Variability in synovial inflammation in rheumatoid arthritis investigated by microarray technology

In recent years microarray technology has been used increasingly to acquire knowledge about the pathogenic processes involved in rheumatoid arthritis. The present study investigated variations in gene expression in synovial tissues within and between patients with rheumatoid arthritis. This was done by applying microarray technology on multiple synovial biopsies obtained from the same knee joints. In this way the relative levels of intra-patient and inter-patient variation could be assessed. The biopsies were obtained from 13 different patients: 7 by orthopedic surgery and 6 by rheumatic arthroscopy. The data show that levels of heterogeneity varied substantially between the biopsies, because the number of genes found to be differentially expressed between pairs of biopsies from the same knee ranged from 6 to 2,133. Both arthroscopic and orthopedic biopsies were examined, allowing us to compare the two sampling methods. We found that the average number of differentially expressed genes between biopsies from the same patient was about three times larger in orthopedic than in arthroscopic biopsies. Using a parallel analysis of the tissues by immunohistochemistry, we also identified orthopedic biopsies that were unsuitable for gene expression analysis of synovial inflammation due to sampling of non-inflamed parts of the tissue. Removing these biopsies reduced the average number of differentially expressed genes between the orthopedic biopsies from 455 to 171, in comparison with 143 for the arthroscopic biopsies. Hierarchical clustering analysis showed that the remaining orthopedic and arthroscopic biopsies had gene expression signatures that were unique for each patient, apparently reflecting patient variation rather than tissue heterogeneity. Subsets of genes found to vary between biopsies were investigated for overrepresentation of biological processes by using gene ontology. This revealed representative 'themes' likely to vary between synovial biopsies affected by inflammatory disease.     

Identification of Highly Methylated Genes across Various Types of B-Cell Non-Hodgkin Lymphoma

Epigenetic alterations of gene expression are important in the development of cancer. In this study, we identified genes which are epigenetically altered in major lymphoma types. We used DNA microarray technology to assess changes in gene expression after treatment of 11 lymphoma cell lines with epigenetic drugs. We identified 233 genes with upregulated expression in treated cell lines and with downregulated expression in B-cell lymphoma patient samples (n = 480) when compared to normal B cells (n = 5). The top 30 genes were further analyzed by methylation specific PCR (MSP) in 18 lymphoma cell lines. Seven of the genes were methylated in more than 70% of the cell lines and were further subjected to quantitative MSP in 37 B-cell lymphoma patient samples (diffuse large B-cell lymphoma (activated B-cell like and germinal center B-cell like subtypes), follicular lymphoma and Burkitt`s lymphoma) and normal B lymphocytes from 10 healthy donors. The promoters of DSP, FZD8, KCNH2, and PPP1R14A were methylated in 28%, 67%, 22%, and 78% of the 36 tumor samples, respectively, but not in control samples. Validation using a second series of healthy donor controls (n = 42; normal B cells, peripheral blood mononuclear cells, bone marrow, tonsils and follicular hyperplasia) and fresh-frozen lymphoma biopsies (n = 25), confirmed the results. The DNA methylation biomarker panel consisting of DSP, FZD8, KCNH2, and PPP1R14A was positive in 89% (54/61) of all lymphomas. Receiver operating characteristic analysis to determine the discriminative power between lymphoma and healthy control samples showed a c-statistic of 0.96, indicating a possible role for the biomarker panel in monitoring of lymphoma patients.     

Differences in gene expression between B-cell chronic lymphocytic leukemia and normal B cells: a meta-analysis of three microarray studies

MOTIVATION: A major focus of current cancer research is to identify genes that can be used as markers for prognosis and diagnosis, and as targets for therapy. Microarray technology has been applied extensively for this purpose, even though it has been reported that the agreement between microarray platforms is poor. A critical question is: how can we best combine the measurements of matched genes across microarray platforms to develop diagnostic and prognostic tools related to the underlying biology? RESULTS: We introduce a statistical approach within a Bayesian framework to combine the microarray data on matched genes from three investigations of gene expression profiling of B-cell chronic lymphocytic leukemia (CLL) and normal B cells (NBC) using three different microarray platforms, oligonucleotide arrays, cDNA arrays printed on glass slides and cDNA arrays printed on nylon membranes. Using this approach, we identified a number of genes that were consistently differentially expressed between CLL and NBC samples.     

Insights from the clustering of microarray data associated with the heart disease

Heart failure (HF) is the major of cause of mortality and morbidity in the developed world. Gene expression profiles of animal model of heart failure have been used in number of studies to understand human cardiac disease. In this study, statistical methods of analysing microarray data on cardiac tissues from dogs with pacing induced HF were used to identify differentially expressed genes between normal and two abnormal tissues. The unsupervised techniques principal component analysis (PCA) and cluster analysis were explored to distinguish between three different groups of 12 arrays and to separate the genes which are up regulated in different conditions among 23912 genes in heart failure canines'' microarray data. It was found that out of 23912 genes, 1802 genes were differentially expressed in the three groups at 5% level of significance and 496 genes were differentially expressed at 1% level of significance using one way analysis of variance (ANOVA). The genes clustered using PCA and clustering analysis were explored in the paper to understand HF and a small number of differentially expressed genes related to HF were identified.     

Large Scale Comparison of Gene Expression Levels by Microarrays and RNAseq Using TCGA Data

RNAseq and microarray methods are frequently used to measure gene expression level. While similar in purpose, there are fundamental differences between the two technologies. Here, we present the largest comparative study between microarray and RNAseq methods to date using The Cancer Genome Atlas (TCGA) data. We found high correlations between expression data obtained from the Affymetrix one-channel microarray and RNAseq (Spearman correlations coefficients of ∼0.8). We also observed that the low abundance genes had poorer correlations between microarray and RNAseq data than high abundance genes. As expected, due to measurement and normalization differences, Agilent two-channel microarray and RNAseq data were poorly correlated (Spearman correlations coefficients of only ∼0.2). By examining the differentially expressed genes between tumor and normal samples we observed reasonable concordance in directionality between Agilent two-channel microarray and RNAseq data, although a small group of genes were found to have expression changes reported in opposite directions using these two technologies. Overall, RNAseq produces comparable results to microarray technologies in term of expression profiling. The RNAseq normalization methods RPKM and RSEM produce similar results on the gene level and reasonably concordant results on the exon level. Longer exons tended to have better concordance between the two normalization methods than shorter exons.     

Use of SMART-generated cDNA for gene expression studies in multiple human tumors

We demonstrate here that SMART PCR-amplified cDNAs arrayed on a nylon membrane are suitable for high-throughput tissue expression profiling when starting biological materials are limited. We show that SMART cDNA accurately reflects gene expression patterns found in total RNA by comparing the expression level of several target genes in SMART PCR-amplified cDNAs and their corresponding total RNAs. We also arrayed cDNAs from 68 matched tumor and normal samples on a nylon membrane to determine whether SMART PCR-amplified cDNA could be used for detecting differentially expressed genes in these tissues. These arrays containing normalized tumor and normal cDNAs were hybridized with probes for glutathione peroxidase and gelsolin. The hybridization results revealed cancer-related and patient-specific gene expression differences between tumor and normal tissues for these genes. These studies show that SMART PCR-amplified cDNAs maintain the complexity of the original mRNA population and are thus suitable for high-throughput studies to compare the relative abundance of target genes and to detect differentially expressed genes in a wide variety of tissues simultaneously.     

MicroRNA Profiling of Primary Cutaneous Large B-Cell Lymphomas

Aberrant expression of microRNAs is widely accepted to be pathogenetically involved in nodal diffuse large B-cell lymphomas (DLBCLs). However, the microRNAs profiles of primary cutaneous large B-cell lymphomas (PCLBCLs) are not yet described. Its two main subtypes, i.e., primary cutaneous diffuse large B-cell lymphoma, leg type (PCLBCL-LT) and primary cutaneous follicle center lymphoma (PCFCL) are characterized by an activated B-cell (ABC)-genotype and a germinal center B-cell (GCB)-genotype, respectively. We performed high-throughput sequencing analysis on frozen tumor biopsies from 19 cases of PCFCL and PCLBCL-LT to establish microRNA profiles. Cluster analysis of the complete microRNome could not distinguish between the two subtypes, but 16 single microRNAs were found to be differentially expressed. Single microRNA RT-qPCR was conducted on formalin-fixed paraffin-embedded tumor biopsies of 20 additional cases, confirming higher expression of miR-9-5p, miR-31-5p, miR-129-2-3p and miR-214-3p in PCFCL as compared to PCLBCL-LT. MicroRNAs previously described to be higher expressed in ABC-type as compared to GCB-type nodal DLBCL were not differentially expressed between PCFCL and PCLBCL-LT. In conclusion, PCFCL and PCLBCL-LT differ in their microRNA profiles. In contrast to their gene expression profile, they only show slight resemblance with the microRNA profiles found in GCB- and ABC-type nodal DLBCL.