Biomedical subjects

Leroy Hood

Publications and source records attributed to Leroy Hood.

At least 19 recordsLinked to original sources

PubMed2007

Proteomics analysis of the interactome of N-myc downstream regulated gene 1 and its interactions with the androgen response program in prostate cancer cells.

NDRG1 is known to play important roles in both androgen-induced cell differentiation and inhibition of prostate cancer metastasis. However, the proteins associated with NDRG1 function are not fully enumerated. Using coimmunoprecipitation and mass spectrometry analysis, we identified 58 proteins that interact with NDRG1 in prostate cancer cells. These proteins include nuclear proteins, adhesion molecules, endoplasmic reticulum (ER) chaperons, proteasome subunits, and signaling proteins. Integration of our data with protein-protein interaction data from the Human Proteome Reference Database allowed us to build a comprehensive interactome map of NDRG1. This interactome map consists of several modules such as a nuclear module and a cell membrane module; these modules explain the reported versatile functions of NDRG1. We also determined that serine 330 and threonine 366 of NDRG1 were phosphorylated and demonstrated that the phosphorylation of NDRG1 was prominently mediated by protein kinase A (PKA). Further, we showed that NDRG1 directly binds to beta-catenin and E-cadherin. However, the phosphorylation of NDRG1 did not interrupt the binding of NDRG1 to E-cadherin and beta-catenin. Finally, we showed that the inhibition of NDRG1 expression by RNA interference decreased the ER inducible chaperon GRP94 expression, directly proving that NDRG1 is involved in the ER stress response. Intriguingly, we observed that many members of the NDRG1 interactome are androgen-regulated and that the NDRG1 interactome links to the androgen response network through common interactions with beta-catenin and heat shock protein 90. Therefore we overlaid the transcriptomic expression changes in the NDRG1 interactome in response to androgen treatment and built a dual dynamic picture of the NDRG1 interactome in response to androgen. This interactome map provides the first road map for understanding the functions of NDRG1 in cells and its roles in human diseases, such as prostate cancer, which can progress from androgen-dependent curable stages to androgen-independent incurable stages.

Androgens↗

PubMed2007

ERG upregulation and related ETS transcription factors in prostate cancer.

The aim of this study was to identify and validate differentially expressed genes in matched pairs of benign and malignant prostate tissue. Samples included 29 histologically verified primary tumors and 23 benign controls. Microarray analysis was initially performed using a sequence verified set of 40,000 human cDNA clones. Among the genes most consistently and highly upregulated in prostate cancer was the ETS family transcription factor ERG (ETS related gene). This finding was validated in an expanded patient series (37 tumors and 38 benign samples) using DNA oligonucleotide microarray and real-time quantitative PCR assays. ERG was 20- to more than 100-fold overexpressed in prostate cancer compared with benign prostate tissue in more than 50% of patients according to quantitative PCR. Surprisingly, ERG mRNA levels were found to be significantly higher in the endothelial cell line, HUVEC, than in the prostate cell lines PC3, DU145 and LNCaP. In situ hybridization of prostate cancer tissue revealed that ERG was abundantly expressed in both prostate cancer cells and associated endothelial cells. The consistency and magnitude of ERG overexpression in prostate cancer appeared unique, but several related ETS transcription factors were also overexpressed in matched pairs of tumor and benign samples, whereas ETS2 was significantly underexpressed. Our findings support the hypothesis that ERG overexpression and related ETS transcription factors are important for early prostate carcinogenesis.

DNA Primers↗

PubMed2006

Shotgun glycopeptide capture approach coupled with mass spectrometry for comprehensive glycoproteomics.

We present a robust and general shotgun glycoproteomics approach to comprehensively profile glycoproteins in complex biological mixtures. In this approach, glycopeptides derived from glycoproteins are enriched by selective capture onto a solid support using hydrazide chemistry followed by enzymatic release of the peptides and subsequent analysis by tandem mass spectrometry. The approach was validated using standard protein mixtures that resulted in a close to 100% capture efficiency. Our capture approach was then applied to microsomal fractions of the cisplatin-resistant ovarian cancer cell line IGROV-1/CP. With a Protein Prophet probability value greater than 0.9, we identified a total of 302 proteins with an average protein identification rate of 136 +/- 19 (n = 4) in a single linear quadrupole ion trap (LTQ) mass spectrometer nano-LC-MS experiment and a selectivity of 91 +/- 1.6% (n = 4) for the N-linked glycoconsensus sequence. Our method has several advantages. 1) Digestion of proteins initially into peptides improves the solubility of large membrane proteins and exposes all of the glycosylation sites to ensure equal accessibility to capture reagents. 2) Capturing glycosylated peptides can effectively reduce sample complexity and at the same time increase the confidence of MS-based protein identifications (more potential peptide identifications per protein). 3) The utility of sodium sulfite as a quencher in our capture approach to replace the solid phase extraction step in an earlier glycoprotein chemical capture approach for removing excess sodium periodate allows the overall capture procedure to be completed in a single vessel. This improvement minimizes sample loss, increases sensitivity, and makes our protocol amenable for high throughput implementation, a feature that is essential for biomarker identification and validation of a large number of clinical samples. 4) The approach is demonstrated here on the analysis of N-linked glycopeptides; however, it can be applied equally well to O-glycoprotein analysis.

Amino Acid Sequence↗

PubMed2006

Genetic mapping at 3-kilobase resolution reveals inositol 1,4,5-triphosphate receptor 3 as a risk factor for type 1 diabetes in Sweden.

We mapped the genetic influences for type 1 diabetes (T1D), using 2,360 single-nucleotide polymorphism (SNP) markers in the 4.4-Mb human major histocompatibility complex (MHC) locus and the adjacent 493 kb centromeric to the MHC, initially in a survey of 363 Swedish T1D cases and controls. We confirmed prior studies showing association with T1D in the MHC, most significantly near HLA-DR/DQ. In the region centromeric to the MHC, we identified a peak of association within the inositol 1,4,5-triphosphate receptor 3 gene (ITPR3; formerly IP3R3). The most significant single SNP in this region was at the center of the ITPR3 peak of association (P=1.7 x 10(-4) for the survey study). For validation, we typed an additional 761 Swedish individuals. The P value for association computed from all 1,124 individuals was 1.30 x 10(-6) (recessive odds ratio 2.5; 95% confidence interval [CI] 1.7-3.9). The estimated population-attributable risk of 21.6% (95% CI 10.0%-31.0%) suggests that variation within ITPR3 reflects an important contribution to T1D in Sweden. Two-locus regression analysis supports an influence of ITPR3 variation on T1D that is distinct from that of any MHC class II gene.

Adolescent↗

PubMed2006

Pooled genome linkage scan of aggressive prostate cancer: results from the International Consortium for Prostate Cancer Genetics.

While it is widely appreciated that prostate cancers vary substantially in their propensity to progress to a life-threatening stage, the molecular events responsible for this progression have not been identified. Understanding these molecular mechanisms could provide important prognostic information relevant to more effective clinical management of this heterogeneous cancer. Hence, through genetic linkage analyses, we examined the hypothesis that the tendency to develop aggressive prostate cancer may have an important genetic component. Starting with 1,233 familial prostate cancer families with genome scan data available from the International Consortium for Prostate Cancer Genetics, we selected those that had at least three members with the phenotype of clinically aggressive prostate cancer, as defined by either high tumor grade and/or stage, resulting in 166 pedigrees (13%). Genome-wide linkage data were then pooled to perform a combined linkage analysis for these families. Linkage signals reaching a suggestive level of significance were found on chromosomes 6p22.3 (LOD = 3.0), 11q14.1-14.3 (LOD = 2.4), and 20p11.21-q11.21 (LOD = 2.5). For chromosome 11, stronger evidence of linkage (LOD = 3.3) was observed among pedigrees with an average at diagnosis of 65 years or younger. Other chromosomes that showed evidence for heterogeneity in linkage across strata were chromosome 7, with the strongest linkage signal among pedigrees without male-to-male disease transmission (7q21.11, LOD = 4.1), and chromosome 21, with the strongest linkage signal among pedigrees that had African American ancestry (21q22.13-22.3; LOD = 3.2). Our findings suggest several regions that may contain genes which, when mutated, predispose men to develop a more aggressive prostate cancer phenotype. This provides a basis for attempts to identify these genes, with potential clinical utility for men with aggressive prostate cancer and their relatives.

Black or African American↗

PubMed2006

A molecular correlate to the Gleason grading system for prostate adenocarcinoma.

Adenocarcinomas of the prostate can be categorized into tumor grades based on the extent to which the cancers histologically resemble normal prostate glands. Because grades are surrogates of intrinsic tumor behavior, characterizing the molecular phenotype of grade is of potential clinical importance. To identify molecular alterations underlying prostate cancer grades, we used microdissection to obtain specific cohorts of cancer cells corresponding to the most common Gleason patterns (patterns 3, 4, and 5) from 29 radical prostatectomy samples. We paired each cancer sample with matched benign lumenal prostate epithelial cells and profiled transcript abundance levels by microarray analysis. We identified an 86-gene model capable of distinguishing low-grade (pattern 3) from high-grade (patterns 4 and 5) cancers. This model performed with 76% accuracy when applied to an independent set of 30 primary prostate carcinomas. Using tissue microarrays comprising >800 prostate samples, we confirmed a significant association between high levels of monoamine oxidase A expression and poorly differentiated cancers by immunohistochemistry. We also confirmed grade-associated levels of defender against death (DAD1) protein and HSD17 beta4 transcripts by immunohistochemistry and quantitative RT-PCR, respectively. The altered expression of these genes provides functional insights into grade-associated features of therapy resistance and tissue invasion. Furthermore, in identifying a profile of 86 genes that distinguish high- from low-grade carcinomas, we have generated a set of potential targets for modulating the development and progression of the lethal prostate cancer phenotype.

Adenocarcinoma↗

PubMed2006

Genome-wide analysis of epigenetic silencing identifies BEX1 and BEX2 as candidate tumor suppressor genes in malignant glioma.

Promoter hypermethylation and histone deacetylation are common epigenetic mechanisms implicated in the transcriptional silencing of tumor suppressor genes in human cancer. We treated two immortalized glioma cell lines, T98 and U87, and 10 patient-derived primary glioma cell lines with trichostatin A (TSA), a histone deacetylase inhibitor, or 5-aza-2'-deoxycytidine (5-AzaC), a DNA methyltransferase inhibitor, to comprehensively identify the cohort of genes reactivated through the pharmacologic reversal of these distinct but related epigenetic processes. Whole-genome microarray analysis identified genes induced by TSA (653) or 5-AzaC treatment (170). We selected a subset of reactivated genes that were markedly induced (greater than two-fold) after treatment with either TSA or 5-AzaC in a majority of glioma cell lines but not in cultured normal astrocytes. We then characterized the degree of promoter methylation and transcriptional silencing of selected genes in histologically confirmed human tumor and nontumor brain specimens. We identified two novel brain expressed genes, BEX1 and BEX2, which were silenced in all tumor specimens and exhibited extensive promoter hypermethylation. Viral-mediated reexpression of either BEX1 or BEX2 led to increased sensitivity to chemotherapy-induced apoptosis and potent tumor suppressor effects in vitro and in a xenograft mouse model. Using an integrated approach, we have established a novel platform for the genome-wide screening of epigenetically silenced genes in malignant glioma. This experimental paradigm provides a powerful new method for the identification of epigenetically silenced genes with potential function as tumor suppressors, biomarkers for disease diagnosis and detection, and therapeutically reversible modulators of critical regulatory pathways important in glioma pathogenesis.

Azacitidine↗

PubMed2006

KLK31P is a novel androgen regulated and transcribed pseudogene of kallikreins that is expressed at lower levels in prostate cancer cells than in normal prostate cells.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: KLK31P is a novel androgen regulated and transcribed pseudogene of kallikreins that may play a role in prostate carcinogenesis or maintenance.

Amino Acid Sequence↗

PubMed2006

The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo.

We present a method (the Inferelator) for deriving genome-wide transcriptional regulatory interactions, and apply the method to predict a large portion of the regulatory network of the archaeon Halobacterium NRC-1. The Inferelator uses regression and variable selection to identify transcriptional influences on genes based on the integration of genome annotation and expression data. The learned network successfully predicted Halobacterium's global expression under novel perturbations with predictive power similar to that seen over training data. Several specific regulatory predictions were experimentally tested and verified.

Adenosine Triphosphatases↗

PubMed2006

Long-range multilocus haplotype phasing of the MHC.

Haplotypes are a powerful tool for identifying the genetic basis of common complex diseases. Disease-association mapping requires molecular methods for haplotyping biallelic SNP variation and highly complex polymorphisms. We developed a method for phasing HLA-A, HLA-B, and HLA-DRB1 alleles on chromosome 6 in unrelated individuals. This method uses the highly polymorphic HLA-B locus to discriminate the two HLA haplotypes in heterozygous individuals and its ideal location 1.4 Mbp telomeric to HLA-DRB1 and 1.2 Mbp centromeric to HLA-A to capture 2-Mbp-long genomic DNA. Genomic DNA representing a single HLA-B-captured haplotype is genotyped for HLA-A and HLA-DRB1 alleles and linkage to HLA-B is established. Proof of principle was established in a large blinded study of phase-known samples. Availability of an efficient method for MHC haplotype phase determination will facilitate the mapping of causative MHC-resident genes in many human diseases and has the potential to be broadened to other polymorphic gene complexes.

Genes, MHC Class I↗

PubMed2006

Analysis of the DNA sequence and duplication history of human chromosome 15.

Here we present a finished sequence of human chromosome 15, together with a high-quality gene catalogue. As chromosome 15 is one of seven human chromosomes with a high rate of segmental duplication, we have carried out a detailed analysis of the duplication structure of the chromosome. Segmental duplications in chromosome 15 are largely clustered in two regions, on proximal and distal 15q; the proximal region is notable because recombination among the segmental duplications can result in deletions causing Prader-Willi and Angelman syndromes. Sequence analysis shows that the proximal and distal regions of 15q share extensive ancient similarity. Using a simple approach, we have been able to reconstruct many of the events by which the current duplication structure arose. We find that most of the intrachromosomal duplications seem to share a common ancestry. Finally, we demonstrate that some remaining gaps in the genome sequence are probably due to structural polymorphisms between haplotypes; this may explain a significant fraction of the gaps remaining in the human genome.

Animals↗

PubMed2006

A third approach to gene prediction suggests thousands of additional human transcribed regions.

The identification and characterization of the complete ensemble of genes is a main goal of deciphering the digital information stored in the human genome. Many algorithms for computational gene prediction have been described, ultimately derived from two basic concepts: (1) modeling gene structure and (2) recognizing sequence similarity. Successful hybrid methods combining these two concepts have also been developed. We present a third orthogonal approach to gene prediction, based on detecting the genomic signatures of transcription, accumulated over evolutionary time. We discuss four algorithms based on this third concept: Greens and CHOWDER, which quantify mutational strand biases caused by transcription-coupled DNA repair, and ROAST and PASTA, which are based on strand-specific selection against polyadenylation signals. We combined these algorithms into an integrated method called FEAST, which we used to predict the location and orientation of thousands of putative transcription units not overlapping known genes. Many of the newly predicted transcriptional units do not appear to code for proteins. The new algorithms are particularly apt at detecting genes with long introns and lacking sequence conservation. They therefore complement existing gene prediction methods and will help identify functional transcripts within many apparent "genomic deserts."

Algorithms↗

PubMed2006

Unusual gene order and organization of the sea urchin hox cluster.

While the highly consistent gene order and axial colinear patterns of expression seem to be a feature of vertebrate hox gene clusters, this pattern may be less well conserved across the rest of the bilaterians. We report the first deuterostome instance of an intact hox cluster with a unique gene order where the paralog groups are not expressed in a sequential manner. The finished sequence from BAC clones from the genome of the sea urchin, Strongylocentrotus purpuratus, reveals a gene order wherein the anterior genes (Hox1, Hox2 and Hox3) lie nearest the posterior genes in the cluster such that the most 3' gene is Hox5. (The gene order is 5'-Hox1, 2, 3, 11/13c, 11/13b, 11/13a, 9/10, 8, 7, 6, 5-3'.) The finished sequence result is corroborated by restriction mapping evidence and BAC-end scaffold analyses. Comparisons with a putative ancestral deuterostome Hox gene cluster suggest that the rearrangements leading to the sea urchin gene order were many and complex.

Animals↗

PubMed2005

Proteins associated with Cisplatin resistance in ovarian cancer cells identified by quantitative proteomic technology and integrated with mRNA expression levels.

Nearly all women diagnosed with ovarian cancer receive combination chemotherapy including cis- or carboplatin. Despite high initial response rates, resistance to cisplatin develops in roughly one-third of women during primary treatment and in all women treated for recurrent disease. ICAT coupled with tandem MS is a quantitative proteomic technique for high throughput protein expression profiling of complex protein mixtures. Using ICAT/MS/MS we profiled the nuclear, cytosolic, and microsomal fractions obtained from IGROV-1 [corrected] (cisplatin-sensitive) and IGROV-1/CP [corrected] (cisplatin-resistant) ovarian cancer cell lines. The proteomes of cisplatin-sensitive and -resistant ovarian cancer cells were compared, and protein expression was correlated with mRNA expression profiles. A total of 1117 proteins were identified and quantified. The relative expression of 121 of these varied between the two cell lines. Sixty-three proteins were overexpressed in cisplatin-sensitive, and 58 were over expressed in cisplatin-resistant cells. Examples of proteins at least 5-fold overexpressed in resistant cells and with biological relevance to cancer include cell recognition molecule CASPR3 (13.3-fold), S100 protein family members (8.7-fold), junction adhesion molecule Claudin 4 (7.2-fold), and CDC42-binding protein kinase beta (5.4-fold). Examples of cancer-related proteins at least 5-fold overexpressed in sensitive cells include hepatocyte growth factor inhibitor 1B (13.3-fold) and programmed cell death 6-interacting protein (12.7-fold). The direction of changes in expression levels between proteins and mRNAs were not always in the same direction, possibly reflecting posttranscriptional control of protein expression. We identified proteins whose expression profiles correlate with cisplatin resistance in ovarian cancer cells. Several proteins may be involved in modulating response to cisplatin and have potential as markers of treatment response or treatment targets.

Cell Line, Tumor↗

PubMed2005

A data integration methodology for systems biology.

Different experimental technologies measure different aspects of a system and to differing depth and breadth. High-throughput assays have inherently high false-positive and false-negative rates. Moreover, each technology includes systematic biases of a different nature. These differences make network reconstruction from multiple data sets difficult and error-prone. Additionally, because of the rapid rate of progress in biotechnology, there is usually no curated exemplar data set from which one might estimate data integration parameters. To address these concerns, we have developed data integration methods that can handle multiple data sets differing in statistical power, type, size, and network coverage without requiring a curated training data set. Our methodology is general in purpose and may be applied to integrate data from any existing and future technologies. Here we outline our methods and then demonstrate their performance by applying them to simulated data sets. The results show that these methods select true-positive data elements much more accurately than classical approaches. In an accompanying companion paper, we demonstrate the applicability of our approach to biological data. We have integrated our methodology into a free open source software package named POINTILLIST.

Informatics↗

PubMed2005

A data integration methodology for systems biology: experimental verification.

The integration of data from multiple global assays is essential to understanding dynamic spatiotemporal interactions within cells. In a companion paper, we reported a data integration methodology, designated Pointillist, that can handle multiple data types from technologies with different noise characteristics. Here we demonstrate its application to the integration of 18 data sets relating to galactose utilization in yeast. These data include global changes in mRNA and protein abundance, genome-wide protein-DNA interaction data, database information, and computational predictions of protein-DNA and protein-protein interactions. We divided the integration task to determine three network components: key system elements (genes and proteins), protein-protein interactions, and protein-DNA interactions. Results indicate that the reconstructed network efficiently focuses on and recapitulates the known biology of galactose utilization. It also provided new insights, some of which were verified experimentally. The methodology described here, addresses a critical need across all domains of molecular and cell biology, to effectively integrate large and disparate data sets.

Chromatin Immunoprecipitation↗

PubMed2005

A distinct lineage of CD4 T cells regulates tissue inflammation by producing interleukin 17.

Interleukin 17 (IL-17) has been linked to autoimmune diseases, although its regulation and function have remained unclear. Here we have evaluated in vitro and in vivo the requirements for the differentiation of naive CD4 T cells into effector T helper cells that produce IL-17. This process required the costimulatory molecules CD28 and ICOS but was independent of the cytokines and transcription factors required for T helper type 1 or type 2 differentiation. Furthermore, both IL-4 and interferon-gamma negatively regulated T helper cell production of IL-17 in the effector phase. In vivo, antibody to IL-17 inhibited chemokine expression in the brain during experimental autoimmune encephalomyelitis, whereas overexpression of IL-17 in lung epithelium caused chemokine production and leukocyte infiltration. Thus, IL-17 expression characterizes a unique T helper lineage that regulates tissue inflammation.

Animals↗

PubMed2005

Reverse engineering galactose regulation in yeast through model selection.

We examine the application of statistical model selection methods to reverse-engineering the control of galactose utilization in yeast from DNA microarray experiment data. In these experiments, relationships among gene expression values are revealed through modifications of galactose sugar level and genetic perturbations through knockouts. For each gene variable, we select predictors using a variety of methods, taking into account the variance in each measurement. These methods include maximization of log-likelihood with Cp, AIC, and BIC penalties, bootstrap and cross-validation error estimation, and coefficient shrinkage via the Lasso.

Journal Article↗