Insights from multi-omics integration in complex disease primary tissues.

Genome-wide association studies (GWAS) have provided insights into the genetic basis of complex diseases. In the next step, integrative multi-omics approaches can characterize molecular profiles in relevant primary tissues to reveal the mechanisms that underlie disease development. Here, we highlight recent progress in four examples of complex diseases generated by integrative studies: type 2 diabetes (T2D), osteoarthritis, Alzheimer's disease (AD), and systemic lupus erythematosus (SLE). High-resolution methodologies such as single-cell and spatial omics techniques will become even more important in the future. Furthermore, we emphasize the urgent need to include as yet understudied cell types and increase the diversity of studied populations.

Genetic underpinning of the comorbidity between type 2 diabetes and osteoarthritis.

Multimorbidity is a rising public health challenge with important implications for health management and policy. The most common multimorbidity pattern is the combination of cardiometabolic and osteoarticular diseases. Here, we study the genetic underpinning of the comorbidity between type 2 diabetes and osteoarthritis. We find genome-wide genetic correlation between the two diseases and robust evidence for association-signal colocalization at 18 genomic regions. We integrate multi-omics and functional information to resolve the colocalizing signals and identify high-confidence effector genes, including FTO and IRX3, which provide proof-of-concept insights into the epidemiologic link between obesity and both diseases. We find enrichment for lipid metabolism and skeletal formation pathways for signals underpinning the knee and hip osteoarthritis comorbidities with type 2 diabetes, respectively. Causal inference analysis identifies complex effects of tissue-specific gene expression on comorbidity outcomes. Our findings provide insights into the biological basis for the type 2 diabetes-osteoarthritis disease co-occurrence.

NOS inhibition reverses TLR2-induced chondrocyte dysfunction and attenuates age-related osteoarthritis.

Osteoarthritis (OA) is a joint disease featuring cartilage breakdown and chronic pain. Although age and joint trauma are prominently associated with OA occurrence, the trigger and signaling pathways propagating their pathogenic aspects are ill defined. Following long-term catabolic activity and traumatic cartilage breakdown, debris accumulates and can trigger Toll-like receptors (TLRs). Here we show that TLR2 stimulation suppressed the expression of matrix proteins and induced an inflammatory phenotype in human chondrocytes. Further, TLR2 stimulation impaired chondrocyte mitochondrial function, resulting in severely reduced adenosine triphosphate (ATP) production. RNA-sequencing analysis revealed that TLR2 stimulation upregulated nitric oxide synthase 2 (NOS2) expression and downregulated mitochondria function-associated genes. NOS inhibition partially restored the expression of these genes, and rescued mitochondrial function and ATP production. Correspondingly, Nos2-/- mice were protected from age-related OA development. Taken together, the TLR2-NOS axis promotes human chondrocyte dysfunction and murine OA development, and targeted interventions may provide therapeutic and preventive approaches in OA.

A molecular map of long non-coding RNA expression, isoform switching and alternative splicing in osteoarthritis.

Osteoarthritis is a prevalent joint disease and a major cause of disability worldwide with no curative therapy. Development of disease-modifying therapies requires a better understanding of the molecular mechanisms underpinning disease. A hallmark of osteoarthritis is cartilage degradation. To define molecular events characterizing osteoarthritis at the whole transcriptome level, we performed deep RNA sequencing in paired samples of low- and high-osteoarthritis grade knee cartilage derived from 124 patients undergoing total joint replacement. We detected differential expression between low- and high-osteoarthritis grade articular cartilage for 365 genes and identified a 38-gene signature in osteoarthritis cartilage by replicating our findings in an independent dataset. We also found differential expression for 25 novel long non-coding RNA genes (lncRNAs) and identified potential lncRNA interactions with RNA-binding proteins in osteoarthritis. We assessed alterations in the relative usage of individual gene transcripts and identified differential transcript usage for 82 genes, including ABI3BP, coding for an extracellular matrix protein, AKT1S1, a negative regulator of the mTOR pathway and TPRM4, coding for a transient receptor potential channel. We further assessed genome-wide differential splicing, for the first time in osteoarthritis, and detected differential splicing for 209 genes, which were enriched for extracellular matrix, proteoglycans and integrin surface interactions terms. In the largest study of its kind in osteoarthritis, we find that isoform and splicing changes, in addition to extensive differences in both coding and non-coding sequence expression, are associated with disease and demonstrate a novel layer of genomic complexity to osteoarthritis pathogenesis.

Primary osteoarthritis chondrocyte map of chromatin conformation reveals novel candidate effector genes.

OBJECTIVES: Osteoarthritis is a complex disease with a 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 is essential for identifying long-range contacts between distant genomic features (e.g., between genes and regulatory elements), in a tissue-specific manner. Here, we generate the first whole genome chromosome conformation analysis (Hi-C) map of primary osteoarthritis chondrocytes and identify novel candidate effector genes for the disease. METHODS: Primary chondrocytes collected from 8 patients with knee osteoarthritis underwent Hi-C analysis to link chromosomal structure to genomic sequence. The identified loops were then combined with osteoarthritis GWAS results and epigenomic data from primary knee osteoarthritis chondrocytes to identify variants involved in gene regulation via enhancer-promoter interactions. RESULTS: We identified 345 genetic variants residing within chromatin loop anchors that are associated with 77 osteoarthritis GWAS signals. Ten of these variants reside directly in enhancer regions of 10 newly described active enhancer-promoter loops, identified with multiomics analysis of publicly available chromatin immunoprecipitation sequencing (ChIP-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) data from primary knee chondrocyte cells, pointing to two new candidate effector genes SPRY4 and PAPPA (pregnancy-associated plasma protein A) as well as further support for the gene SLC44A2 known to be involved in osteoarthritis. For example, PAPPA is directly associated with the turnover of insulin-like growth factor 1 (IGF-1) proteins, and IGF-1 is an important factor in the repair of damaged chondrocytes. CONCLUSIONS: We have constructed the first Hi-C map of primary human chondrocytes and have made it available as a resource for the scientific community. By integrating 3D genomics with large-scale genetic association and epigenetic data, we identify novel candidate effector genes for osteoarthritis, which enhance our understanding of disease and can serve as putative high-value novel drug targets.

Insights into the molecular landscape of osteoarthritis in human tissues.

PURPOSE OF REVIEW: To provide an overview of recent developments in the field of osteoarthritis research with a focus on insights gleaned from the application of different -omic technologies. RECENT FINDINGS: We searched for osteoarthritis-relevant studies focusing on transcriptomics, epigenomics, proteomics and metabolomics, published since November of 2019. Study designs showed a trend towards characterizing the genomic profile of osteoarthritis-relevant tissues with high resolution, for example either by using single-cell technologies or by considering several -omic levels and disease stages. SUMMARY: Multitissue interactions (cartilage-subchondral bone; cartilage-synovium) are prevalent in the pathophysiology of osteoarthritis, which is characterized by substantial matrix remodelling in an inflammatory milieu. Subtyping approaches using -omic technologies have contributed to the identification of at least two osteoarthritis endotypes. Studies using data integration approaches have provided molecular maps that are tissue-specific for osteoarthritis and pave the way for expanding these data integration approaches towards a more comprehensive view of disease aetiopathogenesis.