Association of the interferon signature metric with serological disease manifestations but not global activity scores in multiple cohorts of patients with SLE.

OBJECTIVES: The interferon (IFN) signature (IS) in patients with systemic lupus erythematosus (SLE) includes over 100 genes induced by type I IFN pathway activation. We developed a method to quantify the IS using three genes-the IS metric (ISM)-and characterised the clinical characteristics of patients with SLE with different ISM status from multiple clinical trials. METHODS: Blood microarray expression data from a training cohort of patients with SLE confirmed the presence of the IS and identified surrogate genes. We assayed these genes in a quantitative PCR (qPCR) assay, yielding an ISM from the IS. The association of ISM status with clinical disease characteristics was assessed in patients with extrarenal lupus and lupus nephritis from four clinical trials. RESULTS: Three genes, HERC5, EPSTI and CMPK2, correlated well with the IS (p>0.96), and composed the ISM qPCR assay. Using the 95th centile for healthy control data, patients with SLE from different studies were classified into two ISM subsets-ISM-Low and ISM-High-that are longitudinally stable over 36 weeks. Significant associations were identified between ISM-High status and higher titres of anti-dsDNA antibodies, presence of anti extractable nuclear antigen autoantibodies, elevated serum B cell activating factor of the tumour necrosis factor family (BAFF) levels, and hypocomplementaemia. However, measures of overall clinical disease activity were similar for ISM-High and ISM-Low groups. CONCLUSIONS: The ISM is an IS biomarker that divides patients with SLE into two subpopulations-ISM-High and ISM-Low-with differing serological manifestations. The ISM does not distinguish between high and low disease activity, but may have utility in identifying patients more likely to respond to treatment(s) targeting IFN-α. CLINICALTRIALSGOV REGISTRATION NUMBER: NCT00962832.

ReportingTools: an automated result processing and presentation toolkit for high-throughput genomic analyses.

UNLABELLED: It is common for computational analyses to generate large amounts of complex data that are difficult to process and share with collaborators. Standard methods are needed to transform such data into a more useful and intuitive format. We present ReportingTools, a Bioconductor package, that automatically recognizes and transforms the output of many common Bioconductor packages into rich, interactive, HTML-based reports. Reports are not generic, but have been individually designed to reflect content specific to the result type detected. Tabular output included in reports is sortable, filterable and searchable and contains context-relevant hyperlinks to external databases. Additionally, in-line graphics have been developed for specific analysis types and are embedded by default within table rows, providing a useful visual summary of underlying raw data. ReportingTools is highly flexible and reports can be easily customized for specific applications using the well-defined API. AVAILABILITY: The ReportingTools package is implemented in R and available from Bioconductor (version ≥ 2.11) at the URL: http://bioconductor.org/packages/release/bioc/html/ReportingTools.html. Installation instructions and usage documentation can also be found at the above URL.

The genetics of type I interferon in systemic lupus erythematosus.

The discovery that type I interferon (IFN)-inducible genes were strongly upregulated in peripheral blood in SLE over a decade ago sparked interest in understanding the relationship between type I IFN and SLE. Genome-wide association studies provide strong genetic evidence that type I IFNs are important for SLE risk. Of 47 genetic variants associated with SLE, over half (27/47, 57%) can be linked to type I IFN production or signaling. The recent identification of single gene mutations for disorders that share features with SLE--Aicardi-Goutières syndrome, chilblain lupus, and spondyloenchondrodysplasia--provide additional support for the hypothesis that type I IFNs are central drivers of SLE pathogenesis. These insights provide significant focus for efforts to tackle SLE therapeutically.

CompMoby: comparative MobyDick for detection of cis-regulatory motifs.

BACKGROUND: The regulation of gene expression is complex and occurs at many levels, including transcriptional and post-transcriptional, in metazoans. Transcriptional regulation is mainly determined by sequence elements within the promoter regions of genes while sequence elements within the 3' untranslated regions of mRNAs play important roles in post-transcriptional regulation such as mRNA stability and translation efficiency. Identifying cis-regulatory elements, or motifs, in multicellular eukaryotes is more difficult compared to unicellular eukaryotes due to the larger intergenic sequence space and the increased complexity in regulation. Experimental techniques for discovering functional elements are often time consuming and not easily applied on a genome level. Consequently, computational methods are advantageous for genome-wide cis-regulatory motif detection. To decrease the search space in metazoans, many algorithms use cross-species alignment, although studies have demonstrated that a large portion of the binding sites for the same trans-acting factor do not reside in alignable regions. Therefore, a computational algorithm should account for both conserved and nonconserved cis-regulatory elements in metazoans. RESULTS: We present CompMoby (Comparative MobyDick), software developed to identify cis-regulatory binding sites at both the transcriptional and post-transcriptional levels in metazoans without prior knowledge of the trans-acting factors. The CompMoby algorithm was previously shown to identify cis-regulatory binding sites in upstream regions of genes co-regulated in embryonic stem cells. In this paper, we extend the software to identify putative cis-regulatory motifs in 3' UTR sequences and verify our results using experimentally validated data sets in mouse and human. We also detail the implementation of CompMoby into a user-friendly tool that includes a web interface to a streamlined analysis. Our software allows detection of motifs in the following three categories: one, those that are alignable and conserved; two, those that are conserved but not alignable; three, those that are species specific. One of the output files from CompMoby gives the user the option to decide what category of cis-regulatory element to experimentally pursue based on their biological problem. Using experimentally validated biological datasets, we demonstrate that CompMoby is successful in detecting cis-regulatory target sites of known and novel trans-acting factors at the transcriptional and post-transcriptional levels. CONCLUSION: CompMoby is a powerful software tool for systematic de novo discovery of evolutionarily conserved and nonconserved cis-regulatory sequences involved in transcriptional or post-transcriptional regulation in metazoans. This software is freely available to users at http://genome.ucsf.edu/compmoby/.

fREDUCE: detection of degenerate regulatory elements using correlation with expression.

BACKGROUND: The precision of transcriptional regulation is made possible by the specificity of physical interactions between transcription factors and their cognate binding sites on DNA. A major challenge is to decipher transcription factor binding sites from sequence and functional genomic data using computational means. While current methods can detect strong binding sites, they are less sensitive to degenerate motifs. RESULTS: We present fREDUCE, a computational method specialized for the detection of weak or degenerate binding motifs from gene expression or ChIP-chip data. fREDUCE is built upon the widely applied program REDUCE, which elicits motifs by global statistical correlation of motif counts with expression data. fREDUCE introduces several algorithmic refinements that allow efficient exhaustive searches of oligonucleotides with a specified number of degenerate IUPAC symbols. On yeast ChIP-chip benchmarks, fREDUCE correctly identified motifs and their degeneracies with accuracies greater than its predecessor REDUCE as well as other known motif-finding programs. We have also used fREDUCE to make novel motif predictions for transcription factors with poorly characterized binding sites. CONCLUSION: We demonstrate that fREDUCE is a valuable tool for the prediction of degenerate transcription factor binding sites, especially from array datasets with weak signals that may elude other motif detection methods.

Systematic identification of cis-regulatory sequences active in mouse and human embryonic stem cells.

Understanding the transcriptional regulation of pluripotent cells is of fundamental interest and will greatly inform efforts aimed at directing differentiation of embryonic stem (ES) cells or reprogramming somatic cells. We first analyzed the transcriptional profiles of mouse ES cells and primordial germ cells and identified genes upregulated in pluripotent cells both in vitro and in vivo. These genes are enriched for roles in transcription, chromatin remodeling, cell cycle, and DNA repair. We developed a novel computational algorithm, CompMoby, which combines analyses of sequences both aligned and non-aligned between different genomes with a probabilistic segmentation model to systematically predict short DNA motifs that regulate gene expression. CompMoby was used to identify conserved overrepresented motifs in genes upregulated in pluripotent cells. We show that the motifs are preferentially active in undifferentiated mouse ES and embryonic germ cells in a sequence-specific manner, and that they can act as enhancers in the context of an endogenous promoter. Importantly, the activity of the motifs is conserved in human ES cells. We further show that the transcription factor NF-Y specifically binds to one of the motifs, is differentially expressed during ES cell differentiation, and is required for ES cell proliferation. This study provides novel insights into the transcriptional regulatory networks of pluripotent cells. Our results suggest that this systematic approach can be broadly applied to understanding transcriptional networks in mammalian species.

Cell- and gene-specific regulation of primary target genes by the androgen receptor.

The androgen receptor (AR) mediates the physiologic and pathophysiologic effects of androgens including sexual differentiation, prostate development, and cancer progression by binding to genomic androgen response elements (AREs), which influence transcription of AR target genes. The composition and context of AREs differ between genes, thus enabling AR to confer multiple regulatory functions within a single nucleus. We used expression profiling of an immortalized human prostate epithelial cell line to identify 205 androgen-responsive genes (ARGs), most of them novel. In addition, we performed chromatin immunoprecipitation to identify 524 AR binding regions and validated in reporter assays the ARE activities of several such regions. Interestingly, 67% of our AREs resided within approximately 50 kb of the transcription start sites of 84% of our ARGs. Indeed, most ARGs were associated with two or more AREs, and ARGs were sometimes themselves linked in gene clusters containing up to 13 AREs and 12 ARGs. AREs appeared typically to be composite elements, containing AR binding sequences adjacent to binding motifs for other transcriptional regulators. Functionally, ARGs were commonly involved in prostate cell proliferation, communication, differentiation, and possibly cancer progression. Our results provide new insights into cell- and gene-specific mechanisms of transcriptional regulation of androgen-responsive gene networks.

Determinants of cell- and gene-specific transcriptional regulation by the glucocorticoid receptor.

The glucocorticoid receptor (GR) associates with glucocorticoid response elements (GREs) and regulates selective gene transcription in a cell-specific manner. Native GREs are typically thought to be composite elements that recruit GR as well as other regulatory factors into functional complexes. We assessed whether GR occupancy is commonly a limiting determinant of GRE function as well as the extent to which core GR binding sequences and GRE architecture are conserved at functional loci. We surveyed 100-kb regions surrounding each of 548 known or potentially glucocorticoid-responsive genes in A549 human lung cells for GR-occupied GREs. We found that GR was bound in A549 cells predominately near genes responsive to glucocorticoids in those cells and not at genes regulated by GR in other cells. The GREs were positionally conserved at each responsive gene but across the set of responsive genes were distributed equally upstream and downstream of the transcription start sites, with 63% of them >10 kb from those sites. Strikingly, although the core GR binding sequences across the set of GREs varied extensively around a consensus, the precise sequence at an individual GRE was conserved across four mammalian species. Similarly, sequences flanking the core GR binding sites also varied among GREs but were conserved at individual GREs. We conclude that GR occupancy is a primary determinant of glucocorticoid responsiveness in A549 cells and that core GR binding sequences as well as GRE architecture likely harbor gene-specific regulatory information.