Regulation of the cyclin E gene by transcription factor E2F1.

A variety of results point to the transcription factor E2F as a critical determinant of the G1/S-phase transition during the cell cycle in mammalian cells, serving to activate the transcription of a group of genes that encode proteins necessary for DNA replication. In addition, E2F activity appears to be directly regulated by the action of retinoblastoma protein (RB) and RB-related proteins and indirectly regulated through the action of G1 cyclins and associated kinases. We now show that the accumulation of G1 cyclins is regulated by E2F1. E2F binding sites are found in both the cyclin E and cyclin D1 promoters, both promoters are activated by E2F gene products, and at least for cyclin E, the E2F sites contribute to cell cycle-dependent control. Most important, the endogenous cyclin E gene is activated following expression of the E2F1 product encoded by a recombinant adenovirus vector. These results suggest the involvement of E2F1 and cyclin E in an autoregulatory loop that governs the accumulation of critical activities affecting the progression of cells through G1.

Expression of a flower-specific Myb protein in leaf cells using a viral vector causes ectopic activation of a target promoter.

The promoter of the bean PAL2 gene (encoding phenylalanine ammonia-lyase; EC 4.3.1.5) is a model for studies of tissue-restricted gene expression in plants. Petal epidermis is one of the tissues in which this promoter is activated in tobacco. Previous work suggested that a major factor establishing the pattern of PAL2 expression in tobacco petals is the tissue distribution of a protein closely related to Myb305, which is a Myb-like transcriptional activator from snapdragon. In the present work, we show that Myb305 expression in tobacco leaves causes ectopic activation of the PAL2 promoter. To achieve Myb305 expression in planta, a viral expression vector was used. This approach combines the utility of transient assays with the possibility of direct biochemical detection of the introduced factor and may have wider application for studying the function of plant transcription factors.

Chimeric tumor necrosis factor receptors with constitutive signaling activity.

Many hormone and cytokine receptors are crosslinked by their specific ligands, and multimerization is an essential step leading to the generation of a signal. In the case of the tumor necrosis factor (TNF) receptors (TNF-Rs), antibody-induced crosslinking is sufficient to trigger a cytolytic effect. However, the quaternary structural requirements for signaling--i.e., the formation of dimers, trimers, or higher-order multimers--have remained obscure. Moreover, it has not been clear whether the 55-kDa or 75-kDa TNF-R is responsible for initiation of cytolysis. We reasoned that an obligate receptor dimer, targeted to the plasma membrane, might continuously signal the presence of TNF despite the actual absence of the ligand. Such a molecule, inserted into an appropriate vector, could be used to project receptor-specific "TNF-like" activity to specific cells and tissues in vivo. Accordingly, we constructed sequences encoding chimeric receptors in which the extracellular domain of the mouse erythropoietin receptor (Epo-R) was fused to the "stem," transmembrane domain, and cytoplasmic domain of the two mouse TNF-Rs. Thus, the Epo-R group was used to drive dimerization of the TNF-R cytoplasmic domain. These chimeric proteins were well expressed in a variety of cell lines and bound erythropoietin at the cell surface. Both the 55-kDa and the 75-kDa Epo/TNF-R chimeras exerted a constitutive cytotoxic effect detected by cotransfection or clonogenic assay. Thus, despite the lack of structural homology between the cytoplasmic domains of the two TNF-Rs, a similar signaling endpoint was observed. Moreover, dimerization (rather than trimerization or higher-order multimerization) was sufficient for elicitation of a biological response.

Long-term in vivo expression of the human glucocerebrosidase gene in nonhuman primates after CD34+ hematopoietic cell transduction with cell-free retroviral vector preparations.

Successful gene transfer into stem cells would provide a potentially useful therapeutic modality for treatment of inherited and acquired disorders affecting hematopoietic tissues. Coculture of primate bone marrow cells with retroviral producer cells, autologous stroma, or an engineered stromal cell line expressing human stem cell factor has resulted in a low efficiency of gene transfer as reflected by the presence of 0.1-5% of genetically modified cells in the blood of reconstituted animals. Our experiments in a nonhuman primate model were designed to explore various transduction protocols that did not involve coculture in an effort to define clinically useful conditions and to enhance transduction efficiency of repopulating cells. We report the presence of genetically modified cells at levels ranging from 0.1% (granulocytes) to 14% (B lymphocytes) more than 1 year following reconstitution of myeloablated animals with CD34+ immunoselected cells transduced in suspension culture with cytokines for 4 days with a retrovirus containing the glucocerebrosidase gene. A period of prestimulation for 7 days in the presence of autologous stroma separated from the CD34+ cells by a porous membrane did not appear to enhance transduction efficiency. Infusion of transduced CD34+ cells into animals without myeloablation resulted in only transient appearance of genetically modified cells in peripheral blood. Our results document that retroviral transduction of primate repopulating cells can be achieved without coculture with stroma or producer cells and that the proportion of genetically modified cells may be highest in the B-lymphoid lineage under the given transduction conditions.

A regulatory element in the promoter of the human granulocyte-macrophage colony-stimulating factor gene that has related sequences in other T-cell-expressed cytokine genes.

Granulocyte-macrophage colony-stimulating factor (GM-CSF) is a cytokine with a broad spectrum of cell-differentiating and colony-stimulating activities. It is expressed by several undifferentiated (bone marrow stromal cells, fibroblasts) and fully differentiated (T cells, macrophages, and endothelial cells) cells. Its expression in T cells is activation dependent. We have found a regulatory element in the promoter of the GM-CSF gene which contains two symmetrically nested inverted repeats (-192 CTTGGAAAGGTTCATTAATGAAAACCCCCAAG -161). In transfection assays with the human GM-CSF promoter, this element has a strong positive effect on the expression of a reporter gene by the human T-cell line Jurkat J6 upon stimulation with phorbol dibutyrate and ionomycin or anti-CD3 antibody. This element also acts as an enhancer when inserted into a minimal promoter vector. In DNA band-retardation assays this sequence produces six specific bands that involve one or the other of the inverted repeats. We have also shown that a DNA-protein complex can be formed involving both repeats and probably more than one protein. The external inverted repeat contains a core sequence CTTGG...CCAAG, which is also present in the promoters of several other T-cell-expressed human cytokines (interleukins 4, 5, and 13). The corresponding elements in GM-CSF and interleukin 5 promoters compete for the same proteins in band-retardation assays. The palindromic elements in these genes are larger than the core sequence, suggesting that some of the interacting proteins may be different for different genes. Considering the strong positive regulatory effect and their presence in several T-cell-expressed cytokine genes, these elements may be involved in the coordinated expression of these cytokines in T-helper cells.

Better prediction of protein contact number using a support vector regression analysis of amino acid sequence

Background: Protein tertiary structure can be partly characterized via each amino acid's contact number measuring how residues are spatially arranged. The contact number of a residue in a folded protein is a measure of its exposure to the local environment, and is defined as the number of C-beta atoms in other residues within a sphere around the C-beta atom of the residue of interest. Contact number is partly conserved between protein folds and thus is useful for protein fold and structure prediction. In turn, each residue's contact number can be partially predicted from primary amino acid sequence, assisting tertiary fold analysis from sequence data. In this study, we provide a more accurate contact number prediction method from protein primary sequence. Results: We predict contact number from protein sequence using a novel support vector regression algorithm. Using protein local sequences with multiple sequence alignments (PSI-BLAST profiles), we demonstrate a correlation coefficient between predicted and observed contact numbers of 0.70, which outperforms previously achieved accuracies. Including additional information about sequence weight and amino acid composition further improves prediction accuracies significantly with the correlation coefficient reaching 0.73. If residues are classified as being either contacted or non-contacted, the prediction accuracies are all greater than 77%, regardless of the choice of classification thresholds. Conclusion: The successful application of support vector regression to the prediction of protein contact number reported here, together with previous applications of this approach to the prediction of protein accessible surface area and B-factor profile, suggests that a support vector regression approach may be very useful for determining the structure-function relation between primary sequence and higher order consecutive protein structural and functional properties.

Prediction of protein B-factor profiles

The polypeptide backbones and side chains of proteins are constantly moving due to thermal motion and the kinetic energy of the atoms. The B-factors of protein crystal structures reflect the fluctuation of atoms about their average positions and provide important information about protein dynamics. Computational approaches to predict thermal motion are useful for analyzing the dynamic properties of proteins with unknown structures. In this article, we utilize a novel support vector regression (SVR) approach to predict the B-factor distribution (B-factor profile) of a protein from its sequence. We explore schemes for encoding sequences and various settings for the parameters used in SVR. Based on a large dataset of high-resolution proteins, our method predicts the B-factor distribution with a Pearson correlation coefficient (CC) of 0.53. In addition, our method predicts the B-factor profile with a CC of at least 0.56 for more than half of the proteins. Our method also performs well for classifying residues (rigid vs. flexible). For almost all predicted B-factor thresholds, prediction accuracies (percent of correctly predicted residues) are greater than 70%. These results exceed the best results of other sequence-based prediction methods. (C) 2005 Wiley-Liss, Inc.

Effect of disrupted SOX18 transcription factor function on tumor growth, vascularization, and endothelial development

Background. The growth of solid tumors depends on establishing blood supply; thus, inhibiting tumor angiogenesis has been a long-term goal in cancer therapy. The SOX18 transcription factor is a key regulator of murine and human blood vessel formation. Methods: We established allograft melanoma tumors in wild-type mice, Sox18-null mice, and mice expressing a dominant-negative form of Sox18 (Sox18RaOp) (n = 4 per group) and measured tumor growth and microvessel density by immunohistochemical analysis with antibodies to the endothelial marker CD31 and the pericyte marker NG2. We also assessed the affects of disrupted SOX18 function on MCF-7 human breast cancer and human umbilical vein endothelial cell (HUVEC) proliferation by measuring BrdU incorporation and by MTS assay, cell migration using Boyden chamber assay, and capillary tube formation in vitro. All statistical tests were two-sided. Results: Allograft tumors in Sox18-null and Sox18RaOp mice grew more slowly than those in wild-type mice (tumor volume at day 14, Sox18 null, mean = 486 mm(3), 95% confidence interval [CI] = 345 mm(3) to 627 mm(3), p = .004; Sox18RaOp, mean = 233 mm(3), 95% CI = 73 mm(3) to 119 mm(3), p < .001; versus wild-type, mean = 817 mm(3), 95% CI = 643 mm(3) to 1001 mm(3)) and had fewer CD31- and NG2-expressing vessels. Expression of dominant-negative Sox18 reduced the proliferation of MCF-7 cells (BrdU incorporation: MCF-7(Ra) = 20%, 95% CI = 15% to 25% versus MCF-7 = 41%, 95% CI = 35% to 45%; P = .013) and HUVECs (optical density at 490 nm, empty vector, mean = 0.46 versus SOX18 mean = 0.29; difference = 0.17, 95% CI = 0.14 to 0.19; P = .001) compared with control subjects. Overexpression of wild-type SOX18 promoted capillary tube formation of HUVECs in vitro, whereas expression of dominant-negative SOX18 impaired tube formation of HUVECs and the migration of MCF-7 cells via the disruption of the actin cytoskeleton. Conclusions: SOX18 is a potential target for antiangiogenic therapy of human cancers.

Predicting residue-wise contact orders in proteins by support vector regression

Background: The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results: We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. Conclusion: The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

A study of stock market fluctuations and their relation to business conditions

Most research on stock prices is based on the present value model or the more general consumption-based model. When applied to real economic data, both of them are found unable to account for both the stock price level and its volatility. Three essays here attempt to both build a more realistic model, and to check whether there is still room for bubbles in explaining fluctuations in stock prices. In the second chapter, several innovations are simultaneously incorporated into the traditional present value model in order to produce more accurate model-based fundamental prices. These innovations comprise replacing with broad dividends the more narrow traditional dividends that are more commonly used, a nonlinear artificial neural network (ANN) forecasting procedure for these broad dividends instead of the more common linear forecasting models for narrow traditional dividends, and a stochastic discount rate in place of the constant discount rate. Empirical results show that the model described above predicts fundamental prices better, compared with alternative models using linear forecasting process, narrow dividends, or a constant discount factor. Nonetheless, actual prices are still largely detached from fundamental prices. The bubblelike deviations are found to coincide with business cycles. The third chapter examines possible cointegration of stock prices with fundamentals and non-fundamentals. The output gap is introduced to form the nonfundamental part of stock prices. I use a trivariate Vector Autoregression (TVAR) model and a single equation model to run cointegration tests between these three variables. Neither of the cointegration tests shows strong evidence of explosive behavior in the DJIA and S&P 500 data. Then, I applied a sup augmented Dickey-Fuller test to check for the existence of periodically collapsing bubbles in stock prices. Such bubbles are found in S&P data during the late 1990s. Employing econometric tests from the third chapter, I continue in the fourth chapter to examine whether bubbles exist in stock prices of conventional economic sectors on the New York Stock Exchange. The ‘old economy’ as a whole is not found to have bubbles. But, periodically collapsing bubbles are found in Material and Telecommunication Services sectors, and the Real Estate industry group.

A Study of Stock Market Fluctuations and their Relations to Business Conditions

Most research on stock prices is based on the present value model or the more general consumption-based model. When applied to real economic data, both of them are found unable to account for both the stock price level and its volatility. Three essays here attempt to both build a more realistic model, and to check whether there is still room for bubbles in explaining fluctuations in stock prices. In the second chapter, several innovations are simultaneously incorporated into the traditional present value model in order to produce more accurate model-based fundamental prices. These innovations comprise replacing with broad dividends the more narrow traditional dividends that are more commonly used, a nonlinear artificial neural network (ANN) forecasting procedure for these broad dividends instead of the more common linear forecasting models for narrow traditional dividends, and a stochastic discount rate in place of the constant discount rate. Empirical results show that the model described above predicts fundamental prices better, compared with alternative models using linear forecasting process, narrow dividends, or a constant discount factor. Nonetheless, actual prices are still largely detached from fundamental prices. The bubble-like deviations are found to coincide with business cycles. The third chapter examines possible cointegration of stock prices with fundamentals and non-fundamentals. The output gap is introduced to form the non-fundamental part of stock prices. I use a trivariate Vector Autoregression (TVAR) model and a single equation model to run cointegration tests between these three variables. Neither of the cointegration tests shows strong evidence of explosive behavior in the DJIA and S&P 500 data. Then, I applied a sup augmented Dickey-Fuller test to check for the existence of periodically collapsing bubbles in stock prices. Such bubbles are found in S&P data during the late 1990s. Employing econometric tests from the third chapter, I continue in the fourth chapter to examine whether bubbles exist in stock prices of conventional economic sectors on the New York Stock Exchange. The ‘old economy’ as a whole is not found to have bubbles. But, periodically collapsing bubbles are found in Material and Telecommunication Services sectors, and the Real Estate industry group.

Kerr-induced nonlinearity reduction in coherent optical OFDM by low complexity support vector machine regression-based equalization

We experimentally demonstrate 7-dB reduction of nonlinearity penalty in 40-Gb/s CO-OFDM at 2000-km using support vector machine regression-based equalization. Simulation in WDM-CO-OFDM shows up to 12-dB enhancement in Q-factor compared to linear equalization.

Variable 360° Vector-Sum Phase Shifter With Coarse and Fine Vector Scaling

A CMOS vector-sum phase shifter covering the full 360° range is presented in this paper. Broadband operational transconductance amplifiers with variable transconductance provide coarse scaling of the quadrature vector amplitudes. Fine scaling of the amplitudes is accomplished using a passive resistive network. Expressions are derived to predict the maximum bit resolution of the phase shifter from the scaling factor of the coarse and fine vector-scaling stages. The phase shifter was designed and fabricated using the standard 130-nm CMOS process and was tested on-wafer over the frequency range of 4.9–5.9 GHz. The phase shifter delivers root mean square (rms) phase and amplitude errors of 1.25° and 0.7 dB, respectively, at the midband frequency of 5.4 GHz. The input and output return losses are both below 17 dB over the band, and the insertion loss is better than 4 dB over the band. The circuit uses an area of 0.303 mm2 excluding bonding pads and draws 28 mW from a 1.2 V supply.

Porosity imaged by a vector projection algorithm correlates with fractal dimension measured on 3D models obtained by microCT

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