Thomas E. Royce

Distribution of NF-B-binding sites across human chromosome 22

We have mapped the chromosomal binding site distribution of a transcription factor in human cells. The NF-κB family of transcription factors plays an essential role in regulating the induction of genes involved in several physiological processes, including apoptosis, immunity, and inflammation. The binding sites of the NF-κB family member p65 were determined by using chromatin immunoprecipitation and a genomic microarray of human chromosome 22 DNA. Sites of binding were observed along the entire chromosome in both coding and noncoding regions, with an enrichment at the 5′ end of genes. Strikingly, a significant proportion of binding was seen in intronic regions, demonstrating that transcription factor binding is not restricted to promoter regions. NF-κB binding was also found at genes whose expression was regulated by tumor necrosis factor α, a known inducer of NF-κB-dependent gene expression, as well as adjacent to genes whose expression is not affected by tumor necrosis factor α. Many of these latter genes are either known to be activated by NF-κB under other conditions or are consistent with NF-κBs role in the immune and apoptotic responses. Our results suggest that binding is not restricted to promoter regions and that NF-κB binding occurs at a significant number of genes whose expression is not altered, thereby suggesting that binding alone is not sufficient for gene activation.

CREB binds to multiple loci on human chromosome 22.

ABSTRACT The cyclic AMP-responsive element-binding protein (CREB) is an important transcription factor that can be activated by hormonal stimulation and regulates neuronal function and development. An unbiased, global analysis of where CREB binds has not been performed. We have mapped for the first time the binding distribution of CREB along an entire human chromosome. Chromatin immunoprecipitation of CREB-associated DNA and subsequent hybridization of the associated DNA to a genomic DNA microarray containing all of the nonrepetitive DNA of human chromosome 22 revealed 215 binding sites corresponding to 192 different loci and 100 annotated potential gene targets. We found binding near or within many genes involved in signal transduction and neuronal function. We also found that only a small fraction of CREB binding sites lay near well-defined 5′ ends of genes; the majority of sites were found elsewhere, including introns and unannotated regions. Several of the latter lay near novel unannotated transcriptionally active regions. Few CREB targets were found near full-length cyclic AMP response element sites; the majority contained shorter versions or close matches to this sequence. Several of the CREB targets were altered in their expression by treatment with forskolin; interestingly, both induced and repressed genes were found. Our results provide novel molecular insights into how CREB mediates its functions in humans.

Systematic prediction and validation of breakpoints associated with copy-number variants in the human genome

Copy-number variants (CNVs) are an abundant form of genetic variation in humans. However, approaches for determining exact CNV breakpoint sequences (physical deletion or duplication boundaries) across individuals, crucial for associating genotype to phenotype, have been lacking so far, and the vast majority of CNVs have been reported with approximate genomic coordinates only. Here, we report an approach, called BreakPtr, for fine-mapping CNVs (available from http://breakptr.gersteinlab.org). We statistically integrate both sequence characteristics and data from high-resolution comparative genome hybridization experiments in a discrete-valued, bivariate hidden Markov model. Incorporation of nucleotide-sequence information allows us to take into account the fact that recently duplicated sequences (e.g., segmental duplications) often coincide with breakpoints. In anticipation of an upcoming increase in CNV data, we developed an iterative, “active” approach to initially scoring with a preliminary model, performing targeted validations, retraining the model, and then rescoring, and a flexible parameterization system that intuitively collapses from a full model of 2,503 parameters to a core one of only 10. Using our approach, we accurately mapped >400 breakpoints on chromosome 22 and a region of chromosome 11, refining the boundaries of many previously approximately mapped CNVs. Four predicted breakpoints flanked known disease-associated deletions. We validated an additional four predicted CNV breakpoints by sequencing. Overall, our results suggest a predictive resolution of ≈300bp. This level of resolution enables more precise correlations between CNVs and across individuals than previously possible, allowing the study of CNV population frequencies. Further, it enabled us to demonstrate a clear Mendelian pattern of inheritance for one of the CNVs.

Integrative Microarray Analysis of Pathways Dysregulated in Metastatic Prostate Cancer

Microarrays have been used to identify genes involved in cancer progression. We have now developed an algorithm that identifies dysregulated pathways from multiple expression array data sets without a priori definition of gene expression thresholds. Integrative microarray analysis of pathways (IMAP) was done using existing expression array data from localized and metastatic prostate cancer. Comparison of metastatic cancer and localized disease in multiple expression array profiling studies using the IMAP approach yielded a list of about 100 pathways that were significantly dysregulated (P < 0.05) in prostate cancer metastasis. The pathway that showed the most significant dysregulation, HIV-I NEF, was validated at both the transcript level and the protein level by quantitative PCR and immunohistochemical analysis, respectively. Validation by unsupervised analysis on an independent data set using the gene expression signature from the HIV-I NEF pathway verified the accuracy of our method. Our results indicate that this pathway is especially dysregulated in hormone-refractory prostate cancer.

Immunity against Ixodes scapularis salivary proteins expressed within 24 hours of attachment thwarts tick feeding and impairs Borrelia transmission.

In North America, the black-legged tick, Ixodes scapularis, an obligate haematophagus arthropod, is a vector of several human pathogens including Borrelia burgdorferi, the Lyme disease agent. In this report, we show that the tick salivary gland transcriptome and proteome is dynamic and changes during the process of engorgement. We demonstrate, using a guinea pig model of I. scapularis feeding and B. burgdorferi transmission, that immunity directed against salivary proteins expressed in the first 24 h of tick attachment — and not later — is sufficient to evoke all the hallmarks of acquired tick-immunity, to thwart tick feeding and also to impair Borrelia transmission. Defining this subset of proteins will promote a mechanistic understanding of novel I. scapularis proteins critical for the initiation of tick feeding and for Borrelia transmission.

Computational analysis of membrane proteins: genomic occurrence, structure prediction and helix interactions

We review recent computational advances in the study of membrane proteins, focusing on those that have at least one transmembrane helix. Transmembrane protein regions are, in many respects, easier to investigate computationally than experimentally, due to the uniformity of their structure and interactions (e.g. consisting predominately of nearly parallel helices packed together) on one hand and presenting the challenges of solubility on the other. We present the progress made on identifying and classifying membrane proteins into families, predicting their structure from amino-acid sequence patterns (using many different methods), and analyzing their interactions and packing The total result of this work allows us for the first time to begin to think about the membrane protein interactome, the set of all interactions between distinct transmembrane helices in the lipid bilayer.

Toward a universal microarray: prediction of gene expression through nearest-neighbor probe sequence identification

A generic DNA microarray design applicable to any species would greatly benefit comparative genomics. We have addressed the feasibility of such a design by leveraging the great feature densities and relatively unbiased nature of genomic tiling microarrays. Specifically, we first divided each Homo sapiens Refseq-derived genes spliced nucleotide sequence into all of its possible contiguous 25 nt subsequences. For each of these 25 nt subsequences, we searched a recent human transcript mapping experiments probe design for the 25 nt probe sequence having the fewest mismatches with the subsequence, but that did not match the subsequence exactly. Signal intensities measured with each genes nearest-neighbor features were subsequently averaged to predict their gene expression levels in each of the experiments thirty-three hybridizations. We examined the fidelity of this approach in terms of both sensitivity and specificity for detecting actively transcribed genes, for transcriptional consistency between exons of the same gene, and for reproducibility between tiling array designs. Taken together, our results provide proof-of-principle for probing nucleic acid targets with off-target, nearest-neighbor features.

Assessing the need for sequence-based normalization in tiling microarray experiments

MOTIVATION Increases in microarray feature density allow the construction of so-called tiling microarrays. These arrays, or sets of arrays, contain probes targeting regions of sequenced genomes at regular genomic intervals. The unbiased nature of this approach allows for the identification of novel transcribed sequences, the localization of transcription factor binding sites (ChIP-chip), and high resolution comparative genomic hybridization, among other uses. These applications are quickly growing in popularity as tiling microarrays become more affordable. To reach maximum utility, the tiling microarray platform needs be developed to the point that 1 nt resolutions are achieved and that we have confidence in individual measurements taken at this fine of resolution. Any biases in tiling array signals must be systematically removed to achieve this goal. RESULTS Towards this end, we investigated the importance of probe sequence composition on the efficacy of tiling microarrays for identifying novel transcription and transcription factor binding sites. We found that intensities are highly sequence dependent and can greatly influence results. We developed three metrics for assessing this sequence dependence and use them in evaluating existing sequence-based normalizations from the tiling microarray literature. In addition, we applied three new techniques for addressing this problem; one method, adapted from similar work on GeneChip brand microarrays, is based on modeling array signal as a linear function of probe sequence, the second method extends this approach by iterative weighting and re-fitting of the model, and the third technique extrapolates the popular quantile normalization algorithm for between-array normalization to probe sequence space. These three methods perform favorably to existing strategies, based on the metrics defined here. AVAILABILITY http://tiling.gersteinlab.org/sequence_effects/

ExpressYourself: a modular platform for processing and visualizing microarray data

DNA microarrays are widely used in biological research; by analyzing differential hybridization on a single microarray slide, one can detect changes in mRNA expression levels, increases in DNA copy numbers and the location of transcription factor binding sites on a genomic scale. Having performed the experiments, the major challenge is to process large, noisy datasets in order to identify the specific array elements that are significantly differentially hybridized. This normally requires aggregating different, often incompatible programs into a multi-step pipeline. Here we present ExpressYourself, a fully integrated platform for processing microarray data. In completely automated fashion, it will correct the background array signal, normalize the Cy5 and Cy3 signals, score levels of differential hybridization, combine the results of replicate experiments, filter problematic regions of the array and assess the quality of individual and replicate experiments. ExpressYourself is designed with a highly modular architecture so various types of microarray analysis algorithms can readily be incorporated as they are developed; for example, the system currently implements several normalization methods, including those that simultaneously consider signal intensity and slide location. The processed data are presented using a web-based graphical interface to facilitate comparison with the original images of the array slides. In particular, Express Yourself is able to regenerate images of the original microarray after applying various steps of processing, which greatly facilities identification of position-specific artifacts. The program is freely available for use at http://bioinfo.mbb.yale.edu/expressyourself.

Canvas: versatile and scalable detection of copy number variants

MOTIVATION Versatile and efficient variant calling tools are needed to analyze large scale sequencing datasets. In particular, identification of copy number changes remains a challenging task due to their complexity, susceptibility to sequencing biases, variation in coverage data and dependence on genome-wide sample properties, such as tumor polyploidy or polyclonality in cancer samples. RESULTS We have developed a new tool, Canvas, for identification of copy number changes from diverse sequencing experiments including whole-genome matched tumor-normal and single-sample normal re-sequencing, as well as whole-exome matched and unmatched tumor-normal studies. In addition to variant calling, Canvas infers genome-wide parameters such as cancer ploidy, purity and heterogeneity. It provides fast and easy-to-run workflows that can scale to thousands of samples and can be easily incorporated into variant calling pipelines. AVAILABILITY AND IMPLEMENTATION Canvas is distributed under an open source license and can be downloaded from https://github.com/Illumina/canvas CONTACT eroller@illumina.com SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.