Biomedical subjects

Joel S Parker

Publications and source records attributed to Joel S Parker.

13 recordsLinked to original sources

PubMed2006

Novel mRNA targets for tristetraprolin (TTP) identified by global analysis of stabilized transcripts in TTP-deficient fibroblasts.

Tristetraprolin (TTP) is a tandem CCCH zinc finger protein that was identified through its rapid induction by mitogens in fibroblasts. Studies of TTP-deficient mice and cells derived from them showed that TTP could bind to certain AU-rich elements in mRNAs, leading to increases in the rates of mRNA deadenylation and destruction. Known physiological target mRNAs for TTP include tumor necrosis factor alpha, granulocyte-macrophage colony-stimulating factor, and interleukin-2beta. Here we used microarray analysis of RNA from wild-type and TTP-deficient fibroblast cell lines to identify transcripts with different decay rates, after serum stimulation and actinomycin D treatment. Of 250 mRNAs apparently stabilized in the absence of TTP, 23 contained two or more conserved TTP binding sites; nine of these appeared to be stabilized on Northern blots. The most dramatically affected transcript encoded the protein Ier3, recently implicated in the physiological control of blood pressure. The Ier3 transcript contained several conserved TTP binding sites that could bind TTP directly and conferred TTP sensitivity to the mRNA in cell transfection studies. These studies have identified several new, physiologically relevant TTP target transcripts in fibroblasts; these target mRNAs encode proteins from a variety of functional classes.

Animals↗

PubMed2006

Gene expression profiles identify epithelial-to-mesenchymal transition and activation of nuclear factor-kappaB signaling as characteristics of a high-risk head and neck squamous cell carcinoma.

Gene expression signatures generated from DNA microarray analyses have shown promise as predictive biomarkers of clinical outcome. In this study, we determined a high-risk signature for disease recurrence using formalin-fixed head and neck squamous cell carcinoma (HNSCC) tumors and compared the results with an independent data set obtained from fresh frozen tumors. We also showed that genes involved in epithelial-to-mesenchymal transition (EMT) and nuclear factor-kappaB (NF-kappaB) signaling deregulation are the most prominent molecular characteristics of the high-risk tumors. Gene expression was determined in 40 samples, including 34 formalin-fixed tissues and 6 matched frozen tissues, from 29 HNSCC patients. A 75-gene list predictive of disease recurrence was determined by training on the formalin-fixed tumor data set and tested on data from the independent frozen tumor set from 60 HNSCC patients. The difference in recurrence-free survival (RFS) between the high-risk versus low-risk groups in the training and test sets was statistically significant (P = 0.002 and 0.03, respectively, log-rank test). In addition, the gene expression data was interrogated using Gene Set Enrichment Analysis to determine biological significance. The most significant sets of genes enriched in the high-risk tumors were genes involving EMT, NF-kappaB activation, and cell adhesion. In conclusion, global gene expression analysis is feasible using formalin-fixed tissue. The 75-gene list can be used as a prognostic biomarker of recurrence, and our data suggest that the molecular determinants of EMT and NF-kappaB activation can be targeted as the novel therapy in the identified high-risk patients.

Adult↗

PubMed2006

Extensive post-transcriptional regulation of microRNAs and its implications for cancer.

MicroRNAs (miRNAs) are short, noncoding RNAs that post-transcriptionally regulate gene expression. While hundreds of mammalian miRNA genes have been identified, little is known about the pathways that regulate the production of active miRNA species. Here we show that a large fraction of miRNA genes are regulated post-transcriptionally. During early mouse development, many miRNA primary transcripts, including the Let-7 family, are present at high levels but are not processed by the enzyme Drosha. An analysis of gene expression in primary tumors indicates that the widespread down-regulation of miRNAs observed in cancer is due to a failure at the Drosha processing step. These data uncover a novel regulatory step in miRNA function and provide a mechanism for miRNA down-regulation in cancer.

Animals↗

PubMed2005

Hepatic gene expression changes throughout the day in the Fischer rat: implications for toxicogenomic experiments.

There is increasing use of transcriptional profiling in hepatotoxicity studies in the rat. Understanding hepatic gene expression changes over time is critical, since tissue collection may occur throughout the day. Furthermore, when comparing results from different data sets, times of dosing and tissue collection may vary. Circadian effects on the mouse hepatic transcriptome have been well documented. However, limited reports exist for the rat. In one study approximately 7% of the hepatic genes showed a diurnal expression pattern in a comparison of rat liver samples collected during the day versus livers collected at night. The results of a second study comparing rat liver samples collected at multiple time points over a circadian day suggest only minimal variation of the hepatic transcriptome. We studied temporal hepatic gene expression in 48 untreated F344/N rats using both approaches employed in these previous studies. Statistical analysis of microarray (SAM) identified differential expression in day/night comparisons, but was less sensitive for liver samples collected at multiple times of day. However, a Fourier analysis identified numerous periodically expressed genes in these samples including period genes, clock genes, clock-controlled genes, and genes involved in metabolic pathways. Furthermore, rhythms in gene expression were identified for several circadian genes not previously reported in the rat liver. Transcript levels for twenty genes involved in circadian and metabolic pathways were confirmed using quantitative RT-PCR. The results of this study demonstrate a prominent circadian rhythm in gene expression in the rat that is a critical factor in planning toxicogenomic experiments.

Animals↗

PubMed2005

Temporal correlation of pathology and DNA damage with gene expression in a choline-deficient model of rat liver injury.

Hepatocellular carcinoma (HCC) is the terminal event in chronic liver diseases with repeated cycles of cellular injury and regeneration. Although much is known about the cellular pathogenesis and etiological agents leading to HCC, the molecular events are not well understood. The choline-deficient (CD) model of rodent HCC involves the consecutive emergence of a fatty liver, apoptosis, compensatory proliferation, fibrosis, and cirrhosis that is markedly similar to the sequence of events typified by human HCC. Moreover, oxidative stress is thought to play a pivotal role in the progression of the disease. Here, we hypothesize that gene expression profiling can temporally mirror the histopathology and oxidative DNA damage observed with this model. We show that clusters of highly co-regulated genes representing distinct cellular pathways for lipid biosynthesis and metabolism, apoptosis, cell proliferation, and tissue remodeling temporally correlate with the well-defined sequential emergence of pathological alterations in the progression of liver disease. Additionally, an oxidative stress signature was observed that was corroborated in a time-dependent manner with increases in oxidized purines and abasic sites in DNA. Collectively, expression patterns were strongly driven by pathology, demonstrating that patterns of gene expression in advanced stages of liver disease are primarily driven by histopathological changes and to a much lesser degree by the original etiological agent. In conclusion, gene expression profiling coupled with the CD model of HCC provides a unique opportunity to unveil the molecular events associated with various stages of liver injury and carcinogenesis and to distinguish between causal and consecutive changes.

Animals↗

PubMed2005

Variation in the hepatic gene expression in individual male Fischer rats.

A new tool beginning to have wider application in toxicology studies is transcript profiling using microarrays. Microarrays provide an opportunity to directly compare transcript populations in the tissues of chemical-exposed and unexposed animals. While several studies have addressed variation between microarray platforms and between different laboratories, much less effort has been directed toward individual animal differences especially among control animals where RNA samples are usually pooled. Estimation of the variation in gene expression in tissues from untreated animals is essential for the recognition and interpretation of subtle changes associated with chemical exposure. In this study hepatic gene expression as well as standard toxicological parameters were evaluated in 24 rats receiving vehicle only in 2 independent experiments. Unsupervised clustering demonstrated some individual variation but supervised clustering suggested that differentially expressed genes were generally random. The level of hepatic gene expression under carefully controlled study conditions is less than 1.5-fold for most genes. The impact of individual animal variability on microarray data can be minimized through experimental design.

Animals↗

PubMed2005

Transcriptional profiling of the left and median liver lobes of male f344/n rats following exposure to acetaminophen.

The liver is a common organ for transcriptional profiling because of its role in xenobiotic metabolism and because hepatotoxicity is a common response to chemical exposure. To explore the impact that sampling different lobes may have on transcriptional profiling experiments we have examined and compared gene expression profiles of the left and median lobes of livers from male F344 rats exposed to toxic and nontoxic doses of acetaminophen. Transcript profiling using micorarrays revealed clear differences in the response of the left and median liver lobes of F344 rats to acetaminophen exposure both at low doses as well as doses that caused hepatotoxicity. Differences were found in the total number of differentially expressed genes in the left and median lobes, the number and identity of genes that were differentially expressed uniquely only in the left or median lobe, and in the patterns of gene expression. While it is not possible to generalize these results to compounds other than acetaminophen or other strains of rat, these results highlight the potential impact of sampling differences on the interpretation of gene expression profiles in the liver.

Acetaminophen↗

PubMed2005

Microarray data analysis of mouse neoplasia.

Microarray gene expression analysis offers great promise to help us understand the molecular events of experimental carcinogenesis, but have such promises been fulfilled? Studies of gene expression profiles of rodent are being published and demonstrate that yes, indeed, gene array data is furthering our understanding of tumor biology. Recent studies have identified differentially expressed genes in rodent mammary, colon, lung, and liver tumors. Although relatively few genes on the rodent arrays have been fully characterized, information has been generated to better identify signatures of histologic type and grade, understand invasion and metastasis, identify candidate biomarkers of early development, identify gene networks in carcinogenesis, understand responses to therapy, and decifer overlap with molecular events in human cancers. Data from mouse lung, mammary gland, and liver tumor studies are reviewed as examples of how to approach and interpret gene array data. Methods of gene array data analysis were also applied for discovery of genes involved in the regression of mouse liver tumors induced by chlordane, a nongenotoxic murine hepatocarcinogen. Promises are beginning to be fulfilled and it is clear that pathologists and toxicologists, in collaboration with molecular biologists, bioinformatists,and other scientists are making great strides in the design, analysis, and interpretation of microarray data for cancer studies.

Animals↗

PubMed2004

Co-localization of differentially expressed genes and shared susceptibility loci in human autoimmunity.

Autoimmune diseases arise from complex interactions between environmental and genetic factors. Genetic linkage scans show that different autoimmune diseases share overlapping susceptibility loci. Lymphocytes from individuals with different autoimmune diseases, as well as unaffected first-degree relatives, also share a common gene expression profile. We sought to determine if genes within this autoimmune expression profile were nonrandomly distributed in the genome and if their distribution overlapped with shared disease susceptibility loci. We found that differentially expressed genes were distributed in a nonrandom fashion in chromosomal domains within the genome. Furthermore, positions of these domains were not statistically different from a number of shared autoimmune disease susceptibility loci. To our knowledge, this is the first study showing concordance between gene expression and genetic linkage results in common complex multifactorial human diseases.

Adult↗

PubMed2004

Molecular classification of head and neck squamous cell carcinomas using patterns of gene expression.

The prognostication of head and neck squamous cell carcinoma (HNSCC) is largely based upon the tumor size and location and the presence of lymph node metastases. Here we show that gene expression patterns from 60 HNSCC samples assayed on cDNA microarrays allowed categorization of these tumors into four distinct subtypes. These subtypes showed statistically significant differences in recurrence-free survival and included a subtype with a possible EGFR-pathway signature, a mesenchymal-enriched subtype, a normal epithelium-like subtype, and a subtype with high levels of antioxidant enzymes. Supervised analyses to predict lymph node metastasis status were approximately 80% accurate when tumor subsite and pathological node status were considered simultaneously. This work represents an important step toward the identification of clinically significant biomarkers for HNSCC.

Adult↗

PubMed2004

Prediction of toxicant-specific gene expression signatures after chemotherapeutic treatment of breast cell lines.

Global gene expression profiling has demonstrated that the predominant cellular response to a range of toxicants is a general stress response. This stereotyped environmental stress response commonly includes repression of protein synthesis and cell-cycle-regulated genes and induction of DNA damage and oxidative stress-responsive genes. Our laboratory recently characterized the general stress response of breast cell lines derived from basal-like and luminal epithelium after treatment with doxorubicin (DOX) or 5-fluorouracil (5FU) and showed that each cell type has a distinct response. However, we expected that some of the expression changes induced by DOX and 5FU would be unique to each compound and might reflect the underlying mechanisms of action of these agents. Therefore, we employed supervised analyses (significance analysis of microarrays) to identify genes that showed differential expression between DOX-treated and 5FU-treated cell lines. We then used cross-validation analyses and identified genes that afforded high predictive accuracy in classifying samples into the two treatment classes. To test whether these gene lists had good predictive accuracy in an independent data set, we treated our panel of cell lines with etoposide, a compound mechanistically similar to DOX. We demonstrated that using expression patterns of 100 genes we were able to obtain 100% predictive accuracy in classifying the etoposide samples as being more similar in expression to DOX-treated than to 5FU-treated samples. These analyses also showed that toxicant-specific gene expression patterns, similar to general stress responses, vary according to cell type.

Antibiotics, Antineoplastic↗

PubMed2003

Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases.

BACKGROUND: Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining. This can be challenging when the underlying model of the data is unknown. The goal of this study was to determine whether optimizing neural network architecture using genetic programming as a machine learning strategy would improve the ability of neural networks to model and detect nonlinear interactions among genes in studies of common human diseases. RESULTS: Using simulated data, we show that a genetic programming optimized neural network approach is able to model gene-gene interactions as well as a traditional back propagation neural network. Furthermore, the genetic programming optimized neural network is better than the traditional back propagation neural network approach in terms of predictive ability and power to detect gene-gene interactions when non-functional polymorphisms are present. CONCLUSION: This study suggests that a machine learning strategy for optimizing neural network architecture may be preferable to traditional trial-and-error approaches for the identification and characterization of gene-gene interactions in common, complex human diseases.

Algorithms↗

PubMed2002

Symbolic discriminant analysis of microarray data in autoimmune disease.

New laboratory technologies such as DNA microarrays have made it possible to measure the expression levels of thousands of genes simultaneously in a particular cell or tissue. The challenge for genetic epidemiologists will be to develop statistical and computational methods that are able to identify subsets of gene expression variables that classify and predict clinical endpoints. Linear discriminant analysis is a popular multivariate statistical approach for classification of observations into groups. This is because the theory is well described and the method is easy to implement and interpret. However, an important limitation is that linear discriminant functions need to be prespecified. To address this limitation and the limitation of linearity, we have developed symbolic discriminant analysis (SDA) for the automatic selection of gene expression variables and discriminant functions that can take any form. In the present study, we demonstrate that SDA is capable of identifying combinations of gene expression variables that are able to classify and predict autoimmune diseases.

Autoimmune Diseases↗