Transcriptomic Analysis of Cytokine-Treated Tissue-Engineered Cartilage as An In Vitro Model of Osteoarthritis

Osteoarthritis (OA), as the most common form of arthritis and a leading cause of disability worldwide, currently has no disease-modifying drugs. Inflammation plays an important role in cartilage degeneration in OA, and pro-inflammatory cytokines, IL-1β and TNF-α, have been shown to induce degradative changes along with aberrant gene expression in chondrocytes, the only resident cells in cartilage. The goal of this study was to further understand the transcriptomic regulation of tissue-engineered cartilage in response to inflammatory cytokines using an in vitro miPSC model system. We performed RNA sequencing for the IL-1β or TNF-α treated tissue-engineered cartilage derived from murine iPSCs, and analyzed transcriptomic profiles by comparing with those of two different osteoarthritis models and human OA cartilage samples. We investigated differentially expressed genes (DEGs) as well as gene set enrichment and protein-protein interaction network, showing a significant similarity between model systems and human OA cartilage. Our analysis revealed a significant number of overlapping DEGs, together with consistent pathway enrichment in inflammatory response, cytokine-mediated response and extracellular matrix organization, which support that the murine iPSC model system can replicate many of the characteristics of OA cartilage at the transcriptomic level, specifically in the catabolic aspect of inflammation induce OA. The murine iPSC model system provides a method for studying the pro-inflammatory response and pathogenesis in OA cartilage and will be a valuable dataset for identifying therapeutic targets of inflammation-induced OA

The microRNA-29 family in cartilage homeostasis and osteoarthritis

MicroRNAs have been shown to function in cartilage development and homeostasis, as well as in progression of osteoarthritis. The objective of the current study was to identify microRNAs involved in the onset or early progression of osteoarthritis and characterise their function in chondrocytes. MicroRNA expression in mouse knee joints post-DMM surgery was measured over 7 days. Expression of miR-29b-3p was increased at day 1 and regulated in the opposite direction to its potential targets. In a mouse model of cartilage injury and in end-stage human OA cartilage, the miR-29 family were also regulated. SOX9 repressed expression of miR-29a-3p and miR-29b-3p via the 29a/b1 promoter. TGFβ1 decreased expression of miR-29a, b and c (3p) in primary chondrocytes, whilst IL-1β increased (but LPS decreased) their expression. The miR-29 family negatively regulated Smad, NFκB and canonical WNT signalling pathways. Expression profiles revealed regulation of new WNT-related genes. Amongst these, FZD3, FZD5, DVL3, FRAT2, CK2A2 were validated as direct targets of the miR-29 family. These data identify the miR-29 family as microRNAs acting across development and progression of OA. They are regulated by factors which are important in OA and impact on relevant signalling pathways

PU.1 controls fibroblast polarization and tissue fibrosis

Fibroblasts are polymorphic cells with pleiotropic roles in organ morphogenesis, tissue homeostasis and immune responses. In fibrotic diseases, fibroblasts synthesize abundant amounts of extracellular matrix, which induces scarring and organ failure. By contrast, a hallmark feature of fibroblasts in arthritis is degradation of the extracellular matrix because of the release of metalloproteinases and degrading enzymes, and subsequent tissue destruction. The mechanisms that drive these functionally opposing pro-fibrotic and pro-inflammatory phenotypes of fibroblasts remain unknown. Here we identify the transcription factor PU.1 as an essential regulator of the pro-fibrotic gene expression program. The interplay between transcriptional and post-transcriptional mechanisms that normally control the expression of PU.1 expression is perturbed in various fibrotic diseases, resulting in the upregulation of PU.1, induction of fibrosis-associated gene sets and a phenotypic switch in extracellular matrix-producing pro-fibrotic fibroblasts. By contrast, pharmacological and genetic inactivation of PU.1 disrupts the fibrotic network and enables reprogramming of fibrotic fibroblasts into resting fibroblasts, leading to regression of fibrosis in several organs

Genetic biomarkers in osteoarthritis: a quick overview

[Abstract] Osteoarthritis (OA) is a chronic musculoskeletal disease with a polygenic and heterogeneous nature. In addition, when clinical manifestations appear, the evolution of the disease is usually already irreversible. Therefore, the efforts on OA research are focused mainly on the discovery of therapeutic targets and reliable biomarkers that permit the early identification of different OA-related parameters such as diagnosis, prognosis, or phenotype identification. To date, potential candidate protein biomarkers have been associated with different aspects of the disease; however, there is currently no gold standard. In this sense, genomic data could act as complementary biomarkers of diagnosis and prognosis or even help to identify therapeutic targets of the disease. In this review, we will describe the most recent advances in genetic biomarkers in OA over the past three years.Instituto de Salud Carlos III; PI17/00210Instituto de Salud Carlos III; PI16/02124Instituto de Salud Carlos III; PI20/00614Instituto de salud Carlos III; RETIC-RIER-RD16/0012/0002Instituto de Salud Carlos III; PRB3-ISCIII-PT17/0019/0014Xunta de Galicia; IN607A2017/1

A systems biology approach to defining regulatory mechanisms for cartilage and tendon cell phenotypes

Phenotypic plasticity of adult somatic cells has provided emerging avenues for the development of regenerative therapeutics. In musculoskeletal biology the mechanistic regulatory networks of genes governing the phenotypic plasticity of cartilage and tendon cells has not been considered systematically. Additionally, a lack of strategies to effectively reproduce in vitro functional models of cartilage and tendon is retarding progress in this field. De- and redifferentiation represent phenotypic transitions that may contribute to loss of function in ageing musculoskeletal tissues. Applying a systems biology network analysis approach to global gene expression profiles derived from common in vitro culture systems (monolayer and three-dimensional cultures) this study demonstrates common regulatory mechanisms governing de- and redifferentiation transitions in cartilage and tendon cells. Furthermore, evidence of convergence of gene expression profiles during monolayer expansion of cartilage and tendon cells, and the expression of key developmental markers, challenges the physiological relevance of this culture system. The study also suggests that oxidative stress and PI3K signalling pathways are key modulators of in vitro phenotypes for cells of musculoskeletal origin

Text Mining and Gene Expression Analysis Towards Combined Interpretation of High Throughput Data

Microarrays can capture gene expression activity for thousands of genes simultaneously and thus make it possible to analyze cell physiology and disease processes on molecular level. The interpretation of microarray gene expression experiments profits from knowledge on the analyzed genes and proteins and the biochemical networks in which they play a role. The trend is towards the development of data analysis methods that integrate diverse data types. Currently, the most comprehensive biomedical knowledge source is a large repository of free text articles. Text mining makes it possible to automatically extract and use information from texts. This thesis addresses two key aspects, biomedical text mining and gene expression data analysis, with the focus on providing high-quality methods and data that contribute to the development of integrated analysis approaches. The work is structured in three parts. Each part begins by providing the relevant background, and each chapter describes the developed methods as well as applications and results. Part I deals with biomedical text mining: Chapter 2 summarizes the relevant background of text mining; it describes text mining fundamentals, important text mining tasks, applications and particularities of text mining in the biomedical domain, and evaluation issues. In Chapter 3, a method for generating high-quality gene and protein name dictionaries is described. The analysis of the generated dictionaries revealed important properties of individual nomenclatures and the used databases (Fundel and Zimmer, 2006). The dictionaries are publicly available via a Wiki, a web service, and several client applications (Szugat et al., 2005). In Chapter 4, methods for the dictionary-based recognition of gene and protein names in texts and their mapping onto unique database identifiers are described. These methods make it possible to extract information from texts and to integrate text-derived information with data from other sources. Three named entity identification systems have been set up, two of them building upon the previously existing tool ProMiner (Hanisch et al., 2003). All of them have shown very good performance in the BioCreAtIvE challenges (Fundel et al., 2005a; Hanisch et al., 2005; Fundel and Zimmer, 2007). In Chapter 5, a new method for relation extraction (Fundel et al., 2007) is presented. It was applied on the largest collection of biomedical literature abstracts, and thus a comprehensive network of human gene and protein relations has been generated. A classification approach (Küffner et al., 2006) can be used to specify relation types further; e. g., as activating, direct physical, or gene regulatory relation. Part II deals with gene expression data analysis: Gene expression data needs to be processed so that differentially expressed genes can be identified. Gene expression data processing consists of several sequential steps. Two important steps are normalization, which aims at removing systematic variances between measurements, and quantification of differential expression by p-value and fold change determination. Numerous methods exist for these tasks. Chapter 6 describes the relevant background of gene expression data analysis; it presents the biological and technical principles of microarrays and gives an overview of the most relevant data processing steps. Finally, it provides a short introduction to osteoarthritis, which is in the focus of the analyzed gene expression data sets. In Chapter 7, quality criteria for the selection of normalization methods are described, and a method for the identification of differentially expressed genes is proposed, which is appropriate for data with large intensity variances between spots representing the same gene (Fundel et al., 2005b). Furthermore, a system is described that selects an appropriate combination of feature selection method and classifier, and thus identifies genes which lead to good classification results and show consistent behavior in different sample subgroups (Davis et al., 2006). The analysis of several gene expression data sets dealing with osteoarthritis is described in Chapter 8. This chapter contains the biomedical analysis of relevant disease processes and distinct disease stages (Aigner et al., 2006a), and a comparison of various microarray platforms and osteoarthritis models. Part III deals with integrated approaches and thus provides the connection between parts I and II: Chapter 9 gives an overview of different types of integrated data analysis approaches, with a focus on approaches that integrate gene expression data with manually compiled data, large-scale networks, or text mining. In Chapter 10, a method for the identification of genes which are consistently regulated and have a coherent literature background (Küffner et al., 2005) is described. This method indicates how gene and protein name identification and gene expression data can be integrated to return clusters which contain genes that are relevant for the respective experiment together with literature information that supports interpretation. Finally, in Chapter 11 ideas on how the described methods can contribute to current research and possible future directions are presented

Pathophysiology of Skeletal Disease

These studies have resulted in substantial advances in the field of osteoporosis and osteoarthritis research. Crucial contributions to pivotal osteoporosis GWAS identified 518 bone mineral density and 13 fracture loci that now account for over 20% of the population variance in bone density. We identified over 300 novel eBMD loci and validated numerous genes, including (i) transferrin receptor-2 as a novel regulator of bone mass acting via BMP/p38MAPK/Wnt signalling, (ii) over 100 genes that determine bone mass, quality and strength comprising enzymes, ion and amino acid transporters, cell cycle regulators, transcription factors and modulators of non-canonical Wnt signalling, and (iii) age-specific and sexually dimorphic genetic effects on bone mineral density. Recent discoveries include (i) identification of a new cell type with a unique transcriptome, termed the “osteomorph”. Bone resorbing multinucleated osteoclasts undergo cycles of cell fission and fusion, recycling via osteomorphs in the bone marrow to regulate osteoclast motility and dynamic bone remodelling in vivo, (ii) elucidation of a transcriptome map of genes expressed in osteocytes, the master regulatory cells in bone. Osteocyte signature genes correlate closely with loci identified in human GWAS and in the nosology of monogenic skeletal disorders, establishing the cellular pathogenesis of various skeletal diseases, and (iii) development of novel imaging methods in osteoarthritis disease models and generation of the first multi ‘omic molecular QTL map of human disease to accelerate causative gene discovery in osteoarthritis. This multidisciplinary and international approach is transformative and has resulted in a comprehensive atlas of human and murine genetic influences on bone and joint disease that offer novel insights into the pathophysiology of osteoporosis and osteoarthritis with exciting opportunities for biomarker discovery and drug development. This body of work has resulted in invitations to contribute seminal chapters in major international textbooks, including (i) Genetics of Bone Biology and Skeletal Disease (2018) and (ii) Osteoporosis (2020), and Plenary Lectures to the (i) 21st World Congress on Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (IOFESCEO WCO), (ii) ASBMR Bone Turnover Markers Annual Meeting, and (iii) 9th Congress of the Romanian Society of Osteoporosis and Musculoskeletal Diseases (all in 2021). My work was recognised by the European Calcified Tissue Society Steven Boonen Clinical Research Award (2018), and I was elected Fellow of the Academy of Medical Sciences (2019), Member of Academia Europaea (2021) and Fellow of the Association of Physicians of Great Britain & Ireland (2021).Open Acces