Detection of gene expression signatures related to underlying disease and treatment in rheumatoid arthritis patients.

OBJECTIVES: Gene expression signatures can provide an unbiased view into the molecular changes underlying biologically and medically interesting phenotypes. We therefore initiated this study to identify signatures that would be of utility in studying rheumatoid arthritis (RA). METHODS: We used microarray profiling of peripheral blood mononuclear cells (PBMCs) in 30 RA patients to assess the effect of different biologic agent (biologics) treatments and to quantify the degree of a type-I interferon (IFN) signature in these patients. A numeric score was derived for the quantification step and applied to patients with RA. To further characterize the IFN response in our cohort, we employed type-I IFN treatment of PBMCs in vitro and in reporter assays. RESULTS: Profiling identified a subset of RA patients with upregulation of type-I IFN-regulated transcripts, thereby corroborating previous reports showing RA to be heterogeneous for an IFN component. A comparison of individuals currently untreated with a biologic with those treated with infliximab, tocilizumab, or abatacept suggested that each biologic induces a specific gene signature in PBMCs. CONCLUSIONS: It is possible to observe signs of type-I IFN pathway activation in a subset of clinically active RA patients without C-reactive protein elevation. Furthermore, biologics-specific gene signatures in patients with RA indicate that looking for a biologic-specific response pattern may be a potential future tool for predicting individual patient response.

What is the best reference RNA? And other questions regarding the design and analysis of two-color microarray experiments.

The reference design is a practical and popular choice for microarray studies using two-color platforms. In the reference design, the reference RNA uses half of all array resources, leading investigators to ask: What is the best reference RNA? We propose a novel method for evaluating reference RNAs and present the results of an experiment that was specially designed to evaluate three common choices of reference RNA. We found no compelling evidence in favor of any particular reference. In particular, a commercial reference showed no advantage in our data. Our experimental design also enabled a new way to test the effectiveness of pre-processing methods for two-color arrays. Our results favor using intensity normalization and foregoing background subtraction. Finally, we evaluate the sensitivity and specificity of data quality filters, and we propose a new filter that can be applied to any experimental design and does not rely on replicate hybridizations.

Multiplexed measurements of gene signatures in different analytes using the Nanostring nCounter Assay System.

BACKGROUND: We assessed NanoString's nCounter Analysis System for its ability to quantify gene expression of forty-eight genes in a single reaction with 100 ng of total RNA or an equivalent amount of tissue lysate. In the nCounter System, multiplexed gene expression target levels are directly detected, without enzymatic reactions, via two sequence-specific probes. The individual mRNA is captured with one mRNA target sequence-specific capture probe that is used in a post-hybridization affinity purification procedure. The second mRNA target specific-sequence and fluorescent-labeled colored coded probe is then used in the detection with the 3-component complex separated on a surface via an applied electric field followed by imaging. We evaluated reproducibility, accuracy, concordance with quantitative RT-PCR, linearity, dynamic range, and the ability of the system to assay different inputs (matched samples of total RNA from Flash Frozen (FF) and Formalin Fixed Paraffin Embedded Tissues (FFPET), and crude tissue lysates (CTL)). FINDINGS: The nCounter Analysis System provided data equivalent to that produced by Taqman(R)-based assays for genes expressed within the ranges of the calibration curves (above ~0.5 mRNA copies per human cell based on an assumption of 10 pg of total RNA per cell). System response was linear over more than two orders of magnitude with typical CVs of ~6% for concentrations above 1 fM (105 molecules per mL). Profiling the industry-standard MAQC data set yielded correlation coefficients of >0.83 for intensity values and >0.99 for measured ratios. Ninety percent of nCounter ratio measurements were within 1.27-1.33 fold changes of the Taqman(R) data (0.34-0.41 in log2 scale) for FF total RNA samples. CONCLUSION: The nCounter Analysis System generated robust data for multi-gene expression signatures across three different sample preparation conditions.

The transcriptome and its translation during recovery from cell cycle arrest in Saccharomyces cerevisiae.

Complete genome sequences together with high throughput technologies have made comprehensive characterizations of gene expression patterns possible. While genome-wide measurement of mRNA levels was one of the first applications of these advances, other important aspects of gene expression are also amenable to a genomic approach, for example, the translation of message into protein. Earlier we reported a high throughput technology for simultaneously studying mRNA level and translation, which we termed translation state array analysis, or TSAA. The current studies test the proposition that TSAA can identify novel instances of translation regulation at the genome-wide level. As a biological model, cultures of Saccharomyces cerevisiae were cell cycle-arrested using either alpha-factor or the temperature-sensitive cdc15-2 allele. Forty-eight mRNAs were found to change significantly in translation state following release from alpha-factor arrest, including genes involved in pheromone response and cell cycle arrest such as BAR1, SST2, and FAR1. After the shift of the cdc15-2 strain from 37 degrees C to 25 degrees C, 54 mRNAs were altered in translation state, including the products of the stress genes HSP82, HSC82, and SSA2. Thus, regulation at the translational level seems to play a significant role in the response of yeast cells to external physical or biological cues. In contrast, surprisingly few genes were found to be translationally controlled as cells progressed through the cell cycle. Additional refinements of TSAA should allow characterization of both transcriptional and translational regulatory networks on a genomic scale, providing an additional layer of information that can be integrated into models of system biology and function.

Gene expression analyzed by high-resolution state array analysis and quantitative proteomics: response of yeast to mating pheromone.

The transcriptome provides the database from which a cell assembles its collection of proteins. Translation of individual mRNA species into their encoded proteins is regulated, producing discrepancies between mRNA and protein levels. Using a new modeling approach to data analysis, a striking diversity is revealed in association of the transcriptome with the translational machinery. Each mRNA has its own pattern of ribosome loading, a circumstance that provides an extraordinary dynamic range of regulation, above and beyond actual transcript levels. Using this approach together with quantitative proteomics, we explored the immediate changes in gene expression in response to activation of a mitogen-activated protein kinase pathway in yeast by mating pheromone. Interestingly, in 26% of those transcripts where the predicted protein synthesis rate changed by at least 3-fold, more than half of these changes resulted from altered translational efficiencies. These observations underscore that analysis of transcript level, albeit extremely important, is insufficient by itself to describe completely the phenotypes of cells under different conditions.