Primary osteoarthritis chondrocyte map of chromatin conformation reveals novel candidate effector genes DOI Creative Commons
Norbert Bittner, Chenfu Shi, Danyun Zhao

и другие.

Annals of the Rheumatic Diseases, Год журнала: 2024, Номер 83(8), С. 1048 - 1059

Опубликована: Март 13, 2024

Osteoarthritis is a complex disease with huge public health burden. Genome-wide association studies (GWAS) have identified hundreds of osteoarthritis-associated sequence variants, but the effector genes underpinning these signals remain largely elusive. Understanding chromosome organisation in three-dimensional (3D) space essential for identifying long-range contacts between distant genomic features (e.g., and regulatory elements), tissue-specific manner. Here, we generate first whole genome conformation analysis (Hi-C) map primary osteoarthritis chondrocytes identify novel candidate disease.

Язык: Английский

FinnGen provides genetic insights from a well-phenotyped isolated population DOI Creative Commons
Mitja Kurki,

Juha Karjalainen,

Priit Palta

и другие.

Nature, Год журнала: 2023, Номер 613(7944), С. 508 - 518

Опубликована: Янв. 18, 2023

Abstract Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These survived the founding bottleneck rather than being distributed over large ultrarare variants. Although this effect is well established Mendelian genetics, its value common disease genetics less explored 1,2 . FinnGen aims to study genome and national health register data 500,000 Finnish individuals. Given relatively high median age participants (63 years) substantial fraction hospital-based recruitment, enriched for end points. Here we analyse from 224,737 15 diseases that have previously been investigated genome-wide association studies (GWASs). We also include meta-analyses biobank Estonia United Kingdom. identified 30 new associations, primarily variants, population. A GWAS 1,932 2,733 significant associations (893 phenome-wide (PWS), P 2.6 × 10 –11 ) at 2,496 (771 PWS) independent loci with 807 (247 Among these, fine-mapping implicated 148 (73 coding associated 83 (42 Moreover, 91 (47 had an <5% non-Finnish European individuals, which 62 (32 were by more twofold Finland. findings demonstrate power bottlenecked populations find entry points into biology through low-frequency, impact

Язык: Английский

Процитировано

2347

Osteoarthritis: pathogenic signaling pathways and therapeutic targets DOI Creative Commons
Qing Yao, Xiaohao Wu, Chu Tao

и другие.

Signal Transduction and Targeted Therapy, Год журнала: 2023, Номер 8(1)

Опубликована: Фев. 3, 2023

Abstract Osteoarthritis (OA) is a chronic degenerative joint disorder that leads to disability and affects more than 500 million population worldwide. OA was believed be caused by the wearing tearing of articular cartilage, but it now commonly referred as whole-joint initiated with biochemical cellular alterations in synovial tissues, which histological structural changes ends up whole tissue dysfunction. Currently, there no cure for OA, partly due lack comprehensive understanding pathological mechanism initiation progression disease. Therefore, better signaling pathways key molecules involved pathogenesis crucial therapeutic target design drug development. In this review, we first summarize epidemiology including its prevalence, incidence burdens, risk factors. We then focus on roles regulation pathways, such Wnt/β-catenin, NF-κB, focal adhesion, HIFs, TGFβ/ΒΜP FGF regulators AMPK, mTOR, RUNX2 onset development OA. addition, factors associated MMPs, ADAMTS/ADAMs, PRG4, are discussed detail. Finally, provide updates current clinical therapies trials biological treatments drugs Research advances basic knowledge cartilage biology will have significant impact translational value developing strategies.

Язык: Английский

Процитировано

618

Insights from multi-omics integration in complex disease primary tissues DOI Creative Commons
Peter Kreitmaier, Georgia Katsoula, Eleftheria Zeggini

и другие.

Trends in Genetics, Год журнала: 2022, Номер 39(1), С. 46 - 58

Опубликована: Сен. 19, 2022

Genome-wide association studies (GWAS) have revealed the genetic basis of complex diseases. Integrative investigating multi-omics data disease-relevant primary tissues are needed to refine these insights.By highlighting recent integrative in relevant four distinct diseases (type 2 diabetes, osteoarthritis, Alzheimer's disease, and systemic lupus erythematosus), we outline usefulness this approach across disease types.Multi-omics approaches extended our biological understanding (e.g., functional interpretation GWAS signals, construction new molecular maps) potential clinically insights patient stratification, biomarker identification). provided into In next step, can characterize profiles reveal mechanisms that underlie development. Here, highlight progress examples generated by studies: type diabetes (T2D), (AD), erythematosus (SLE). High-resolution methodologies such as single-cell spatial omics techniques will become even more important future. Furthermore, emphasize urgent need include yet understudied cell types increase diversity studied populations. Complex driven a combination multiple environmental factors. Due their high prevalence osteoarthritis: 40% over age 70 years [1.Cui A. et al.Global, regional prevalence, incidence risk factors knee osteoarthritis population-based studies.EClinicalMedicine. 2020; 29–30100587Abstract Full Text PDF PubMed Scopus (327) Google Scholar]; diabetes: 6.28% world population [2.Khan M.A.B. al.Epidemiology - global burden forecasted trends.J. Epidemiol. Glob. Health. 10: 107-111Crossref Scholar]), represent substantial for public health systems [3.Vos T. al.Global 369 injuries 204 countries territories, 1990–2019: systematic analysis Global Burden Disease Study 2019.Lancet. 396: 1204-1222Abstract (4846) Scholar]. context an aging population, is predicted future, underlining importance developing effective personalized treatment methods, including discovery novel drug targets (especially drugs been approved another context, referred repurposing), identification biomarkers, improved stratification [4.Zeggini E. al.Translational genomics precision medicine: moving from lab clinic.Science. 2019; 365: 1409-1413Crossref (86) identified loci implicated much-needed architecture [5.Buniello al.The NHGRI-EBI Catalog published genome-wide studies, targeted arrays summary statistics 2019.Nucleic Acids Res. 47: D1005-D1012Crossref (1935) However, translating findings clinical applications remains challenging Issues strong linkage disequilibrium between variants on haplotypes (the actual causal variant locus often elusive) or effector genes variants, particularly noncoding regions (see Glossary). Multi-omics human provide types, thus revealing beyond those derived studies. This information contribute overcoming current challenges translational efforts (Figure 1, Key figure). Briefly, be integrated with results identify target using inference Mendelian randomization [6.Hemani G. MR-Base platform supports phenome.eLife. 2018; 7e34408Crossref (1872) Scholar] colocalization [7.Giambartolomei C. al.Bayesian test colocalisation pairs statistics.PLoS Genet. 2014; 10e1004383Crossref (1036) Scholar,8.Giambartolomei al.A Bayesian framework trait statistics.Bioinformatics. 34: 2538-2545Crossref (113) Scholar]). improve characterization, especially residing sequence. Indeed, computational intersections datasets [e.g., chromatin immunoprecipitation followed sequencing (ChIP-seq), assay transposase-accessible (ATAC-seq), etc.] found some traits, tend reside enriched within regulatory sequence [9.Boer C.G. al.Deciphering genetics 826,690 individuals 9 populations.Cell. 2021; 184: 4784-4818.e17Abstract (84) Scholar, 10.Wightman D.P. study 1,126,563 identifies disease.Nat. 53: 1276-1282Crossref (199) 11.Viñuela al.Genetic effects gene expression pancreatic islets implications T2D.Nat. Commun. 11: 4912Crossref (53) Functional largely enabled developments high-throughput methods enable tissue-specific profiling several layers, DNA methylation, accessibility, transcript protein level. large projects like GTEx [12.GTEx Consortium The atlas tissues.Science. 369: 1318-1330Crossref Scholar], ENCODE [13.Dunham I. al.An encyclopedia elements genome.Nature. 2012; 489: 57-74Crossref (11513) ROADMAP [14.Roadmap Epigenomics al.Integrative 111 reference epigenomes.Nature. 2015; 518: 317-330Crossref (3792) Human Cell Atlas [15.Regev Atlas.eLife. 2017; 6e27041Crossref (1102) made genome-wide, maps publicly available, providing well-established resources landscapes (Box 1). These large, available investigation biological, layers [16.Hasin Y. al.Multi-omics disease.Genome Biol. 18: 83Crossref (1038) refined link disease.Box 1Public dataInternational collaborations which serve data, example, follow-up signals.Launched 2010, databasei provides catalog splicing 49 tissues, collected postmortem samples 838 (version 8) Scholar].ENCODEii was established 2003 pilot project describe mouse genomes, initially focused 1% genome [72.ENCODE Project Identification project.Nature. 2007; 447: 799-816Crossref (4183) but has expanded whole genome. version includes RNA transcription, binding, modification replication timing [73.ENCODE Expanded encyclopaedias genomes.Nature. 583: 699-710Crossref (625) It describes 926 535 humans 339 815 candidate cis-regulatory elements.Roadmapiii presents epigenomic (further 16 ENCODE, 127 total). comprises histone patterns, Scholar].The (HCA) international collaboration aims generate at resolution For one HCA-associated investigated 500 000 cells 400 24 organs [74.Tabula Sapiens Tabula Sapiens: multiple-organ, transcriptomic humans.Science. 2022; 376eabl4896Google HPC coordination platformiv currently than 26 million 38 donors (7 July 2022).Furthermore, there databases disease-specific information. Musculoskeletal Knowledge Portalv genomic musculoskeletal traits [75.Westendorf J.J. Portal: improving access data.Nat. Rev. Rheumatol. 1-2Crossref (3) hosts 301 281 traits.Similarly, Type Diabetes Portalvi T2D-relevant (349 datasets, 347 traits). Other T2D portals Translational Pancreatic Islet Genotype Tissue-Expression Resource (TIGER), eQTL islet [26.Alonso L. al.TIGER: variation landscape islets.Cell Rep. 37109807Abstract (25) Epigenome Atlasvii [24.Chiou J. al.Single-cell accessibility type- state-specific programs risk.Nat. 455-466Crossref Scholar].For AD, AD Portal initiative makes AD-relevant accessible [76.Greenwood A.K. repository multi-omic aging.Curr. Protoc. Hum. 108e105PubMed International signals. Launched ENCODEii elements. Roadmapiii 2022). traits. Similarly, addition, (multi-)omics applied tissue 2), is, patients nondisease donors. Using may not when peripheral cellular models.Box 2Analyses associate diseasesA standard conduct differential analyses, cases controls. similar case–control GWAS. contrast signals estimated play role (and vice versa, genotypes affected because they form conception), changes features (RNA abundances, marks, states) could consequences rather driving disease. Thus, analyses markers necessarily causally involved interest.In integrate developed A example integration matching influence levels gene, termed quantitative (eQTLs), scale combined drivers likely genes) through exert tissue. diseases, high-confidence promising [77.King E.A. al.Are support twice approved? Revised estimates impact probability approval.PLoS 15e1008489Crossref (215) strategies infer networks [78.Ogris al.Versatile knowledge guided network method prioritizing key data.Sci. 6806Crossref (7) estimate low-dimensional representations stratify [79.Argelaguet R. al.MOFA+: statistical comprehensive multi-modal data.Genome 21: 111Crossref (170) interest. increased progression stages. disease-affected reflect unrelated processes less prevention (but still treatment), whereas pre-diseased help elucidate pathomechanism, prevention. review, cover 3) 1 Table affect different pose challenges: (i) heterogeneous metabolic difficult access. (ii) Osteoarthritis joint disorder included databases. (iii) neurodegenerative affects brain, organ only post-mortem. (iv) SLE autoimmune heterogeneity.Box 3The largest erythematosusGWAS polygenic T2D, SLE.In date comprised 889 180 834 159 055 controls [80.Mahajan al.Multi-ancestry highlights power diverse populations translation.Nat. 54: 560-572Crossref (73) Of these, major part European descent (51.1%).For 826 690 (177 517 649 173 controls), 99.3% ancestry 126 563 (90 338 cases, 036 225 controls) [10.Wightman Another fewer total (n = 788 989), higher number 326) [48.Bellenguez al.New etiology related dementias.Nat. 412-436Crossref (246) Both only.The performed 208 370 (13 377 194 993 all East Asian [59.Yin X. al.Meta-analysis 208370 Asians 113 susceptibility erythematosus.Ann. Rheum. Dis. 80: 632-640Crossref (54) Scholar].Table 1Overview measured studiesaAbbreviations: HiChIP, Hi-C immunoprecipitation; mux-seq, multiplexed sequencing; scTHS-seq, sc-transposome hypersensitive sites snDrop-seq, single-nucleus Droplet-based WGBS, whole-genome bisulfite sequencing.DiseasePrimary tissueTypes approachesOmicsType diabetesPancreatic isletsBulk:450k, WGBSATAC-seqHi-CChIP-seqpcHi-CRNA-seqMass spectrometrySingle-cell/nucleus:snATAC-seqscRNA-seqDNA methylationChromatin accessibilityChromatin conformationProtein–DNA interactomePromoter capture conformationTranscriptomicsProteomicsSingle-nucleus (sn) accessibilitySingle-cell (sc) transcriptomicsOsteoarthritisCartilageBulk:450k, EPICATAC-seqRNA-seqMass spectrometryMethylationChromatin accessibilityTranscriptomicsProteomicsSynoviumBulk:RNA-seqMass spectrometryTranscriptomicsProteomicsAlzheimer's diseaseBrainBulk:ATAC-seqHiChIPRNA-seqChIP-seqMass spectrometrySingle-cell/nucleus:scATAC-seqscRNA-seqsnDrop-seqscTHS-seqChromatin accessibilityEnhancer connectomeTranscriptomicsProtein–DNA interactomeProteomics, phosphoproteomic, lipidomicssc accessibilitysc transcriptomicssn transcriptomicsn accessibilitySystemic erythematosusBloodSingle cellmux-seqsc transcriptomicsBulk:RNA-seq, microarrayMass spectrometryNMR spectroscopyTranscriptomicsProteomics, metabolomicsMetabolomicsa Abbreviations: sequencing. Open table tab SLE. (51.1%). only. affecting 450 people worldwide characterized impairment insulin secretion signaling, carbohydrate, lipid, metabolism [17.Mahajan al.Fine-mapping single-variant high-density imputation islet-specific epigenome maps.Nat. 50: 1505-1513Crossref (859) Scholar,18.Scott R.A. Europeans.Diabetes. 66: 2888-2902Crossref (440) two defective endocrine beta lack/reduced response insulin-sensitive [19.Galicia-Garcia U. al.Pathophysiology mellitus.Int. Mol. Sci. 21E6275Crossref (567) Over 700 >90% mapped sequences majority (which explain 19% [20.Polfus L.M. characterization populations.HGG Adv. 2100029PubMed Scholar]) mainly secretion. To end, pancreas vital gain insight regulation. Despite being access, linked expression. constitute valuable reference, specif

Язык: Английский

Процитировано

91

Sirt6 attenuates chondrocyte senescence and osteoarthritis progression DOI Creative Commons
Ming-liang Ji, Hua Jiang, Zhuang Li

и другие.

Nature Communications, Год журнала: 2022, Номер 13(1)

Опубликована: Дек. 10, 2022

Sirt6 has been implicated as a key regulator in aging-related diseases, including osteoarthritis. However, its functional role and molecular mechanism chondrocyte senescence osteoarthritis pathophysiology remain largely undefined. Here we show that deficiency exaggerates progression, whereas intra-articular injection of adenovirus-Sirt6 markedly attenuates surgical destabilization medial meniscus-induced Mechanistically, can directly interact with STAT5 deacetylate STAT5, thus inhibiting the IL-15/JAK3-induced translocation from cytoplasm to nucleus, which inactivates IL-15/JAK3/STAT5 signaling. Mass spectrometry revealed deacetylated conserved lysine 163 on STAT5. Mutation arginine abolished regulatory effect Sirt6. In vivo, specific ablation chondrocytes exacerbated Pharmacological activation substantially alleviated senescence. Taken together, by Targeting represents promising new approach for

Язык: Английский

Процитировано

88

Causality of genetically determined metabolites and metabolic pathways on osteoarthritis: a two-sample mendelian randomization study DOI Creative Commons

Yifei Gu,

Qianmei Jin,

Jinquan Hu

и другие.

Journal of Translational Medicine, Год журнала: 2023, Номер 21(1)

Опубликована: Май 31, 2023

Abstract Background Osteoarthritis (OA) is one of the most prevalent musculoskeletal diseases and leading cause pain disability in aged population. However, underlying biological mechanism has not been fully understood. This study aims to reveal causal effect circulation metabolites on OA susceptibility. Methods A two-sample Mendelian Randomization (MR) analysis was performed estimate causality GDMs OA. genome-wide association (GWAS) 486 used as exposure, whereas 8 different phenotypes, including any-site (All OA), knee and/or hip (knee/hip OA, spine finger thumb (hand were set outcomes. Inverse-variance weighted (IVW) for calculating estimates. weight mode, median, MR-egger, MR-PRESSO sensitive analysis. Furthermore, metabolic pathway via web-based Metaconflict 4.0. All statistical analyses R software. Results In this MR analysis, a total 235 causative associations between phenotypes observed. After false discovery rate (FDR) correction 9 robust 7 (e.g., arginine, kynurenine, isovalerylcarnitine) 5 finally identified. Additionally, eleven significant pathways 4 identified by Conclusion The finding our suggested that can be considered useful circulating biomarkers screening prevention clinical practice, also serve candidate molecules future exploration drug target selection.

Язык: Английский

Процитировано

62

Unveiling inflammatory and prehypertrophic cell populations as key contributors to knee cartilage degeneration in osteoarthritis using multi-omics data integration DOI Creative Commons
Yue Fan,

Xuzhao Bian,

Xiaogao Meng

и другие.

Annals of the Rheumatic Diseases, Год журнала: 2024, Номер 83(7), С. 926 - 944

Опубликована: Фев. 7, 2024

Objectives Single-cell and spatial transcriptomics analysis of human knee articular cartilage tissue to present a comprehensive transcriptome landscape osteoarthritis (OA)-critical cell populations. Methods RNA sequencing spatially resolved transcriptomic technology have been applied characterise the cellular heterogeneity which were collected from 8 OA donors, 3 non-OA control total 19 samples. The novel chondrocyte population marker genes interest validated by immunohistochemistry staining, quantitative real-time PCR, etc. OA-critical populations through integrative analyses publicly available bulk data large-scale genome-wide association studies. Results We identified 33 population-specific that define 11 populations, including 9 known 2 new is, pre-inflammatory (preInfC) inflammatory (InfC). findings make this an important addition literature include: (1) InfC activates mediator MIF-CD74; (2) prehypertrophic (preHTC) hypertrophic (HTC) are potentially populations; (3) most OA-associated differentially expressed reside in surface superficial zone; (4) prefibrocartilage (preFC) is major contributor stratification patients with OA, resulting both inflammatory-related subtype non-inflammatory-related subtype. Conclusions Our results highlight InfC, preHTC, preFC HTC as potential target for therapy. Also, we conclude profiling those might be used stratify patient defining cohorts clinical trials precision medicine.

Язык: Английский

Процитировано

43

Engineering approaches for RNA-based and cell-based osteoarthritis therapies DOI
Carlisle R. DeJulius,

Bonnie L. Walton,

Juan M. Colazo

и другие.

Nature Reviews Rheumatology, Год журнала: 2024, Номер 20(2), С. 81 - 100

Опубликована: Янв. 22, 2024

Язык: Английский

Процитировано

30

The role of obesity and adipose tissue dysfunction in osteoarthritis pain DOI
Marie Binvignat, Jérémie Sellam, Françis Berenbaum

и другие.

Nature Reviews Rheumatology, Год журнала: 2024, Номер 20(9), С. 565 - 584

Опубликована: Авг. 7, 2024

Язык: Английский

Процитировано

29

Clinical phenotypes, molecular endotypes and theratypes in OA therapeutic development DOI
Ali Mobasheri, Richard F. Loeser

Nature Reviews Rheumatology, Год журнала: 2024, Номер 20(9), С. 525 - 526

Опубликована: Май 17, 2024

Язык: Английский

Процитировано

20

A multi-omic atlas of human embryonic skeletal development DOI Creative Commons
Ken To, Lijiang Fei, J. Patrick Pett

и другие.

Nature, Год журнала: 2024, Номер 635(8039), С. 657 - 667

Опубликована: Ноя. 20, 2024

Human embryonic bone and joint formation is determined by coordinated differentiation of progenitors in the nascent skeleton. The cell states, epigenetic processes key regulatory factors that underlie lineage commitment these cells remain elusive. Here we applied paired transcriptional profiling approximately 336,000 nucleus droplets spatial transcriptomics to establish a multi-omic atlas human cranium development between 5 11 weeks after conception. Using combined modelling data, characterized regionally distinct limb cranial osteoprogenitor trajectories across skeleton further described networks govern intramembranous endochondral ossification. Spatial localization clusters our situ sequencing data using new tool, ISS-Patcher, revealed mechanisms progenitor zonation during formation. Through trajectory analysis, predicted potential non-canonical cellular origins for chondrocytes from Schwann cells. We also introduce SNP2Cell, tool link cell-type-specific polygenic traits such as osteoarthritis. osteolineage here, simulated silico perturbations genes cause monogenic craniosynostosis implicate states disease mechanisms. This work forms detailed dynamic cartilage maturation advances fundamental understanding cell-fate determination skeletal development.

Язык: Английский

Процитировано

20