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Classification of four distinct osteoarthritis subtypes with a knee joint tissue transcriptome atlas.

ABSTRACT: The limited molecular classifications and disease signatures of osteoarthritis (OA) impede the development of prediagnosis and targeted therapeutics for OA patients. To classify and understand the subtypes of OA, we collected three types of tissue including cartilage, subchondral bone, and synovium from multiple clinical centers and constructed an extensive transcriptome atlas of OA patients. By applying unsupervised clustering analysis to the cartilage transcriptome, OA patients were classified into four subtypes with distinct molecular signatures: a glycosaminoglycan metabolic disorder subtype (C1), a collagen metabolic disorder subtype (C2), an activated sensory neuron subtype (C3), and an inflammation subtype (C4). Through ligand-receptor crosstalk analysis of the three knee tissue types, we linked molecular functions with the clinical symptoms of different OA subtypes. For example, the Gene Ontology functional term of vasculature development was enriched in the subchondral bone-cartilage crosstalk of C2 and the cartilage-subchondral bone crosstalk of C4, which might lead to severe osteophytes in C2 patients and apparent joint space narrowing in C4 patients. Based on the marker genes of the four OA subtypes identified in this study, we modeled OA subtypes with two independent published RNA-seq datasets through random forest classification. The findings of this work contradicted traditional OA diagnosis by medical imaging and revealed distinct molecular subtypes in knee OA patients, which may allow for precise diagnosis and treatment of OA.

SUBMITTER: Yuan C

PROVIDER: S-EPMC7658991 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Classification of four distinct osteoarthritis subtypes with a knee joint tissue transcriptome atlas.

Yuan Chunhui C Pan Zongyou Z Zhao Kun K Li Jun J Sheng Zixuan Z Yao Xudong X Liu Hua H Zhang Xiaolei X Yang Yang Y Yu Dongsheng D Zhang Yu Y Xu Yuzi Y Zhang Zhi-Yong ZY Huang Tianlong T Liu Wanlu W Ouyang Hongwei H

Bone research 20201112 1

The limited molecular classifications and disease signatures of osteoarthritis (OA) impede the development of prediagnosis and targeted therapeutics for OA patients. To classify and understand the subtypes of OA, we collected three types of tissue including cartilage, subchondral bone, and synovium from multiple clinical centers and constructed an extensive transcriptome atlas of OA patients. By applying unsupervised clustering analysis to the cartilage transcriptome, OA patients were classified ...[more]

PMID: 33298863

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Project description:Objectives:The aim of this study was to provide a comprehensive understanding of alterations in messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs) in cartilage affected by osteoarthritis (OA). Methods:The expression profiles of mRNAs, lncRNAs, and circRNAs in OA cartilage were assessed using whole-transcriptome sequencing. Bioinformatics analyses included prediction and reannotation of novel lncRNAs and circRNAs, their classification, and their placement into subgroups. Gene ontology and pathway analysis were performed to identify differentially expressed genes (DEGs), differentially expressed lncRNAs (DELs), and differentially expressed circRNAs (DECs). We focused on the overlap of DEGs and targets of DELs previously identified in seven high-throughput studies. The top ten DELs were verified by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) in articular chondrocytes, both in vitro and in vivo. Results:In total, 739 mRNAs, 1152 lncRNAs, and 42 circRNAs were found to be differentially expressed in OA cartilage tissue. Among these, we identified 18 overlapping DEGs and targets of DELs, and the top ten DELs were screened by expression profile analysis as candidate OA-related genes. WISP2, ATF3, and CHI3L1 were significantly increased in both normal versus OA tissues and normal versus interleukin (IL)-1?-induced OA-like cell models, while ADAM12, PRELP, and ASPN were shown to be significantly decreased. Among the identified DELs, we observed higher expression of ENST00000453554 and MSTRG.99593.3, and lower expression of MSTRG.44186.2 and NONHSAT186094.1 in normal versus OA cells and tissues. Conclusion:This study revealed expression patterns of coding and noncoding RNAs in OA cartilage, which added sets of genes and noncoding RNAs to the list of candidate diagnostic biomarkers and therapeutic agents for OA patients.Cite this article: H. Li, H. H. Yang, Z. G. Sun, H. B. Tang, J. K. Min. Whole-transcriptome sequencing of knee joint cartilage from osteoarthritis patients. Bone Joint Res 2019;8:288-301. DOI: 10.1302/2046-3758.86.BJR-2018-0297.R1.

Project description:ObjectivesThe aim of this study was to provide a comprehensive understanding of alterations in messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs) in cartilage affected by osteoarthritis (OA).MethodsThe expression profiles of mRNAs, lncRNAs, and circRNAs in OA cartilage were assessed using whole-transcriptome sequencing. Bioinformatics analyses included prediction and reannotation of novel lncRNAs and circRNAs, their classification, and their placement into subgroups. Gene ontology and pathway analysis were performed to identify differentially expressed genes (DEGs), differentially expressed lncRNAs (DELs), and differentially expressed circRNAs (DECs). We focused on the overlap of DEGs and targets of DELs previously identified in seven high-throughput studies. The top ten DELs were verified by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) in articular chondrocytes, both in vitro and in vivo.ResultsIn total, 739 mRNAs, 1152 lncRNAs, and 42 circRNAs were found to be differentially expressed in OA cartilage tissue. Among these, we identified 18 overlapping DEGs and targets of DELs, and the top ten DELs were screened by expression profile analysis as candidate OA-related genes. WISP2, ATF3, and CHI3L1 were significantly increased in both normal versus OA tissues and normal versus interleukin (IL)-1β-induced OA-like cell models, while ADAM12, PRELP, and ASPN were shown to be significantly decreased. Among the identified DELs, we observed higher expression of ENST00000453554 and MSTRG.99593.3, and lower expression of MSTRG.44186.2 and NONHSAT186094.1 in normal versus OA cells and tissues.ConclusionThis study revealed expression patterns of coding and noncoding RNAs in OA cartilage, which added sets of genes and noncoding RNAs to the list of candidate diagnostic biomarkers and therapeutic agents for OA patients.Cite this article: H. Li, H. H. Yang, Z. G. Sun, H. B. Tang, J. K. Min. Whole-transcriptome sequencing of knee joint cartilage from osteoarthritis patients. Bone Joint Res 2019;8:290-303. DOI: 10.1302/2046-3758.87.BJR-2018-0297.R1.

Project description:BackgroundSynovial inflammation is associated with pain severity in patients with knee osteoarthritis (OA). The aim here was to determine in a population with knee OA, whether synovial tissue from areas associated with pain exhibited different synovial fibroblast subsets, compared to synovial tissue from sites not associated with pain. A further aim was to compare differences between early and end-stage disease synovial fibroblast subsets.MethodsPatients with early knee OA (n = 29) and end-stage knee OA (n = 22) were recruited. Patient reported pain was recorded by questionnaire and using an anatomical knee pain map. Proton density fat suppressed MRI axial and sagittal sequences were analysed and scored for synovitis. Synovial tissue was obtained from the medial and lateral parapatellar and suprapatellar sites. Fibroblast single cell RNA sequencing was performed using Chromium 10X and analysed using Seurat. Transcriptomes were functionally characterised using Ingenuity Pathway Analysis and the effect of fibroblast secretome on neuronal growth assessed using rat DRGN.FindingsParapatellar synovitis was significantly associated with the pattern of patient-reported pain in knee OA patients. Synovial tissue from sites of patient-reported pain exhibited a differential transcriptomic phenotype, with distinct synovial fibroblast subsets in early OA and end-stage OA. Functional pathway analysis revealed that synovial tissue and fibroblast subsets from painful sites promoted fibrosis, inflammation and the growth and activity of neurons. The secretome of fibroblasts from early OA painful sites induced greater survival and neurite outgrowth in dissociated adult rodent dorsal root ganglion neurons.InterpretationSites of patient-reported pain in knee OA exhibit a different synovial tissue phenotype and distinct synovial fibroblast subsets. Further interrogation of these fibroblast pathotypes will increase our understanding of the role of synovitis in OA joint pain and provide a rationale for the therapeutic targeting of fibroblast subsets to alleviate pain in patients.FundingThis study was funded by Versus Arthritis, UK (21530; 21812).

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Classification of four distinct osteoarthritis subtypes with a knee joint tissue transcriptome atlas.

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Classification of four distinct osteoarthritis subtypes with a knee joint tissue transcriptome atlas.

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