Biomedical subjects

Michael Q Zhang

Publications and source records attributed to Michael Q Zhang.

At least 19 recordsLinked to original sources

PubMed2007

Tissue-specific regulatory elements in mammalian promoters.

Transcription factor-binding sites and the cis-regulatory modules they compose are central determinants of gene expression. We previously showed that binding site motifs and modules in proximal promoters can be used to predict a significant portion of mammalian tissue-specific transcription. Here, we report on a systematic analysis of promoters controlling tissue-specific expression in heart, kidney, liver, pancreas, skeletal muscle, testis and CD4 T cells, for both human and mouse. We integrated multiple sources of expression data to compile sets of transcripts with strong evidence for tissue-specific regulation. The analysis of the promoters corresponding to these sets produced a catalog of predicted tissue-specific motifs and modules, and cis-regulatory elements. Predicted regulatory interactions are supported by statistical evidence, and provide a foundation for targeted experiments that will improve our understanding of tissue-specific regulatory networks. In a broader context, methods used to construct the catalog provide a model for the analysis of genomic regions that regulate differentially expressed genes.

Algorithms↗

PubMed2006

Pan-genome isolation of low abundance transcripts using SAGE tag.

The SAGE (serial analysis of gene expression) method is sensitive at detecting the lower abundance transcripts. More than a third of human SAGE tags identified are novel representing the low abundance unknown transcripts. Using the GLGI method (generation of longer 3' EST from SAGE tag for gene identification), we converted 1009 low-copy, human X chromosome-specific SAGE tags into 10210 3' ESTs. We identified 3418 unique 3' ESTs, 46% of which are novel and originated from the lower abundance transcripts. However, nearly all 3' ESTs were mapped to various regions across the genome but not X chromosome. Detailed analysis indicates that those 3' ESTs were isolated by SAGE tag mis-priming to the non-parent transcripts. Replacing SAGE tags with non-transcribed genomic DNA tags resulted in poor amplification, indicating that the sequence similarity between different transcripts contributed to the amplification. Our study shows the prevalence of novel low abundance transcripts that can be isolated efficiently through SAGE tags mis-priming.

Base Pair Mismatch↗

PubMed2006

Computational prediction of novel components of lung transcriptional networks.

MOTIVATION: Little is known regarding the transcriptional mechanisms involved in forming and maintaining epithelial cell lineages of the mammalian respiratory tract. RESULTS: Herein, a motif discovery approach was used to identify novel transcriptional regulators in the lung using genes previously found to be regulated by Foxa2 or Wnt signaling pathways. A human-mouse comparison of both novel and known motifs was also performed. Some of the factors and families identified here were previously shown to be involved epithelial cell differentiation (ETS family, HES-1 and MEIS-1), and ciliogenesis (RFX family), but have never been characterized in lung epithelia. Other unidentified over-represented motifs suggest the existence of novel mammalian lung transcription factors. Of the fraction of motifs examined we describe 25 transcription factor family predictions for lung. Fifteen novel factors were shown here to be expressed in mouse lung, and/or human bronchial or distal lung epithelial tissues or lung epithelial cell lineages. AVAILABILITY: DME: http://rulai.cshl.edu/dme. MATCOMPARE: http://rulai.cshl.edu/MatCompare. MOTIFCLASS is available from the authors.

Algorithms↗

PubMed2006

A new method for detecting human recombination hotspots and its applications to the HapMap ENCODE data.

Computational detection of recombination hotspots from population polymorphism data is important both for understanding the nature of recombination and for applications such as association studies. We propose a new method for this task based on a multiple-hotspot model and an (approximate) log-likelihood ratio test. A truncated, weighted pairwise log-likelihood is introduced and applied to the calculation of the log-likelihood ratio, and a forward-selection procedure is adopted to search for the optimal hotspot predictions. The method shows a relatively high power with a low false-positive rate in detecting multiple hotspots in simulation data and has a performance comparable to the best results of leading computational methods in experimental data for which recombination hotspots have been characterized by sperm-typing experiments. The method can be applied to both phased and unphased data directly, with a very fast computational speed. We applied the method to the 10 500-kb regions of the HapMap ENCODE data and found 172 hotspots among the three populations, with average hotspot width of 2.4 kb. By comparisons with the simulation data, we found some evidence that hotspots are not all identical across populations. The correlations between detected hotspots and several genomic characteristics were examined. In particular, we observed that DNaseI-hypersensitive sites are enriched in hotspots, suggesting the existence of human beta hotspots similar to those found in yeast.

Base Composition↗

PubMed2006

Predicting methylation status of CpG islands in the human brain.

MOTIVATION: Over 50% of human genes contain CpG islands in their 5'-regions. Methylation patterns of CpG islands are involved in tissue-specific gene expression and regulation. Mis-epigenetic silencing associated with aberrant CpG island methylation is one mechanism leading to the loss of tumor suppressor functions in cancer cells. Large-scale experimental detection of DNA methylation is still both labor-intensive and time-consuming. Therefore, it is necessary to develop in silico approaches for predicting methylation status of CpG islands. RESULTS: Based on a recent genome-scale dataset of DNA methylation in human brain tissues, we developed a classifier called MethCGI for predicting methylation status of CpG islands using a support vector machine (SVM). Nucleotide sequence contents as well as transcription factor binding sites (TFBSs) are used as features for the classification. The method achieves specificity of 84.65% and sensitivity of 84.32% on the brain data, and can also correctly predict about two-third of the data from other tissues reported in the MethDB database. AVAILABILITY: An online predictor based on MethCGI is available at http://166.111.201.7/MethCGI.html CONTACT: mzhang@cshl.edu SUPPLEMENTARY INFORMATION: Supplementary data available at Bioinformatics online and http://166.111.201.7/help.html.

Algorithms↗

PubMed2006

An increased specificity score matrix for the prediction of SF2/ASF-specific exonic splicing enhancers.

Numerous disease-associated point mutations exert their effects by disrupting the activity of exonic splicing enhancers (ESEs). We previously derived position weight matrices to predict putative ESEs specific for four human SR proteins. The score matrices are part of ESEfinder, an online resource to identify ESEs in query sequences. We have now carried out a refined functional SELEX screen for motifs that can act as ESEs in response to the human SR protein SF2/ASF. The test BRCA1 exon under selection was internal, rather than the 3'-terminal IGHM exon used in our earlier studies. A naturally occurring heptameric ESE in BRCA1 exon 18 was replaced with two libraries of random sequences, one seven nucleotides in length, the other 14. Following three rounds of selection for in vitro splicing via internal exon inclusion, new consensus motifs and score matrices were derived. Many winner sequences were demonstrated to be functional ESEs in S100-extract-complementation assays with recombinant SF2/ASF. Motif-score threshold values were derived from both experimental and statistical analyses. Motif scores were shown to correlate with levels of exon inclusion, both in vitro and in vivo. Our results confirm and extend our earlier data, as many of the same motifs are recognized as ESEs by both the original and our new score matrix, despite the different context used for selection. Finally, we have derived an increased specificity score matrix that incorporates information from both of our SF2/ASF-specific matrices and that accurately predicts the exon-skipping phenotypes of deleterious point mutations.

Alternative Splicing↗

PubMed2006

Computational prediction of methylation status in human genomic sequences.

Epigenetic effects in mammals depend largely on heritable genomic methylation patterns. We describe a computational pattern recognition method that is used to predict the methylation landscape of human brain DNA. This method can be applied both to CpG islands and to non-CpG island regions. It computes the methylation propensity for an 800-bp region centered on a CpG dinucleotide based on specific sequence features within the region. We tested several classifiers for classification performance, including K means clustering, linear discriminant analysis, logistic regression, and support vector machine. The best performing classifier used the support vector machine approach. Our program (called hdfinder) presently has a prediction accuracy of 86%, as validated with CpG regions for which methylation status has been experimentally determined. Using hdfinder, we have depicted the entire genomic methylation patterns for all 22 human autosomes.

Algorithms↗

PubMed2006

Adaptively inferring human transcriptional subnetworks.

Although the human genome has been sequenced, progress in understanding gene regulation in humans has been particularly slow. Many computational approaches developed for lower eukaryotes to identify cis-regulatory elements and their associated target genes often do not generalize to mammals, largely due to the degenerate and interactive nature of such elements. Motivated by the switch-like behavior of transcriptional responses, we present a systematic approach that allows adaptive determination of active transcriptional subnetworks (cis-motif combinations, the direct target genes and physiological processes regulated by the corresponding transcription factors) from microarray data in mammals, with accuracy similar to that achieved in lower eukaryotes. Our analysis uncovered several new subnetworks active in human liver and in cell-cycle regulation, with similar functional characteristics as the known ones. We present biochemical evidence for our predictions, and show that the recently discovered G2/M-specific E2F pathway is wider than previously thought; in particular, E2F directly activates certain mitotic genes involved in hepatocellular carcinomas. Additionally, we demonstrate that this method can predict subnetworks in a condition-specific manner, as well as regulatory crosstalk across multiple tissues. Our approach allows systematic understanding of how phenotypic complexity is regulated at the transcription level in mammals and offers marked advantage in systems where little or no prior knowledge of transcriptional regulation is available.

Algorithms↗

PubMed2006

Characterization of RNase R-digested cellular RNA source that consists of lariat and circular RNAs from pre-mRNA splicing.

Besides linear RNAs, pre-mRNA splicing generates three forms of RNAs: lariat introns, Y-structure introns from trans-splicing, and circular exons through exon skipping. To study the persistence of excised introns in total cellular RNA, we used three Escherichia coli 3' to 5' exoribonucleases. Ribonuclease R (RNase R) thoroughly degrades the abundant linear RNAs and the Y-structure RNA, while preserving the loop portion of a lariat RNA. Ribonuclease II (RNase II) and polynucleotide phosphorylase (PNPase) also preserve the lariat loop, but are less efficient in degrading linear RNAs. RNase R digestion of the total RNA from human skeletal muscle generates an RNA pool consisting of lariat and circular RNAs. RT-PCR across the branch sites confirmed lariat RNAs and circular RNAs in the pool generated by constitutive and alternative splicing of the dystrophin pre-mRNA. Our results indicate that RNase R treatment can be used to construct an intronic cDNA library, in which majority of the intron lariats are represented. The highly specific activity of RNase R implies its ability to screen for rare intragenic trans-splicing in any target gene with a large background of cis-splicing. Further analysis of the intronic RNA pool from a specific tissue or cell will provide insights into the global profile of alternative splicing.

Dystrophin↗

PubMed2006

A clustering property of highly-degenerate transcription factor binding sites in the mammalian genome.

Transcription factor binding sites (TFBSs) are short DNA sequences interacting with transcription factors (TFs), which regulate gene expression. Due to the relatively short length of such binding sites, it is largely unclear how the specificity of protein-DNA interaction is achieved. Here, we have performed a genome-wide analysis of TFBS-like sequences for the transcriptional repressor, RE1 Silencing Transcription Factor (REST), as well as for several other representative mammalian TFs (c-myc, p53, HNF-1 and CREB). We find a nonrandom distribution of inexact sites for these TFs, referred to as highly-degenerate TFBSs, that are enriched around the cognate binding sites. Comparisons among human, mouse and rat orthologous promoters reveal that these highly-degenerate sites are conserved significantly more than expected by random chance, suggesting their positive selection during evolution. We propose that this arrangement provides a favorable genomic landscape for functional target site selection.

Animals↗

PubMed2006

Profiling alternatively spliced mRNA isoforms for prostate cancer classification.

BACKGROUND: Prostate cancer is one of the leading causes of cancer illness and death among men in the United States and world wide. There is an urgent need to discover good biomarkers for early clinical diagnosis and treatment. Previously, we developed an exon-junction microarray-based assay and profiled 1532 mRNA splice isoforms from 364 potential prostate cancer related genes in 38 prostate tissues. Here, we investigate the advantage of using splice isoforms, which couple transcriptional and splicing regulation, for cancer classification. RESULTS: As many as 464 splice isoforms from more than 200 genes are differentially regulated in tumors at a false discovery rate (FDR) of 0.05. Remarkably, about 30% of genes have isoforms that are called significant but do not exhibit differential expression at the overall mRNA level. A support vector machine (SVM) classifier trained on 128 signature isoforms can correctly predict 92% of the cases, which outperforms the classifier using overall mRNA abundance by about 5%. It is also observed that the classification performance can be improved using multivariate variable selection methods, which take correlation among variables into account. CONCLUSION: These results demonstrate that profiling of splice isoforms is able to provide unique and important information which cannot be detected by conventional microarrays.

Algorithms↗

PubMed2006

DNA motifs in human and mouse proximal promoters predict tissue-specific expression.

Comprehensive identification of cis-regulatory elements is necessary for accurately reconstructing gene regulatory networks. We studied proximal promoters of human and mouse genes with differential expression across 56 terminally differentiated tissues. Using in silico techniques to discover, evaluate, and model interactions among sequence elements, we systematically identified regulatory modules that distinguish elevated from inhibited expression in the corresponding transcripts. We used these putative regulatory modules to construct a single predictive model for each of the 56 tissues. These predictors distinguish tissue-specific elevated from inhibited expression with statistical significance in 80% of the tissues (45 of 56). The predictors also reveal synergy between cis-regulatory modules and explain large-scale tissue-specific differential expression. For testis and liver, the predictors include computationally predicted motifs. For most other tissues, the predictors reveal synergy between experimentally verified motifs and indicate genes that are regulated by similar tissue-specific machinery. The identification in proximal promoters of cis-regulatory modules with tissue-specific activity lays the groundwork for complete characterization and deciphering of cis-regulatory DNA code in mammalian genomes.

Animals↗

PubMed2005

Large-scale structure of genomic methylation patterns.

The mammalian genome depends on patterns of methylated cytosines for normal function, but the relationship between genomic methylation patterns and the underlying sequence is unclear. We have characterized the methylation landscape of the human genome by global analysis of patterns of CpG depletion and by direct sequencing of 3073 unmethylated domains and 2565 methylated domains from human brain DNA. The genome was found to consist of short (<4 kb) unmethylated domains embedded in a matrix of long methylated domains. Unmethylated domains were enriched in promoters, CpG islands, and first exons, while methylated domains comprised interspersed and tandem-repeated sequences, exons other than first exons, and non-annotated single-copy sequences that are depleted in the CpG dinucleotide. The enrichment of regulatory sequences in the relatively small unmethylated compartment suggests that cytosine methylation constrains the effective size of the genome through the selective exposure of regulatory sequences. This buffers regulatory networks against changes in total genome size and provides an explanation for the C value paradox, which concerns the wide variations in genome size that scale independently of gene number. This suggestion is compatible with the finding that cytosine methylation is universal among large-genome eukaryotes, while many eukaryotes with genome sizes <5 x 10(8) bp do not methylate their DNA.

Animals↗

PubMed2005

Regulating gene expression through RNA nuclear retention.

Multiple mechanisms have evolved to regulate the eukaryotic genome. We have identified CTN-RNA, a mouse tissue-specific approximately 8 kb nuclear-retained poly(A)+ RNA that regulates the level of its protein-coding partner. CTN-RNA is transcribed from the protein-coding mouse cationic amino acid transporter 2 (mCAT2) gene through alternative promoter and poly(A) site usage. CTN-RNA is diffusely distributed in nuclei and is also localized to paraspeckles. The 3'UTR of CTN-RNA contains elements for adenosine-to-inosine editing, involved in its nuclear retention. Interestingly, knockdown of CTN-RNA also downregulates mCAT2 mRNA. Under stress, CTN-RNA is posttranscriptionally cleaved to produce protein-coding mCAT2 mRNA. Our findings reveal a role of the cell nucleus in harboring RNA molecules that are not immediately needed to produce proteins but whose cytoplasmic presence is rapidly required upon physiologic stress. This mechanism of action highlights an important paradigm for the role of a nuclear-retained stable RNA transcript in regulating gene expression.

3' Untranslated Regions↗

PubMed2005

Distribution of SR protein exonic splicing enhancer motifs in human protein-coding genes.

Exonic splicing enhancers (ESEs) are pre-mRNA cis-acting elements required for splice-site recognition. We previously developed a web-based program called ESEfinder that scores any sequence for the presence of ESE motifs recognized by the human SR proteins SF2/ASF, SRp40, SRp55 and SC35 (http://rulai.cshl.edu/tools/ESE/). Using ESEfinder, we have undertaken a large-scale analysis of ESE motif distribution in human protein-coding genes. Significantly higher frequencies of ESE motifs were observed in constitutive internal protein-coding exons, compared with both their flanking intronic regions and with pseudo exons. Statistical analysis of ESE motif frequency distributions revealed a complex relationship between splice-site strength and increased or decreased frequencies of particular SR protein motifs. Comparison of constitutively and alternatively spliced exons demonstrated slightly weaker splice-site scores, as well as significantly fewer ESE motifs, in the alternatively spliced group. Our results underline the importance of ESE-mediated SR protein function in the process of exon definition, in the context of both constitutive splicing and regulated alternative splicing.

Alternative Splicing↗

PubMed2005

Genome-wide promoter extraction and analysis in human, mouse, and rat.

Large-scale and high-throughput genomics research needs reliable and comprehensive genome-wide promoter annotation resources. We have conducted a systematic investigation on how to improve mammalian promoter prediction by incorporating both transcript and conservation information. This enabled us to build a better multispecies promoter annotation pipeline and hence to create CSHLmpd (Cold Spring Harbor Laboratory Mammalian Promoter Database) for the biomedical research community, which can act as a starting reference system for more refined functional annotations.

Animals↗

PubMed2005

Identifying tissue-selective transcription factor binding sites in vertebrate promoters.

We present a computational method aimed at systematically identifying tissue-selective transcription factor binding sites. Our method focuses on the differences between sets of promoters that are associated with differentially expressed genes, and it is effective at identifying the highly degenerate motifs that characterize vertebrate transcription factor binding sites. Results on simulated data indicate that our method detects motifs with greater accuracy than the leading methods, and its detection of strongly overrepresented motifs is nearly perfect. We present motifs identified by our method as the most overrepresented in promoters of liver- and muscle-selective genes, demonstrating that our method accurately identifies known transcription factor binding sites and previously uncharacterized motifs.

Animals↗

PubMed2005

TRED: a Transcriptional Regulatory Element Database and a platform for in silico gene regulation studies.

In order to understand gene regulation, accurate and comprehensive knowledge of transcriptional regulatory elements is essential. Here, we report our efforts in building a mammalian Transcriptional Regulatory Element Database (TRED) with associated data analysis functions. It collects cis- and trans-regulatory elements and is dedicated to easy data access and analysis for both single-gene-based and genome-scale studies. Distinguishing features of TRED include: (i) relatively complete genome-wide promoter annotation for human, mouse and rat; (ii) availability of gene transcriptional regulation information including transcription factor binding sites and experimental evidence; (iii) data accuracy is ensured by hand curation; (iv) efficient user interface for easy and flexible data retrieval; and (v) implementation of on-the-fly sequence analysis tools. TRED can provide good training datasets for further genome-wide cis-regulatory element prediction and annotation, assist detailed functional studies and facilitate the decipher of gene regulatory networks (http://rulai.cshl.edu/TRED).

Animals↗