z. Phenome-wide causal associations between osteoarthritis and other complex traits through the latent causal variable analysis. BMC Musculoskelet Disord 2024; 25:238. [PMID: 38532343 DOI: 10.1186/s12891-024-07360-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 03/15/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND Individuals with osteoarthritis present with comorbidities, and the potential causal associations remain incompletely elucidated. The present study undertook a large-scale investigation about the causality between osteoarthritis and variable traits, using the summary-level data of genome-wide association studies (GWAS). METHODS The present study included the summary-level GWS data of knee osteoarthritis, hip osteoarthritis, hip or knee osteoarthritis, hand osteoarthritis, and other 1355 traits. Genetic correlation analysis was conducted between osteoarthritis and other traits through cross-trait bivariate linkage disequilibrium score regression. Subsequently, latent causal variable analysis was performed to explore the causal association when there was a significant genetic correlation. Genetic correlation and latent causal variable analysis were conducted on the Complex Traits Genomics Virtual Lab platform ( https://vl.genoma.io/ ). RESULTS We found 133 unique phenotypes showing causal relationships with osteoarthritis. Our results confirmed several well-established risk factors of osteoarthritis, such as obesity, weight, BMI, and meniscus derangement. Additionally, our findings suggested putative causal links between osteoarthritis and multiple factors. Socioeconomic determinants such as occupational exposure to dust and diesel exhaust, extended work hours exceeding 40 per week, and unemployment status were implicated. Furthermore, our analysis revealed causal associations with cardiovascular and metabolic disorders, including heart failure, deep venous thrombosis, type 2 diabetes mellitus, and elevated cholesterol levels. Soft tissue and musculoskeletal disorders, such as hallux valgus, internal derangement of the knee, and spondylitis, were also identified to be causally related to osteoarthritis. The study also identified the putative causal associations of osteoarthritis with digestive and respiratory diseases, such as Barrett's esophagus, esophagitis, and asthma, as well as psychiatric conditions including panic attacks and manic or hyperactive episodes. Additionally, we observed osteoarthritis causally related to pharmacological treatments, such as the use of antihypertensive medications, anti-asthmatic drugs, and antidepressants. CONCLUSION Our study uncovered a wide range of traits causally associated with osteoarthritis. Further studies are needed to validate and illustrate the detailed mechanism of those causal associations.
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Affiliation(s)
- Lin Mei
- Department of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Changsha, China
| | - Zhiming Zhang
- Department of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Changsha, China
| | - Ruiqi Chen
- Department of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Changsha, China
| | - Zhihong Li
- Department of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, China.
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Changsha, China.
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Ramos YFM, Rice SJ, Ali SA, Pastrello C, Jurisica I, Rai MF, Collins KH, Lang A, Maerz T, Geurts J, Ruiz-Romero C, June RK, Thomas Appleton C, Rockel JS, Kapoor M. Evolution and advancements in genomics and epigenomics in OA research: How far we have come. Osteoarthritis Cartilage 2024:S1063-4584(24)00054-2. [PMID: 38428513 DOI: 10.1016/j.joca.2024.02.656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/29/2024] [Accepted: 02/25/2024] [Indexed: 03/03/2024]
Abstract
OBJECTIVE Osteoarthritis (OA) is the most prevalent musculoskeletal disease affecting articulating joint tissues, resulting in local and systemic changes that contribute to increased pain and reduced function. Diverse technological advancements have culminated in the advent of high throughput "omic" technologies, enabling identification of comprehensive changes in molecular mediators associated with the disease. Amongst these technologies, genomics and epigenomics - including methylomics and miRNomics, have emerged as important tools to aid our biological understanding of disease. DESIGN In this narrative review, we selected articles discussing advancements and applications of these technologies to OA biology and pathology. We discuss how genomics, deoxyribonucleic acid (DNA) methylomics, and miRNomics have uncovered disease-related molecular markers in the local and systemic tissues or fluids of OA patients. RESULTS Genomics investigations into the genetic links of OA, including using genome-wide association studies, have evolved to identify 100+ genetic susceptibility markers of OA. Epigenomic investigations of gene methylation status have identified the importance of methylation to OA-related catabolic gene expression. Furthermore, miRNomic studies have identified key microRNA signatures in various tissues and fluids related to OA disease. CONCLUSIONS Sharing of standardized, well-annotated omic datasets in curated repositories will be key to enhancing statistical power to detect smaller and targetable changes in the biological signatures underlying OA pathogenesis. Additionally, continued technological developments and analysis methods, including using computational molecular and regulatory networks, are likely to facilitate improved detection of disease-relevant targets, in-turn, supporting precision medicine approaches and new treatment strategies for OA.
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Affiliation(s)
- Yolande F M Ramos
- Dept. Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Sarah J Rice
- Biosciences Institute, International Centre for Life, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Shabana Amanda Ali
- Henry Ford Health + Michigan State University Health Sciences, Detroit, MI, USA
| | - Chiara Pastrello
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, UHN, Toronto, Ontario, Canada
| | - Igor Jurisica
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, UHN, Toronto, Ontario, Canada; Departments of Medical Biophysics and Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Muhammad Farooq Rai
- Department of Biological Sciences, Center for Biotechnology, College of Medicine & Health Sciences, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Kelsey H Collins
- Department of Orthopaedic Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Annemarie Lang
- Departments of Orthopaedic Surgery and Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Tristan Maerz
- Department of Orthopaedic Surgery, University of Michigan, Ann Arbor, MI, USA
| | - Jeroen Geurts
- Rheumatology, Department of Musculoskeletal Medicine, Lausanne University Hospital, Lausanne, Switzerland
| | - Cristina Ruiz-Romero
- Grupo de Investigación de Reumatología (GIR), Unidad de Proteómica, INIBIC -Hospital Universitario A Coruña, SERGAS, A Coruña, Spain
| | - Ronald K June
- Department of Mechanical & Industrial Engineering, Montana State University, Bozeman, MT, USA
| | - C Thomas Appleton
- Department of Medicine, University of Western Ontario, London, Ontario, Canada
| | - Jason S Rockel
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, UHN, Toronto, Ontario, Canada
| | - Mohit Kapoor
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, UHN, Toronto, Ontario, Canada.
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Jiang Z, Cai X, Yao X, Zhang S, Lan W, Jin Z, Tang F, Ma W, Yao X, Chen C, Lan T. The causal effect of cytokine cycling levels on osteoarthritis: a bidirectional Mendelian randomized study. Front Immunol 2024; 14:1334361. [PMID: 38274820 PMCID: PMC10808687 DOI: 10.3389/fimmu.2023.1334361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 12/27/2023] [Indexed: 01/27/2024] Open
Abstract
Objective Osteoarthritis (OA) is the most prevalent joint disease globally, serving as a primary cause of pain and disability. However, the pathological processes underlying OA remain incompletely understood. Several studies have noted an association between cytokines and OA, yet the causal link between them remains ambiguous. This study aims to identify cytokines potentially causally related to OA using Mendelian randomization (MR) analysis, informing early clinical diagnosis and treatment decisions. Methods We conducted a genome-wide association study (GWAS) on 12 OA traits involving 177,517 cases and 649,173 controls from 9 international cohorts. For discovery MR analysis, we used 103 cytokines from two European populations as instrumental variables (IVs). Concurrently, another European population OA GWAS database (36,185 cases and 135,185 controls) was used to replicate MR analysis, employing the inverse variance weighted (IVW) method as the primary analytic approach. Additional methods tested included MR Egger, Weighted median, and Weighted mode. We merged the MR findings through meta-analysis. Heterogeneity testing, level pleiotropy testing (MR Egger intercept test and MRPRESSO), and sensitivity analysis via Leave One Out (LOO) were conducted to verify result robustness. Lastly, reverse MR analysis was performed. Results The meta-analysis merger revealed a correlation between CX3CL1 cycle levels and increased OA risk (OR=1.070, 95% CI: 1.040-1.110; P<0.010). We also observed associations between MCP4 (OR=0.930, 95% CI: 0.890-0.970; P<0.010) and CCL25 (OR=0.930, 95% CI: 0.890-0.970; P<0.010) with reduced OA risk. The sensitivity analysis results corroborate the robustness of these findings. Conclusion Our MR analysis indicates a potential causal relationship between CX3CL1, MCP4, CCL25, and OA risk changes. Further research is warranted to explore the influence of cytokines on OA development.
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Affiliation(s)
- Zong Jiang
- Second Clinical Medical College, Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Xin Cai
- Department of Rheumatology and Immunology, The First People's Hospital Of Guiyang, Guiyang, China
| | - Xiaoling Yao
- Second Clinical Medical College, Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Shaoqin Zhang
- Department of Rheumatology and Immunology, The First People's Hospital Of Guiyang, Guiyang, China
| | - Weiya Lan
- Second Clinical Medical College, Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Zexu Jin
- Department of Rheumatology and Immunology, The First People's Hospital Of Guiyang, Guiyang, China
| | - Fang Tang
- Department of Rheumatology and Immunology, The Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Wukai Ma
- Department of Rheumatology and Immunology, The Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Xueming Yao
- Department of Rheumatology and Immunology, The Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Changming Chen
- Department of Rheumatology and Immunology, The Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Tianzuo Lan
- Department of Rheumatology and Immunology, The First People's Hospital Of Guiyang, Guiyang, China
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Mbatchou J, McPeek MS. JASPER: fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.18.571948. [PMID: 38187553 PMCID: PMC10769254 DOI: 10.1101/2023.12.18.571948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Joint association analysis of multiple traits with multiple genetic variants can provide insight into genetic architecture and pleiotropy, improve trait prediction and increase power for detecting association. Furthermore, some traits are naturally high-dimensional, e.g., images, networks or longitudinally measured traits. Assessing significance for multitrait genetic association can be challenging, especially when the sample has population sub-structure and/or related individuals. Failure to adequately adjust for sample structure can lead to power loss and inflated type 1 error, and commonly used methods for assessing significance can work poorly with a large number of traits or be computationally slow. We developed JASPER, a fast, powerful, robust method for assessing significance of multitrait association with a set of genetic variants, in samples that have population sub-structure, admixture and/or relatedness. In simulations, JASPER has higher power, better type 1 error control, and faster computation than existing methods, with the power and speed advantage of JASPER increasing with the number of traits. JASPER is potentially applicable to a wide range of association testing applications, including for multiple disease traits, expression traits, image-derived traits and microbiome abundances. It allows for covariates, ascertainment and rare variants and is robust to phenotype model misspecification. We apply JASPER to analyze gene expression in the Framingham Heart Study, where, compared to alternative approaches, JASPER finds more significant associations, including several that indicate pleiotropic effects, some of which replicate previous results, while others have not previously been reported. Our results demonstrate the promise of JASPER for powerful multitrait analysis in structured samples.
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Affiliation(s)
- Joelle Mbatchou
- Regeneron Genetics Center, Tarrytown, NY 10591, USA
- Department of Statistics, The University of Chicago, Chicago, IL 60637, USA
| | - Mary Sara McPeek
- Department of Statistics, The University of Chicago, Chicago, IL 60637, USA
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA
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