Emodin Induces Apoptotic Death in Murine Myelomonocytic Leukemia WEHI-3 Cells In Vitro

Emodin is one of major compounds in rhubarb (Rheum palmatum L.), a plant used as herbal medicine in Chinese population. Although many reports have shown that emodin exhibits anticancer activity in many tumor cell types, there is no available information addressing emodin-affected apoptotic responses in the murine leukemia cell line (WEHI-3) and modulation of the immune response in leukemia mice. We investigated that emodin induced cytotoxic effects in vitro and affected WEHI-3 cells in vivo. This study showed that emodin decreased viability and induced DNA fragmentation in WEHI-3 cells. Cells after exposure to emodin for 24 h have shown chromatin condensation and DNA damage. Emodin stimulated the productions of ROS and Ca2+ and reduced the level of ΔΨm by flow cytometry. Our results from Western blotting suggest that emodin triggered apoptosis of WEHI-3 cells through the endoplasmic reticulum (ER) stress, caspase cascade-dependent and -independent mitochondrial pathways. In in vivo study, emodin enhanced the levels of B cells and monocytes, and it also reduced the weights of liver and spleen compared with leukemia mice. Emodin promoted phagocytic activity by monocytes and macrophages in comparison to the leukemia mice group. In conclusions, emodin induced apoptotic death in murine leukemia WEHI-3 cells and enhanced phagocytosis in the leukemia animal model

Constructing Biological Pathways by a Two-Step Counting Approach

Networks are widely used in biology to represent the relationships between genes and gene functions. In Boolean biological models, it is mainly assumed that there are two states to represent a gene: on-state and off-state. It is typically assumed that the relationship between two genes can be characterized by two kinds of pairwise relationships: similarity and prerequisite. Many approaches have been proposed in the literature to reconstruct biological relationships. In this article, we propose a two-step method to reconstruct the biological pathway when the binary array data have measurement error. For a pair of genes in a sample, the first step of this approach is to assign counting numbers for every relationship and select the relationship with counting number greater than a threshold. The second step is to calculate the asymptotic p-values for hypotheses of possible relationships and select relationships with a large p-value. This new method has the advantages of easy calculation for the counting numbers and simple closed forms for the p-value. The simulation study and real data example show that the two-step counting method can accurately reconstruct the biological pathway and outperform the existing methods. Compared with the other existing methods, this two-step method can provide a more accurate and efficient alternative approach for reconstructing the biological network

Inference of Biological Pathway from Gene Expression Profiles by Time Delay Boolean Networks

<div><p>One great challenge of genomic research is to efficiently and accurately identify complex gene regulatory networks. The development of high-throughput technologies provides numerous experimental data such as DNA sequences, protein sequence, and RNA expression profiles makes it possible to study interactions and regulations among genes or other substance in an organism. However, it is crucial to make inference of genetic regulatory networks from gene expression profiles and protein interaction data for systems biology. This study will develop a new approach to reconstruct time delay Boolean networks as a tool for exploring biological pathways. In the inference strategy, we will compare all pairs of input genes in those basic relationships by their corresponding -scores for every output gene. Then, we will combine those consistent relationships to reveal the most probable relationship and reconstruct the genetic network. Specifically, we will prove that state transition pairs are sufficient and necessary to reconstruct the time delay Boolean network of nodes with high accuracy if the number of input genes to each gene is bounded. We also have implemented this method on simulated and empirical yeast gene expression data sets. The test results show that this proposed method is extensible for realistic networks.</p> </div

The eight basic relationships and their probabilistic hypotheses and -scores.

<p>The eight basic relationships and their probabilistic hypotheses and -scores.</p

One example of time delay Boolean network and its input/output.

<p>One example of time delay Boolean network and its input/output.</p

Splitting counts caused by misclassification error.

<p>Splitting counts caused by misclassification error.</p

By the time delay Boolean network in Figure 1, we generate 100 samples with p = 0.05.

<p>By the time delay Boolean network in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042095#pone-0042095-g001" target="_blank">Figure 1</a>, we generate 100 samples with p = 0.05.</p

Count and probabilities table for , and with misclassification error.

<p>Count and probabilities table for , and with misclassification error.</p

Network reconstruct from the expression data of yeast Saccharomyces cerevisiae.

<p>Network reconstruct from the expression data of yeast Saccharomyces cerevisiae.</p

Boolean network <i>G</i>(<i>V,F</i>), wiring diagram <i>G′</i>(<i>V′,F′</i>) and its input/output.

<p>Boolean network <i>G</i>(<i>V,F</i>), wiring diagram <i>G′</i>(<i>V′,F′</i>) and its input/output.</p