In vivo and in vitro effects of Bletilla striata polysaccharide-loaded paclitaxel nanoparticles on human gastric cancer cells

Purpose: To investigate the in vivo and in vitro effects of paclitaxel nanoparticle (PTX)-loaded Bletilla striata polysaccharide (BSP) on human gastric cancer cells.Methods: Mice weighing 13 to 17 g and aged 4 to 6 weeks, were inoculated with human gastric gland cancer cell line (MKN45), and randomly assigned to five groups: control group, PTX-1 (10 mk/kg) group; PTX-2 (15 mg/kg) group, BSP-PTX-1 (10 mg/kg) group, and BSP-PTX-2 (15 mg/kg) group. The antiproliferative influence of BSP-PTX and its cellular target were determined using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and fluorescence microscopy, respectively.Results: Inhibition of MKN45 cells was significantly higher in BSP-PTX group (88.24 %) than in PTX group (76.74 %, p < 0.05). More BSP-PTX entered the cells than PTX. Tumor inhibition was significantly low in PTX-1 group (37.58 %), relative to the BSP-PTX-I group (45.00 %, p < 0.5). In addition, tumor inhibition was significantly lower in PTX-2 group (52.35 %) than in BSP-PTX-2 group (69.80 %, p < 0.5). The weight gain of mice was lower in the PTX or BSP-PTX groups than in control mice, while the weight gain of mice in BSP-PTX-2 group (26.35 %) was significantly higher than that of PTX-2 group (19.43 %, p < 0.5).Conclusion: Bletilla striata polysaccharide-loaded paclitaxel nanoparticles enhance drug delivery, and effectively and safely exert anti-proliferative effect on MKN45 cells and in mice. Thus, these nanoparticles have good potential for development into anti-gastric cancer agents for clinical application.Keywords: Bletilla striata polysaccharide, Paclitaxel nanoparticles, Human gastric cancer cells, Tumor target, Liver cance

Spatial clustering and common regulatory elements correlate with coordinated gene expression

Many cellular responses to surrounding cues require temporally concerted transcriptional regulation of multiple genes. In prokaryotic cells, a single-input-module motif with one transcription factor regulating multiple target genes can generate coordinated gene expression. In eukaryotic cells, transcriptional activity of a gene is affected by not only transcription factors but also the epigenetic modifications and three-dimensional chromosome structure of the gene. To examine how local gene environment and transcription factor regulation are coupled, we performed a combined analysis of time-course RNA-seq data of TGF-\b{eta} treated MCF10A cells and related epigenomic and Hi-C data. Using Dynamic Regulatory Events Miner (DREM), we clustered differentially expressed genes based on gene expression profiles and associated transcription factors. Genes in each class have similar temporal gene expression patterns and share common transcription factors. Next, we defined a set of linear and radial distribution functions, as used in statistical physics, to measure the distributions of genes within a class both spatially and linearly along the genomic sequence. Remarkably, genes within the same class despite sometimes being separated by tens of million bases (Mb) along genomic sequence show a significantly higher tendency to be spatially close despite sometimes being separated by tens of Mb along the genomic sequence than those belonging to different classes do. Analyses extended to the process of mouse nervous system development arrived at similar conclusions. Future studies will be able to test whether this spatial organization of chromosomes contributes to concerted gene expression.Comment: 30 pages, 9 figures, accepted in PLoS Computational Biolog

Secure Network Function Computation for Linear Functions -- Part I: Source Security

In this paper, we put forward secure network function computation over a directed acyclic network. In such a network, a sink node is required to compute with zero error a target function of which the inputs are generated as source messages at multiple source nodes, while a wiretapper, who can access any one but not more than one wiretap set in a given collection of wiretap sets, is not allowed to obtain any information about a security function of the source messages. The secure computing capacity for the above model is defined as the maximum average number of times that the target function can be securely computed with zero error at the sink node with the given collection of wiretap sets and security function for one use of the network. The characterization of this capacity is in general overwhelmingly difficult. In the current paper, we consider securely computing linear functions with a wiretapper who can eavesdrop any subset of edges up to a certain size r, referred to as the security level, with the security function being the identity function. We first prove an upper bound on the secure computing capacity, which is applicable to arbitrary network topologies and arbitrary security levels. When the security level r is equal to 0, our upper bound reduces to the computing capacity without security consideration. We discover the surprising fact that for some models, there is no penalty on the secure computing capacity compared with the computing capacity without security consideration. We further obtain an equivalent expression of the upper bound by using a graph-theoretic approach, and accordingly we develop an efficient approach for computing this bound. Furthermore, we present a construction of linear function-computing secure network codes and obtain a lower bound on the secure computing capacity