2017-10-31
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Following completion of the Human Genome Project, the generation of massive genomic information has rapidly transformed the field of biomedical research into the post-genomic era. Post-genomics, or so-called system biology, studies the expression and functions of the entire set of genes and proteins present in a whole genome by using high-throughput methodologies including microarray, transcriptomics, proteomics, and metabolomics. With thousands of genes and proteins being analyzed simultaneously, these omics-based technology platforms have significantly contributed to the discovery of the new crop of biomarkers over the past decade. The post-genomic strategies have been applied in various fields, including OA [2].
Transcriptomic Osteoarthritis Biomarkers
Transcriptome refers to all the ribonucleic acids (RNAs) that are transcribed from the genome containing messenger RNAs (mRNAs), ribosomal RNAs (rRNAs), transfer RNAs (tRNAs), and noncoding RNAs. Transcriptomic analysis has been performed through gene microarrays or RNA sequencing (RNA-Seq) to quantify the abundance of all transcripts in a particular biological specimen [4].
Gene microarrays have been widely used in gene expression studies and have proven to be a powerful tool to identify candidate RNA biomarkers for various pathological conditions including OA. Geyer et al. performed a transcriptomic analysis of affected versus intact articular cartilage from the same joint using high- density synthetic oligonucleotide hybridization arrays, and 411 transcripts out of 54,675 probes appeared to be differentially expressed. Of these, 6 genes were upregulated in the affected cartilage of all patients, including insulin-like growth factor-binding protein 3, decay-accelerating factor, and complement factor I. The Research Arthritis and Articular Cartilage (RAAK) study which involved a larger patient cohort was carried out to determine the genome- wide gene expression in 33 pairs of matched OA affected and intact cartilage from the same joint of patients. About 1,717 genes were found to be differentially expressed, and 18 were present with a change of twofold or higher in OA affected cartilage compared with preserved cartilage [19].
Comparing gene expression at damaged focal areas of cartilage to those preserved areas provides information of dynamic changes of genes and pathways involved in OA progression. However, macroscopic assessment of damaged or preserved cartilage is relatively subjective and less accurate, which may partially explain the low consistency of the differentially expressed genes between studies with similar design using comparable tissues [9].
Transcriptome analysis has generated valuable information on the molecular
changes across the whole genome, which will improve our understanding of the complexity of OA phenotypes.
Proteomic Biomarkers
By studying the presence and functions of an entire set of proteins in a particular biological sample, proteomics is being increasingly applied in cartilage research and OA pathology. It also elucidates information regarding protein structure and interactions thereby providing mechanistic insight into disease pathogenesis and a new powerful tool for biomarker exploration. In OA research, proteomic studies have been applied to cartilage tissue, chondrocytes, synovial fluid, serum, urine, and culture supernatant, and have identified significant panels of novel candidate biomarkers [2].
Wu et al. measured the protein compositions in cartilage from OA and healthy donors and found 59 differently expressed proteins by liquid chromatography–mass spectrometry. In particular, HtrA1, a serine protease, was upregulated at high levels in OA cartilage. Another study by Guo et al. performed proteomics on cartilage extractions from individuals with and without OA and identified 16 differentially expressed proteins. Proteomic profiling of chondrocytes also revealed that 19 proteins were increased and 9 decreased significantly in OA cells compared to normal. This study indicated an impaired glycolytic metabolism and an increased stress response in OA chondrocytes, both of which have been reported previously to be implicated in cartilage degradation [1].
With the goal of searching for new OA biomarkers, intensive proteomic profiling studies have focused on bodily fluids from OA and non-OA individuals. Fernandez-Puente et al. measured protein levels in serum from 50 moderate OA patients, 50 severe OA patients, and 50 non-symptomatic controls using isobaric tags for relative and absolute quantitation and matrix-assisted laser desorption/ionization. They identified 349 total proteins in serum, and of these, 6 were modulated only in moderate OA, 13 only in severe OA, and 7 in both groups. In addition to COMP, most of these differentially expressed proteins were novel candidate biomarkers for OA including a few complement components, lipoproteins, von Willebrand factor, tetranectin, and lumican. Han et al. analyzed synovial fluid samples from 36 OA patients and 24 rheumatoid arthritis (RA) patients. Three protein peaks were identified and able to differentiate between OA and RA patients at a sensitivity of 89.4 % and a specificity of 91.2 % by artificial neural networks analysis. Ritter et al. performed a proteomic analysis of knee synovial fluid from 20 OA patients and 10 controls. Sixty-six proteins were differentially present in both OA and control synovial fluid. Analysis showed that these proteins were associated with the acutephase response pathway, the complement pathway, and the coagulation pathway. The complement pathway has been identified in numerous studies to play a critical role in the pathogenesis of OA and a potential biomarker [1].
While a considerable amount of candidate protein markers have been identified from proteomic studies, the studies are not sufficiently consistent. However, proteomics has emerged as a powerful approach to identify proteins in pathological conditions and to discover new potential biomarkers.
