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Jared C Roach,
Kelly D Smith,
Katie L Strobe,
Stephanie M Nissen,
Christian D Haudenschild,
Daixing Zhou, Thomas J Vasicek,
G A Held,
Gustavo A Stolovitzky,
Leroy E Hood,
Alan Aderem
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ABSTRACT: Transcription factors play a key role in integrating and modulating biological information. In this study, we comprehensively measured the changing abundances of mRNAs over a time course of activation of human peripheral-blood-derived mononuclear cells ("macrophages") with lipopolysaccharide. Global and dynamic analysis of transcription factors in response to a physiological stimulus has yet to be achieved in a human system, and our efforts significantly advanced this goal. We used multiple global high-throughput technologies for measuring mRNA levels, including massively parallel signature sequencing and GeneChip microarrays. We identified 92 of 1,288 known human transcription factors as having significantly measurable changes during our 24-h time course. At least 42 of these changes were previously unidentified in this system. Our data demonstrate that some transcription factors operate in a functional range below 10 transcripts per cell, whereas others operate in a range three orders of magnitude greater. The highly reproducible response of many mRNAs indicates feedback control. A broad range of activation kinetics was observed; thus, combinatorial regulation by small subsets of transcription factors would permit almost any timing input to cis-regulatory elements controlling gene transcription.
Proceedings of the National Academy of Sciences 11/2007; 104(41):16245-50. · 9.68 Impact Factor
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ABSTRACT: Fifteen human tissue kallikrein (KLK) genes have been identified as a cluster on chromosome 19. KLK expression is associated with various human diseases including cancers. Noncoding RNAs such as PCA3/DD3 and PCGEM1 have been identified in prostate cancer cells.
Using massively parallel signature sequencing (MPSS) technology, RT-PCR, and 5' rapid amplification of cDNA ends (RACE), we identified and cloned a novel gene that maps to the KLK locus.
We have characterized this gene, named as KLK31P by the HUGO Gene Nomenclature Committee, as an unprocessed KLK pseudogene. It contains five exons, two of which are KLK-derived while the rest are "exonized" interspersed repeats. KLK31P is expressed abundantly in prostate tissues and is androgen regulated. KLK31P is expressed at lower levels in localized and metastatic prostate cancer cells than in normal prostate cells.
KLK31P is a novel androgen regulated and transcribed pseudogene of kallikreins that may play a role in prostate carcinogenesis or maintenance.
The Prostate 07/2006; 66(9):936-44. · 3.48 Impact Factor
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Gregory E Crawford,
Ingeborg E Holt,
James Whittle,
Bryn D Webb,
Denise Tai,
Sean Davis,
Elliott H Margulies,
YiDong Chen,
John A Bernat,
David Ginsburg,
Daixing Zhou,
Shujun Luo, Thomas J Vasicek,
Mark J Daly,
Tyra G Wolfsberg,
Francis S Collins
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ABSTRACT: A major goal in genomics is to understand how genes are regulated in different tissues, stages of development, diseases, and species. Mapping DNase I hypersensitive (HS) sites within nuclear chromatin is a powerful and well-established method of identifying many different types of regulatory elements, but in the past it has been limited to analysis of single loci. We have recently described a protocol to generate a genome-wide library of DNase HS sites. Here, we report high-throughput analysis, using massively parallel signature sequencing (MPSS), of 230,000 tags from a DNase library generated from quiescent human CD4+ T cells. Of the tags that uniquely map to the genome, we identified 14,190 clusters of sequences that group within close proximity to each other. By using a real-time PCR strategy, we determined that the majority of these clusters represent valid DNase HS sites. Approximately 80% of these DNase HS sites uniquely map within one or more annotated regions of the genome believed to contain regulatory elements, including regions 2 kb upstream of genes, CpG islands, and highly conserved sequences. Most DNase HS sites identified in CD4+ T cells are also HS in CD8+ T cells, B cells, hepatocytes, human umbilical vein endothelial cells (HUVECs), and HeLa cells. However, approximately 10% of the DNase HS sites are lymphocyte specific, indicating that this procedure can identify gene regulatory elements that control cell type specificity. This strategy, which can be applied to any cell line or tissue, will enable a better understanding of how chromatin structure dictates cell function and fate.
Genome Research 02/2006; 16(1):123-31. · 13.61 Impact Factor
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ABSTRACT: Massively parallel signature sequencing is an ultra-high throughput sequencing technology. It can simultaneously sequence millions of sequence tags, and, therefore, is ideal for whole genome analysis. When applied to expression profiling, it reveals almost every transcript in the sample and provides its accurate expression level. This chapter describes the technology and its application in establishing stem cell transcriptome databases.
Methods in molecular biology (Clifton, N.J.) 02/2006; 331:285-311.
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ABSTRACT: HTLV-I is the etiologic agent of adult T-cell leukemia (ATL), a fatal T-cell malignancy that is associated with profound immunosuppression. In this study, comprehensive gene expression profiling was performed using massively parallel signature sequencing (MPSS) to investigate virus-host interactions in acutely HTLV-1 transformed cells. The analysis revealed the modulation of numerous genes across different functional classes, many of which have not been previously implicated in HTLV-1 transformation or ATL. Differences in the transcriptomes of transformed cell lines were observed that have provided clues on how different clonal populations of cells respond to virus transformation. Quantitation of HTLV-1 transcription was possible, thus making MPSS a useful tool to study emerging pathogens and unknown microbial causes of human diseases. Importantly, overexpression of GITR, an activation marker that has not been previously reported to be upregulated by HTLV-1-infection or in transformed/leukemic cells and that is associated with the suppressor phenotype of CD4+CD25+ regulatory T-cells (Tregs), was also observed. The deep and quantitative gene expression profile generated by MPSS should provide additional leads for discovery research that can be applied to better understand the pathobiology of HTLV-1 transformation and ATL as well as to developing new therapies.
Biochemical and Biophysical Research Communications 08/2005; 332(2):569-84. · 2.48 Impact Factor
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C Victor Jongeneel,
Mauro Delorenzi,
Christian Iseli,
Daixing Zhou,
Christian D Haudenschild,
Irina Khrebtukova,
Dmitry Kuznetsov,
Brian J Stevenson,
Robert L Strausberg,
Andrew J G Simpson, Thomas J Vasicek
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ABSTRACT: We have used massively parallel signature sequencing (MPSS) to sample the transcriptomes of 32 normal human tissues to an unprecedented depth, thus documenting the patterns of expression of almost 20,000 genes with high sensitivity and specificity. The data confirm the widely held belief that differences in gene expression between cell and tissue types are largely determined by transcripts derived from a limited number of tissue-specific genes, rather than by combinations of more promiscuously expressed genes. Expression of a little more than half of all known human genes seems to account for both the common requirements and the specific functions of the tissues sampled. A classification of tissues based on patterns of gene expression largely reproduces classifications based on anatomical and biochemical properties. The unbiased sampling of the human transcriptome achieved by MPSS supports the idea that most human genes have been mapped, if not functionally characterized. This data set should prove useful for the identification of tissue-specific genes, for the study of global changes induced by pathological conditions, and for the definition of a minimal set of genes necessary for basic cell maintenance. The data are available on the Web at http://mpss.licr.org and http://sgb.lynxgen.com.
Genome Research 08/2005; 15(7):1007-14. · 13.61 Impact Factor
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Biaoyang Lin,
James T White,
Wei Lu,
Tao Xie,
Angelita G Utleg,
Xiaowei Yan,
Eugene C Yi,
Paul Shannon,
Irina Khrebtukova,
Paul H Lange,
David R Goodlett,
Daixing Zhou, Thomas J Vasicek,
Leroy Hood
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ABSTRACT: Prostate cancer is initially responsive to androgen ablation therapy and progresses to androgen-unresponsive states that are refractory to treatment. The mechanism of this transition is unknown. A systems approach to disease begins with the quantitative delineation of the informational elements (mRNAs and proteins) in various disease states. We employed two recently developed high-throughput technologies, massively parallel signature sequencing (MPSS) and isotope-coded affinity tag, to gain a comprehensive picture of the changes in mRNA levels and more restricted analysis of protein levels, respectively, during the transition from androgen-dependent LNCaP (model for early-stage prostate cancer) to androgen-independent CL1 cells (model for late-stage prostate cancer). We sequenced >5 million MPSS signatures, obtained >142,000 tandem mass spectra, and built comprehensive MPSS and proteomic databases. The integrated mRNA and protein expression data revealed underlying functional differences between androgen-dependent and androgen-independent prostate cancer cells. The high sensitivity of MPSS enabled us to identify virtually all of the expressed transcripts and to quantify the changes in gene expression between these two cell states, including functionally important low-abundance mRNAs, such as those encoding transcription factors and signal transduction molecules. These data enable us to map the differences onto extant physiologic networks, creating perturbation networks that reflect prostate cancer progression. We found 37 BioCarta and 14 Kyoto Encyclopedia of Genes and Genomes pathways that are up-regulated and 23 BioCarta and 22 Kyoto Encyclopedia of Genes and Genomes pathways that are down-regulated in LNCaP cells versus CL1 cells. Our efforts represent a significant step toward a systems approach to understanding prostate cancer progression.
Cancer Research 05/2005; 65(8):3081-91. · 7.86 Impact Factor
