Stephen S. Lee

Transcription-Dependent Nuclear-Cytoplasmic Trafficking Is Required for the Function of the von Hippel-Lindau Tumor Suppressor Protein

ABSTRACT Mutation of the von Hippel-Lindau tumor suppressor gene (vhl) causes the von Hippel-Lindau cancer syndrome as well as sporadic renal clear cell carcinoma. To pursue our study of the intracellular localization of VHL protein in relation to its function, we fused VHL to the green fluorescent protein (GFP) to produce the VHL-GFP fusion protein. Like VHL, VHL-GFP binds to elongins B and C and Cullin-2 and regulates target gene product levels, including levels of vascular endothelial growth factor and glucose transporter 1. VHL-GFP localizes predominantly to the cytoplasm, with some detectable nuclear signal. Inhibition of transcription by actinomycin D or 5,6-dichlorobenzimidazole riboside (DRB) causes VHL to be redistributed to the nucleus. A cellular fusion assay was used to demonstrate that inhibition of transcription induces a decrease in the nuclear export rate of VHL. The dependence of transcription for trafficking is lost with a deletion of exon 2, a region with a mutation causing a splice defect in the VHL gene in sporadic renal clear cell carcinoma. Addition of a strong nuclear export signal (NES) derived from the Rev protein results in complete nuclear exclusion and abrogates the redistribution of VHL-GFP-NES into the nucleus upon inhibition of transcription. Leptomycin B, which inhibits NES-mediated nuclear export, reverts the distribution of VHL-GFP-NES to that of VHL-GFP and restores sensitivity to actinomycin D and DRB. Uncoupling of VHL-GFP trafficking to transcription either by an exon 2 deletion or fusion to NES abolishes VHL function. We suggest that VHL function requires not only nuclear or cytoplasmic localization, but also exon 2-mediated transcription-dependent trafficking between these two cellular compartments.

A role for Stat5 in CD8+ T cell homeostasis

Cytokine signals are known to contribute to CD8+ memory T cell homeostasis, but an exact understanding of the mechanism(s) has remained elusive. We have now investigated the role of Stat5 proteins in this process. Whereas Stat5a and Stat5b KO mice have decreased numbers of CD8+ T cells, Stat5-transgenic mice have an increased number of these cells. Stat5b-transgenic mice exhibit increased Ag-induced cell death of CD4+ T cells and augmented proliferation and Bcl-2 expression in CD8+ T cells, providing a basis for this finding. Moreover, CD8+ memory T cells are substantially affected by Stat5 levels. These findings identify Stat5 proteins as critical signaling mediators used by cytokines to regulate CD8+ T cell homeostasis.

Stat5 Synergizes with T Cell Receptor/Antigen Stimulation in the Development of Lymphoblastic Lymphoma

Signal transducer and activator of transcription (STAT) proteins are latent transcription factors that mediate a wide range of actions induced by cytokines, interferons, and growth factors. We now report the development of thymic T cell lymphoblastic lymphomas in transgenic mice in which Stat5a or Stat5b is overexpressed within the lymphoid compartment. The rate of lymphoma induction was markedly enhanced by immunization or by the introduction of TCR transgenes. Remarkably, the Stat5 transgene potently induced development of CD8+ T cells, even in mice expressing a class II–restricted TCR transgene, with resulting CD8+ T cell lymphomas. These data demonstrate the oncogenic potential of dysregulated expression of a STAT protein that is not constitutively activated, and that TCR stimulation can contribute to this process.

Word-based characterization of promoters involved in human DNA repair pathways

BackgroundDNA repair genes provide an important contribution towards the surveillance and repair of DNA damage. These genes produce a large network of interacting proteins whose mRNA expression is likely to be regulated by similar regulatory factors. Full characterization of promoters of DNA repair genes and the similarities among them will more fully elucidate the regulatory networks that activate or inhibit their expression. To address this goal, the authors introduce a technique to find regulatory genomic signatures, which represents a specific application of the genomic signature methodology to classify DNA sequences as putative functional elements within a single organism.ResultsThe effectiveness of the regulatory genomic signatures is demonstrated via analysis of promoter sequences for genes in DNA repair pathways of humans. The promoters are divided into two classes, the bidirectional promoters and the unidirectional promoters, and distinct genomic signatures are calculated for each class. The genomic signatures include statistically overrepresented words, word clusters, and co-occurring words. The robustness of this method is confirmed by the ability to identify sequences that exist as motifs in TRANSFAC and JASPAR databases, and in overlap with verified binding sites in this set of promoter regions.ConclusionThe word-based signatures are shown to be effective by finding occurrences of known regulatory sites. Moreover, the signatures of the bidirectional and unidirectional promoters of human DNA repair pathways are clearly distinct, exhibiting virtually no overlap. In addition to providing an effective characterization method for related DNA sequences, the signatures elucidate putative regulatory aspects of DNA repair pathways, which are notably under-characterized.

Predicting Recidivism in Homicide Offenders Using Classification Tree Analysis

Given the severity of the crime and the lengthy sentences often accompanying convictions, homicide tends to be seen as the culminating event in a criminal career. In an attempt to better understand the types of individuals who commit homicide, many studies have examined the offense history of those convicted of murder and manslaughter. Only recently have researchers begun to realize that in some cases homicide is not an end point in the trajectory of one’s criminal career but rather a potential predictor in a continuing criminal career. Building on existing research, the present study uses a sample of 320 homicide offenders convicted, sentenced, imprisoned, and released in New Jersey from 1990 to 2000 to assess which factors predict future recidivism. We find that classification tree analysis in random forests outperform logistic regressions in classification and prediction of recidivism.

Induction of innate immunity by lipid A mimetics increases survival from pneumonic plague

This study analysed the effect of priming the innate immune system using synthetic lipid A mimetics in a Yersinia pestis murine pulmonary infection model. Two aminoalkyl glucosaminide 4-phosphate (AGP) Toll-like receptor 4 (TLR4) ligands, delivered intranasally, extended time to death or protected against a lethal Y. pestis CO92 challenge. The level of protection was dependent upon the challenge dose of Y. pestis and the timing of AGP therapy. Protection correlated with cytokine induction and a decreased bacterial burden in lung tissue. AGP protection was TLR4-dependent and was not evidenced in transgenic TLR4-deficient mice. AGP therapy augmented with subtherapeutic doses of gentamicin produced dramatically enhanced survival. Combined, these results indicated that AGPs may be useful in protection of immunologically naive individuals against plague and potentially other infectious agents, and that AGP therapy may be used synergistically with other therapies.

Designed to Fit: The Development and Validation of the STRONG-R Recidivism Risk Assessment

Recidivism risk assessment tools have been utilized for decades. Although their implementation and use have the potential to touch nearly every aspect of the correctional system, the creation and examination of optimal development methods have been restricted to a small group of instrument developers. Furthermore, the methodological variation among common instruments used nationally is substantial. The current study examines this variation by reviewing methodologies used to develop several existing assessments and then tests a variety of design variations in an attempt to isolate and select those which provide improved content and predictive performance using a large sample (N = 44,010) of reentering offenders in Washington State. Study efforts were completed in an attempt to isolate and identify potential incremental performance achievements. Findings identify a methodology for improved prediction model performance and, in turn, describe the development and introduction of the Washington State Department of Correction’s recidivism prediction instrument—the Static Risk Offender Need Guide for Recidivism (STRONG-R).

Lipid A Mimetics are Potent Adjuvants for an Intranasal Pneumonic Plague Vaccine

An effective intranasal (i.n.) vaccine against pneumonic plague was developed. The formulation employed two synthetic lipid A mimetics as adjuvant combined with Yersinia pestis-derived V- and F1-protective antigens. The two nontoxic lipid A mimetics, classed as amino-alkyl glucosaminide 4-phosphates (AGPs) are potent ligands for the Toll-like receptor (TLR) 4. Using a murine (BALB/c) pneumonic plague model, we showed a single i.n. application of the vaccine provided 63% protection within 21 days against a Y. pestis CO92 100 LD50 challenge. Protection reached 100% by 150 days. Using a homologous i.n. 1 degrees /2 degrees dose regimen, with the boost administered at varying times, 63% protection was achieved within 7 days and 100% protection was achieved by 21 days after the first immunization. Little or no protection was observed in animals that received antigens alone, and no protection was observed when the vaccine was administered to BALB/c TLR4 mutant mice. Vaccine-induced serum IgG titers to F1 and V-antigen were reflected in high titers for IgG1 and IgG2a, the latter reflecting a bias for a cell-mediated (TH1) immune response. This intranasal vaccine showed 90% protection in Sprague-Dawley rats challenged with 1000 LD50. We conclude that lipid A mimetics are highly effective adjuvants for an i.n. plague vaccine.

WordSeeker: concurrent bioinformatics software for discovering genome-wide patterns and word-based genomic signatures

BackgroundAn important focus of genomic science is the discovery and characterization of all functional elements within genomes. In silico methods are used in genome studies to discover putative regulatory genomic elements (called words or motifs). Although a number of methods have been developed for motif discovery, most of them lack the scalability needed to analyze large genomic data sets.MethodsThis manuscript presents WordSeeker, an enumerative motif discovery toolkit that utilizes multi-core and distributed computational platforms to enable scalable analysis of genomic data. A controller task coordinates activities of worker nodes, each of which (1) enumerates a subset of the DNA word space and (2) scores words with a distributed Markov chain model.ResultsA comprehensive suite of performance tests was conducted to demonstrate the performance, speedup and efficiency of WordSeeker. The scalability of the toolkit enabled the analysis of the entire genome of Arabidopsis thaliana; the results of the analysis were integrated into The Arabidopsis Gene Regulatory Information Server (AGRIS). A public version of WordSeeker was deployed on the Glenn cluster at the Ohio Supercomputer Center.ConclusionWordSeeker effectively utilizes concurrent computing platforms to enable the identification of putative functional elements in genomic data sets. This capability facilitates the analysis of the large quantity of sequenced genomic data.

Technical trading rules as a prior knowledge to a neural networks prediction system for the S&P 500 index

AbstTactFinancial markets data is very noise and non-stationary which makes modeling through machine learning from historical information a challenging problem. Our experience indicates that in markets modeling through neural network learning, significant data preprocessing is needed. We have recently proposed a promising multi-component prediction system for the S&P 500 index which yields a higher return with fewer trades as compared to a neural network predictor alone. The multicomponent system consists of a statistical feature selection, a simple data filtering, two specialized neural networks for extraction of nonlinear relationships from selected data, and a symbolic decision rule base for determining buy/sell recommendations. The objective of this study is to explore if a more sophisticated data filtering process in our multicomponent system leads to further improvements in return or to a reduced number of trades as compared to our current system. The new systems is using some well-known technical trading rules/indicators as a prior symbolic knowledge to develop a directional filter that splits the financial data into up, down, and sideway data sets. We use the directional movement indicators to detect whether the market is trending, and to measure the strength of the trend if it exists. Various experimental results using this system to predict S&P 600 index returns are presented and the result compared to our previously developed multi-component system. The system performance is measured by computing the annual rate of return and the return per trade.