Frequency distribution of TATA Box and extension sequences on human promoters

BACKGROUND: TATA box is one of the most important transcription factor binding sites. But the exact sequences of TATA box are still not very clear. RESULTS: In this study, we conduct a dedicated analysis on the frequency distribution of TATA Box and its extension sequences on human promoters. Sixteen TATA elements derived from the TATA Box motif, TATAWAWN, are classified into three distribution patterns: peak, bottom-peak, and bottom. Fourteen TATA extension sequences are predicted to be the new TATA Box elements due to their high motif factors, which indicate their statistical significance. Statistical analysis on the promoters of mice, zebrafish and drosophila melanogaster verifies seven of these elements. It is also observed that the distribution of TATA elements on the promoters of housekeeping genes are very similar with their distribution on the promoters of tissue specific genes in human. CONCLUSION: The dedicated statistical analysis on TATA box and its extension sequences yields new TATA elements. The statistical significance of these elements has been verified on random data sets by calculating their p values

Predicting Housekeeping Genes Based on Fourier Analysis

Housekeeping genes (HKGs) generally have fundamental functions in basic biochemical processes in organisms, and usually have relatively steady expression levels across various tissues. They play an important role in the normalization of microarray technology. Using Fourier analysis we transformed gene expression time-series from a Hela cell cycle gene expression dataset into Fourier spectra, and designed an effective computational method for discriminating between HKGs and non-HKGs using the support vector machine (SVM) supervised learning algorithm which can extract significant features of the spectra, providing a basis for identifying specific gene expression patterns. Using our method we identified 510 human HKGs, and then validated them by comparison with two independent sets of tissue expression profiles. Results showed that our predicted HKG set is more reliable than three previously identified sets of HKGs

Segmentation and intensity estimation for microarray images with saturated pixels

<p>Abstract</p> <p>Background</p> <p>Microarray image analysis processes scanned digital images of hybridized arrays to produce the input spot-level data for downstream analysis, so it can have a potentially large impact on those and subsequent analysis. Signal saturation is an optical effect that occurs when some pixel values for highly expressed genes or peptides exceed the upper detection threshold of the scanner software (2¹⁶- 1 = 65, 535 for 16-bit images). In practice, spots with a sizable number of saturated pixels are often flagged and discarded. Alternatively, the saturated values are used without adjustments for estimating spot intensities. The resulting expression data tend to be biased downwards and can distort high-level analysis that relies on these data. Hence, it is crucial to effectively correct for signal saturation.</p> <p>Results</p> <p>We developed a flexible mixture model-based segmentation and spot intensity estimation procedure that accounts for saturated pixels by incorporating a censored component in the mixture model. As demonstrated with biological data and simulation, our method extends the dynamic range of expression data beyond the saturation threshold and is effective in correcting saturation-induced bias when the lost information is not tremendous. We further illustrate the impact of image processing on downstream classification, showing that the proposed method can increase diagnostic accuracy using data from a lymphoma cancer diagnosis study.</p> <p>Conclusions</p> <p>The presented method adjusts for signal saturation at the segmentation stage that identifies a pixel as part of the foreground, background or other. The cluster membership of a pixel can be altered versus treating saturated values as truly observed. Thus, the resulting spot intensity estimates may be more accurate than those obtained from existing methods that correct for saturation based on already segmented data. As a model-based segmentation method, our procedure is able to identify inner holes, fuzzy edges and blank spots that are common in microarray images. The approach is independent of microarray platform and applicable to both single- and dual-channel microarrays.</p

Comparing factors affecting commencement and cessation of betel quid chewing behavior in Taiwanese adults

<p>Abstract</p> <p>Background</p> <p>Betel quid is the fourth most common used substance in the world after tobacco, alcohol and caffeine. Although factors related to betel quid chewing or cessation of behaviors were reported previously, few studies simultaneously compared both behaviors in the same population. In addition, it is essential to consider time-to-event concept, since the chance of developing or stopping habit may vary over time. The purpose of this study was to compare the risk factors for commencement and cessation of betel quid chewing behaviors in a time-to-event setting.</p> <p>Methods</p> <p>A stratified multi-stage cluster sampling with selection probabilities proportional to size (PPS) was designed for Taiwanese adults with aged 18 years old and above. Kaplan-Meier estimates and Cox proportional hazard regression models were used to compare and calculate the hazard rate ratios for related factors to commencement or cessation of chewing habits.</p> <p>Results</p> <p>In Taiwan, men had a higher betel quid chewing rate (M: 20.9%, W: 1.2%), but woman chewers had a lower cessation rate (M: 27.5%, W: 12.7%). The hazard rate ratio (HRR) of having chewing habit changed from 4.22 (men vs women) univariately to 1.38 multivariablely, which indicated gender differences were confounded by other factors. In multivariable analysis, the risk factors of gender, education and ethnicity were significantly associated with both starting and cessation of betel quid chewing behavior. The factors of occupation, cigarette smoking and alcohol drinking were only associated with starting habit.</p> <p>Conclusion</p> <p>Commencement or cessation of chewing behavior involves a scenario of time, hence it is preferable to use a time-to-event approach for the comparison. The cessation rates of betel quid chewing were decreasingly associated with the daily consumption of betel quid. Hence, reducing of daily amount in betel quid cessation program may be associated with future stopping habit.</p

Web-based tools can be used reliably to detect patients with major depressive disorder and subsyndromal depressive symptoms

BACKGROUND: Although depression has been regarded as a major public health problem, many individuals with depression still remain undetected or untreated. Despite the potential for Internet-based tools to greatly improve the success rate of screening for depression, their reliability and validity has not been well studied. Therefore the aim of this study was to evaluate the test-retest reliability and criterion validity of a Web-based system, the Internet-based Self-assessment Program for Depression (ISP-D). METHODS: The ISP-D to screen for major depressive disorder (MDD), minor depressive disorder (MinD), and subsyndromal depressive symptoms (SSD) was developed in traditional Chinese. Volunteers, 18 years and older, were recruited via the Internet and then assessed twice on the online ISP-D system to investigate the test-retest reliability of the test. They were subsequently prompted to schedule face-to-face interviews. The interviews were performed by the research psychiatrists using the Mini-International Neuropsychiatric Interview and the diagnoses made according to DSM-IV diagnostic criteria were used for the statistics of criterion validity. Kappa (κ) values were calculated to assess test-retest reliability. RESULTS: A total of 579 volunteer subjects were administered the test. Most of the subjects were young (mean age: 26.2 ± 6.6 years), female (77.7%), single (81.6%), and well educated (61.9% college or higher). The distributions of MDD, MinD, SSD and no depression specified were 30.9%, 7.4%, 15.2%, and 46.5%, respectively. The mean time to complete the ISP-D was 8.89 ± 6.77 min. One hundred and eighty-four of the respondents completed the retest (response rate: 31.8%). Our analysis revealed that the 2-week test-retest reliability for ISP-D was excellent (weighted κ = 0.801). Fifty-five participants completed the face-to-face interview for the validity study. The sensitivity, specificity, positive, and negative predictive values for major depressive disorder were 81.8% and 72.7%, 66.7%, and 85.7% respectively. The overall accuracy was 76.4%. CONCLUSION: The evidence indicates the ISP-D is a reliable and valid online tool for assessing depression. Further studies should test the ISP-D in clinical settings to increase its applications in clinical environments with different populations and in a larger sample size

Using Ribosomal Protein Genes as Reference: A Tale of Caution

Background: Housekeeping genes are needed in every tissue as their expression is required for survival, integrity or duplication of every cell. Housekeeping genes commonly have been used as reference genes to normalize gene expression data, the underlying assumption being that they are expressed in every cell type at approximately the same level. Often, the terms "reference genes'' and "housekeeping genes'' are used interchangeably. In this paper, we would like to distinguish between these terms. Consensus is growing that housekeeping genes which have traditionally been used to normalize gene expression data are not good reference genes. Recently, ribosomal protein genes have been suggested as reference genes based on a meta-analysis of publicly available microarray data. Methodology/Principal Findings: We have applied several statistical tools on a dataset of 70 microarrays representing 22 different tissues, to assess and visualize expression stability of ribosomal protein genes. We confirmed the housekeeping status of these genes, but further estimated expression stability across tissues in order to assess their potential as reference genes. One- and two-way ANOVA revealed that all ribosomal protein genes have significant expression variation across tissues and exhibit tissue-dependent expression behavior as a group. Via multidimensional unfolding analysis, we visualized this tissue-dependency. In addition, we explored mechanisms that may cause tissue dependent effects of individual ribosomal protein genes. Conclusions/Significance: Here we provide statistical and biological evidence that ribosomal protein genes exhibit important tissue-dependent variation in mRNA expression. Though these genes are most stably expressed of all investigated genes in a meta-analysis they cannot be considered true reference genes

Alternative splicing and the progesterone receptor in breast cancer

Progesterone receptor status is a marker for hormone responsiveness and disease prognosis in breast cancer. Progesterone receptor negative tumours have generally been shown to have a poorer prognosis than progesterone receptor positive tumours. The observed loss of progesterone receptor could be through a range of mechanisms, including the generation of alternatively spliced progesterone receptor variants that are not detectable by current screening methods. Many progesterone receptor mRNA variants have been described with deletions of various whole, multiple or partial exons that encode differing protein functional domains. These variants may alter the progestin responsiveness of a tissue and contribute to the abnormal growth associated with breast cancer. Absence of specific functional domains from these spliced variants may also make them undetectable or indistinguishable from full length progesterone receptor by conventional antibodies. A comprehensive investigation into the expression profile and activity of progesterone receptor spliced variants in breast cancer is required to advance our understanding of tumour hormone receptor status. This, in turn, may aid the development of new biomarkers of disease prognosis and improve adjuvant treatment decisions

Human Gene Coexpression Landscape: Confident Network Derived from Tissue Transcriptomic Profiles

This is an open-access article distributed under the terms of the Creative Commons Attribution License.[Background]: Analysis of gene expression data using genome-wide microarrays is a technique often used in genomic studies to find coexpression patterns and locate groups of co-transcribed genes. However, most studies done at global >omic> scale are not focused on human samples and when they correspond to human very often include heterogeneous datasets, mixing normal with disease-altered samples. Moreover, the technical noise present in genome-wide expression microarrays is another well reported problem that many times is not addressed with robust statistical methods, and the estimation of errors in the data is not provided. [Methodology/Principal Findings]: Human genome-wide expression data from a controlled set of normal-healthy tissues is used to build a confident human gene coexpression network avoiding both pathological and technical noise. To achieve this we describe a new method that combines several statistical and computational strategies: robust normalization and expression signal calculation; correlation coefficients obtained by parametric and non-parametric methods; random cross-validations; and estimation of the statistical accuracy and coverage of the data. All these methods provide a series of coexpression datasets where the level of error is measured and can be tuned. To define the errors, the rates of true positives are calculated by assignment to biological pathways. The results provide a confident human gene coexpression network that includes 3327 gene-nodes and 15841 coexpression-links and a comparative analysis shows good improvement over previously published datasets. Further functional analysis of a subset core network, validated by two independent methods, shows coherent biological modules that share common transcription factors. The network reveals a map of coexpression clusters organized in well defined functional constellations. Two major regions in this network correspond to genes involved in nuclear and mitochondrial metabolism and investigations on their functional assignment indicate that more than 60% are house-keeping and essential genes. The network displays new non-described gene associations and it allows the placement in a functional context of some unknown non-assigned genes based on their interactions with known gene families. [Conclusions/Significance]: The identification of stable and reliable human gene to gene coexpression networks is essential to unravel the interactions and functional correlations between human genes at an omic scale. This work contributes to this aim, and we are making available for the scientific community the validated human gene coexpression networks obtained, to allow further analyses on the network or on some specific gene associations. The data are available free online at http://bioinfow.dep.usal.es/coexpression/. © 2008 Prieto et al.Funding and grant support was provided by the Ministery of Health, Spanish Government (ISCiii-FIS, MSyC; Project reference PI061153) and by the Ministery of Education, Castilla-Leon Local Government (JCyL; Project reference CSI03A06).Peer Reviewe

Assessment of clusters of transcription factor binding sites in relationship to human promoter, CpG islands and gene expression

BACKGROUND: Gene expression is regulated mainly by transcription factors (TFs) that interact with regulatory cis-elements on DNA sequences. To identify functional regulatory elements, computer searching can predict TF binding sites (TFBS) using position weight matrices (PWMs) that represent positional base frequencies of collected experimentally determined TFBS. A disadvantage of this approach is the large output of results for genomic DNA. One strategy to identify genuine TFBS is to utilize local concentrations of predicted TFBS. It is unclear whether there is a general tendency for TFBS to cluster at promoter regions, although this is the case for certain TFBS. Also unclear is the identification of TFs that have TFBS concentrated in promoters and to what level this occurs. This study hopes to answer some of these questions. RESULTS: We developed the cluster score measure to evaluate the correlation between predicted TFBS clusters and promoter sequences for each PWM. Non-promoter sequences were used as a control. Using the cluster score, we identified a PWM group called PWM-PCP, in which TFBS clusters positively correlate with promoters, and another PWM group called PWM-NCP, in which TFBS clusters negatively correlate with promoters. The PWM-PCP group comprises 47% of the 199 vertebrate PWMs, while the PWM-NCP group occupied 11 percent. After reducing the effect of CpG islands (CGI) against the clusters using partial correlation coefficients among three properties (promoter, CGI and predicted TFBS cluster), we identified two PWM groups including those strongly correlated with CGI and those not correlated with CGI. CONCLUSION: Not all PWMs predict TFBS correlated with human promoter sequences. Two main PWM groups were identified: (1) those that show TFBS clustered in promoters associated with CGI, and (2) those that show TFBS clustered in promoters independent of CGI. Assessment of PWM matches will allow more positive interpretation of TFBS in regulatory regions

Far-Infrared Therapy Induces the Nuclear Translocation of PLZF Which Inhibits VEGF-Induced Proliferation in Human Umbilical Vein Endothelial Cells

Many studies suggest that far-infrared (FIR) therapy can reduce the frequency of some vascular-related diseases. The non-thermal effect of FIR was recently found to play a role in the long-term protective effect on vascular function, but its molecular mechanism is still unknown. In the present study, we evaluated the biological effect of FIR on vascular endothelial growth factor (VEGF)-induced proliferation in human umbilical vein endothelial cells (HUVECs). We found that FIR ranging 3∼10 µm significantly inhibited VEGF-induced proliferation in HUVECs. According to intensity and time course analyses, the inhibitory effect of FIR peaked at an effective intensity of 0.13 mW/cm2 at 30 min. On the other hand, a thermal effect did not inhibit VEGF-induced proliferation in HUVECs. FIR exposure also inhibited the VEGF-induced phosphorylation of extracellular signal-regulated kinases in HUVECs. FIR exposure further induced the phosphorylation of endothelial nitric oxide (NO) synthase (eNOS) and NO generation in VEGF-treated HUVECs. Both VEGF-induced NO and reactive oxygen species generation was involved in the inhibitory effect of FIR. Nitrotyrosine formation significantly increased in HUVECs treated with VEGF and FIR together. Inhibition of phosphoinositide 3-kinase (PI3K) by wortmannin abolished the FIR-induced phosphorylation of eNOS and Akt in HUVECs. FIR exposure upregulated the expression of PI3K p85 at the transcriptional level. We further found that FIR exposure induced the nuclear translocation of promyelocytic leukemia zinc finger protein (PLZF) in HUVECs. This induction was independent of a thermal effect. The small interfering RNA transfection of PLZF blocked FIR-increased PI3K levels and the inhibitory effect of FIR. These data suggest that FIR induces the nuclear translocation of PLZF which inhibits VEGF-induced proliferation in HUVECs