Identification of blood biomarkers of rheumatoid arthritis by transcript profiling of peripheral blood mononuclear cells from the rat collagen-induced arthritis model

Rheumatoid arthritis (RA) is a chronic debilitating autoimmune disease that results in joint destruction and subsequent loss of function. To better understand its pathogenesis and to facilitate the search for novel RA therapeutics, we profiled the rat model of collagen-induced arthritis (CIA) to discover and characterize blood biomarkers for RA. Peripheral blood mononuclear cells (PBMCs) were purified using a Ficoll gradient at various time points after type II collagen immunization for RNA preparation. Total RNA was processed for a microarray analysis using Affymetrix GeneChip technology. Statistical comparison analyses identified differentially expressed genes that distinguished CIA from control rats. Clustering analyses indicated that gene expression patterns correlated with laboratory indices of disease progression. A set of 28 probe sets showed significant differences in expression between blood from arthritic rats and that from controls at the earliest time after induction, and the difference persisted for the entire time course. Gene Ontology comparison of the present study with previous published murine microarray studies showed conserved Biological Processes during disease induction between the local joint and PBMC responses. Genes known to be involved in autoimmune response and arthritis, such as those encoding Galectin-3, Versican, and Socs3, were identified and validated by quantitative TaqMan RT-PCR analysis using independent blood samples. Finally, immunoblot analysis confirmed that Galectin-3 was secreted over time in plasma as well as in supernatant of cultured tissue synoviocytes of the arthritic rats, which is consistent with disease progression. Our data indicate that gene expression in PBMCs from the CIA model can be utilized to identify candidate blood biomarkers for RA.     

Data-driven analysis approach for biomarker discovery using molecular-profiling technologies

High-throughput molecular-profiling technologies provide rapid, efficient and systematic approaches to search for biomarkers. Supervised learning algorithms are naturally suited to analyse a large amount of data generated using these technologies in biomarker discovery efforts. The study demonstrates with two examples a data-driven analysis approach to analysis of large complicated datasets collected in high-throughput technologies in the context of biomarker discovery. The approach consists of two analytic steps: an initial unsupervised analysis to obtain accurate knowledge about sample clustering, followed by a second supervised analysis to identify a small set of putative biomarkers for further experimental characterization. By comparing the most widely applied clustering algorithms using a leukaemia DNA microarray dataset, it was established that principal component analysis-assisted projections of samples from a high-dimensional molecular feature space into a few low dimensional subspaces provides a more effective and accurate way to explore visually and identify data structures that confirm intended experimental effects based on expected group membership. A supervised analysis method, shrunken centroid algorithm, was chosen to take knowledge of sample clustering gained or confirmed by the first step of the analysis to identify a small set of molecules as candidate biomarkers for further experimentation. The approach was applied to two molecular-profiling studies. In the first study, PCA-assisted analysis of DNA microarray data revealed that discrete data structures exist in rat liver gene expression and correlated with blood clinical chemistry and liver pathological damage in response to a chemical toxicant diethylhexylphthalate, a peroxisome-proliferator-activator receptor agonist. Sixteen genes were then identified by shrunken centroid algorithm as the best candidate biomarkers for liver damage. Functional annotations of these genes revealed roles in acute phase response, lipid and fatty acid metabolism and they are functionally relevant to the observed toxicities. In the second study, 26 urine ions identified from a GC/MS spectrum, two of which were glucose fragment ions included as positive controls, showed robust changes with the development of diabetes in Zucker diabetic fatty rats. Further experiments are needed to define their chemical identities and establish functional relevancy to disease development.     

Protein Kinase C is a mediator of the synthesis and secretion of osteoprotegerin in osteoblast-like cells.

Osteoprotegerin (OPG) is a member of the TNF receptor superfamily and plays a critical role in the development of osteoclasts from precursor cells. OPG is produced by a variety of cells of mesenchymal origin and has been demonstrated to be present in osteoblasts and osteocytes. However, the mechanisms of regulation of OPG production and secretion are not known. Using a highly specific polyclonal antibody, we demonstrate that OPG is synthesized and secreted by osteoblast-like cells in culture. We further show that phorbol myristate acetate, an activator of protein kinase C, activated the secretion of OPG. Further, the increased secretion of OPG correlated well with a corresponding increase in OPG mRNA abundance. In addition, OPG promoter stably integrated into an osteoblast cell line was activated by phorbol myristate acetate. The increase in OPG expression was blocked by an inhibitor of protein kinase C, although the basal OPG expression was not altered. These results suggest that activation of the protein kinase C pathway may play a critical role in OPG expression.     

A proteomic analysis of adult rat bone reveals the presence of cartilage/chondrocyte markers

The non-mineral component of bone matrix consists of 90% collagenous, 10% non-collagenous proteins. These proteins regulate mineralization, growth, cell signaling and differentiation, and provide bone with its tensile strength. Expression of bone matrix proteins have historically been studied individually or in small numbers owing to limitations in analytical technologies. Current mass-spectrometric and separations technologies allow a global view of protein expression patterns in complex samples. To our knowledge, no proteome profile of bone matrix has yet been reported. Therefore, we have used mass spectrometry as a tool to generate a profile of proteins present in the extracellular matrix of adult rat bone. Overall, 108 and 25 proteins were identified with high confidence in the metaphysis and diaphysis, respectively, using a bottom up proteomic technique. Twenty-one of these proteins were present in both the metaphysis and diaphysis including the bone specific proteins, osteocalcin, type I collagen, osteopontin, osteoregulin, and bone sialoprotein. Interestingly, type II collagen, a protein thought to be exclusively expressed in cartilage, was identified in both the metaphysis and diaphysis. This observation was validated by Western blot. Additionally, the presence of aggrecan, another protein expressed in cartilage was identified in the bone matrix extracts by Western blot. The proteome profile generated using this technology represents an initial survey of the acid soluble proteins of bone matrix which provides a reference for the analysis of deviations from the normal composition due to perturbations or disease states.     

Analysis of pathway activity in primary tumors and NCI60 cell lines using gene expression profiling data

To determine cancer pathway activities in nine types of primary tumors and NCI60 cell lines, we applied an in silico approach by examining gene signatures reflective of consequent pathway activation using gene expression data. Supervised learning approaches predicted that the Ras pathway is active in ～70% of lung adenocarcinomas but inactive in most squamous cell carcinomas, pulmonary carcinoids, and small cell lung carcinomas. In contrast, the TGF-β, TNF-α, Src, Myc, E2F3, and β-catenin pathways are inactive in lung adenocarcinomas. We predicted an active Ras, Myc, Src, and/or E2F3 pathway in significant percentages of breast cancer, colorectal carcinoma, and gliomas. Our results also suggest that Ras may be the most prevailing oncogenic pathway. Additionally, many NCI60 cell lines exhibited a gene signature indicative of an active Ras, Myc, and/or Src, but not E2F3, β-catenin, TNF-α, or TGF-β pathway. To our knowledge, this is the first comprehensive survey of cancer pathway activities in nine major tumor types and the most widely used NCI60 cell lines. The "gene expression pathway signatures" we have defined could facilitate the understanding of molecular mechanisms in cancer development and provide guidance to the selection of appropriate cell lines for cancer research and pharmaceutical compound screening.