Tachyplesin I is a 17 amino acid, cationic, antimicrobial peptide with a typical cyclic antiparallel ?-sheet structure. Interactions of tachyplesin I with living bacteria are not well understood, although models have been used to elucidate how tachyplesin I permeabilizes membranes. There are several questions to be answered, such as (i) how does tachyplesin I kill bacteria after it penetrates the membrane and (ii) does bacterial death result from the inactivation of intracellular esterases as well as cell injury? In this study, the dynamic antibacterial processes of tachyplesin I and its interactions with Escherichia coli and Staphylococcus aureus were investigated using laser confocal scanning microscopy in combination with electron microscopy. The effects of tachyplesin I on E. coli cell membrane integrity, intracellular enzyme activity, and cell injury and death were investigated by flow cytometric analysis of cells following single- or double-staining with carboxyfluorescein diacetate or propidium iodide. The results of microscopy indicated that tachyplesin I kills bacteria by acting on the cell membrane and intracellular contents, with the cell membrane representing the primary target. Microscopy results also revealed that tachyplesin I uses different modes of action against E. coli and S. aureus. The results of flow cytometry showed that tachyplesin I caused E. coli cell death mainly by compromising cell membrane integrity and causing the inactivation of intracellular esterases. Flow cytometry also revealed dynamic changes in the different subpopulations of cells with increase in tachyplesin I concentrations. Bacteria exposed to 5?g/mL of tachyplesin I did not die instantaneously; instead, they died gradually via a sublethal injury. However, upon exposure to 10-40?g/mL of tachyplesin I, the bacteria died almost immediately. These results contribute to our understanding of the antibacterial mechanism employed by tachyplesin I.
Tachyplesin I (TP I) is an antimicrobial peptide isolated from the hemocytes of the horseshoe crab. With the developments of DNA microarray technology, the genetic analysis of the toxic effect of TP I on embryo was originally considered in our recent study. Based on our microarray data of the embryonic samples of zebrafish treated with the different doses of TP I, we performed a series of statistical data analyses to explore the toxic effect of TP I at the genomic level. In this paper, we first employed the hexaMplot to illustrate the continuous variation of the gene expressions of the embryonic cells treated with the different doses of TP I. The probabilistic model-based Hough transform was used to classify these differentially coexpressed genes of TP I on the zebrafish embryos. As a result, three line rays supported with the corresponding 174 genes were detected in our analysis. Some biological processes of the featured genes, such as antigen processing, nuclear chromatin, and structural constituent of eye lens, were significantly filtered with the smaller P values.
Predicting disease progression is one of the most challenging problems in prostate cancer research. Adding gene expression data to prediction models that are based on clinical features has been proposed to improve accuracy. In the current study, we applied a logistic regression (LR) model combining clinical features and gene co-expression data to improve the accuracy of the prediction of prostate cancer progression. The top-scoring pair (TSP) method was used to select genes for the model. The proposed models not only preserved the basic properties of the TSP algorithm but also incorporated the clinical features into the prognostic models. Based on the statistical inference with the iterative cross validation, we demonstrated that prediction LR models that included genes selected by the TSP method provided better predictions of prostate cancer progression than those using clinical variables only and/or those that included genes selected by the one-gene-at-a-time approach. Thus, we conclude that TSP selection is a useful tool for feature (and/or gene) selection to use in prognostic models and our model also provides an alternative for predicting prostate cancer progression.
The effects of a drug on the genomic scale can be assessed in a three-color cDNA microarray with the three color intensities represented through the so-called hexaMplot. In our recent study, we have shown that the Hough Transform (HT) applied to the hexaMplot can be used to detect groups of coexpressed genes in the normal-disease-drug samples. However, the standard HT is not well suited for the purpose because 1) the assayed genes need first to be hard-partitioned into equally and differentially expressed genes, with HT ignoring possible information in the former group; 2) the hexaMplot coordinates are negatively correlated and there is no direct way of expressing this in the standard HT and 3) it is not clear how to quantify the association of coexpressed genes with the line along which they cluster. We address these deficiencies by formulating a dedicated probabilistic model-based HT. The approach is demonstrated by assessing effects of the drug Rg1 on homocysteine-treated human umbilical vein endothetial cells. Compared with our previous study, we robustly detect stronger natural groupings of coexpressed genes. Moreover, the gene groups show coherent biological functions with high significance, as detected by the Gene Ontology analysis.
The genetic control of prostate cancer development is poorly understood. Large numbers of gene-expression datasets on different aspects of prostate tumorigenesis are available. We used these data to identify and prioritize candidate genes associated with the development of prostate cancer and bone metastases. Our working hypothesis was that combining meta-analyses on different but overlapping steps of prostate tumorigenesis will improve identification of genes associated with prostate cancer development.
DNA rigidity is an important physical property originating from the DNA three-dimensional structure. Although the general DNA rigidity patterns in human promoters have been investigated, their distinct roles in transcription are largely unknown. In this paper, we discover four highly distinct human promoter groups based on similarity of their rigidity profiles. First, we find that all promoter groups conserve relatively rigid DNAs at the canonical TATA box [a consensus TATA(A/T)A(A/T) sequence] position, which are important physical signals in binding transcription factors. Second, we find that the genes activated by each group of promoters share significant biological functions based on their gene ontology annotations. Finally, we find that these human promoter groups correlate with the tissue-specific gene expression.
Identifying genes associated with cancer development is typically accomplished by comparing mean expression values in normal and tumor tissues, which identifies differentially expressed (DE) genes. Interindividual variation (IV) in gene expression is indirectly included in DE gene identification because given the same absolute differences in means, genes with lower variance tend to have lower p-values. We explored the direct use of IV in gene expression to identify candidate genes associated with cancer development. We focused on prostate (PCa) and lung (LC) cancers and compared IV in the expression level of genes shown to be cancer related with that in all other genes in the human genome. Compared with all those other genes, cancer-related genes tended to have greater IV in normal tissues and a greater increase in IV during the transition from normal to tumorous tissue. Genes without significantly different mean expression values between tumor and normal tissues but with greater IV in tumor than in normal tissue (note: the DE-based approach completely ignores those genes) had stronger associations with clinically important features like Gleason score in PCa or tumor histology in LC than all other genes were. Our results suggest that analyzing IV in gene expression level is useful in identifying novel candidate genes associated with cancer development.
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