Validation of the 10 differentially expressed genes obtained from microarray analysis by qRT-PCR, n = 3.

<p>The <i>p</i>-values of the qRT-PCR data are as follows: 0.0067 (C6ORF52), 0.3920 (CCDC84), 0.3661 (THYMOSIN), 0.8195 (PRVE), 0.3154 (HSPCB), 0.6534 (CYP2J2), 0.002 (AMPD3), 0.1622 (TOR1AIP2), 0.8329 (PTGES3), 0.8808 (ACOX3).</p

Genome-Wide Gene Expression Profiles in Lung Tissues of Pig Breeds Differing in Resistance to Porcine Reproductive and Respiratory Syndrome Virus

<div><p>Porcine reproductive and respiratory syndrome (PRRS) caused by PRRS virus (PRRSV) is an infectious disease characterized by severe reproductive deficiency in pregnant sows, typical respiratory symptoms in piglets, and high mortality rate of piglets. In this study, we employed an Affymetrix microarray chip to compare the gene expression profiles of lung tissue samples from Dapulian (DPL) pigs (a Chinese indigenous pig breed) and Duroc×Landrace×Yorkshire (DLY) pigs after infection with PRRSV. During infection with PRRSV, the DLY pigs exhibited a range of clinical features that typify the disease, whereas the DPL pigs showed only mild signs of the disease. Overall, the DPL group had a lower percentage of CD4⁺ cells and lower CD4⁺/CD8⁺ratios than the DLY group (<i>p</i><0.05). For both IL-10 and TNF-α, the DLY pigs had significantly higher levels than the DPL pigs (<i>p</i><0.01). The DLY pigs have lower serum IFN-γ levels than the DPL pigs (<i>p</i><0.01). The serum IgG levels increased slightly from 0 dpi to 7 dpi, and peaked at 14 dpi (<i>p</i><0.0001). Microarray data analysis revealed 16 differentially expressed (DE) genes in the lung tissue samples from the DLY and DPL pigs (q≤5%), of which LOC100516029 and LOC100523005 were up-regulated in the PRRSV-infected DPL pigs, while the other 14 genes were down-regulated in the PRRSV-infected DPL pigs compared with the PRRSV-infected DLY pigs. The mRNA expression levels of 10 out of the 16 DE genes were validated by real-time quantitative RT-PCR and their fold change was consistent with the result of microarray data analysis. We further analyzed the mRNA expression level of 8 differentially expressed genes between the DPL and DLY pigs for both uninfected and infected groups, and found that TF and USP18 genes were important in underlying porcine resistance or susceptibility to PRRSV.</p></div

Differentially expressed genes between PRRSV-infected DPL and DLY pigs.

<p>Differentially expressed genes between PRRSV-infected DPL and DLY pigs.</p

Gene ontology (GO) analysis of differentially expressed genes.

<p>Gene ontology (GO) analysis of differentially expressed genes.</p

Potent, Selective, and Cell Active Protein Arginine Methyltransferase 5 (PRMT5) Inhibitor Developed by Structure-Based Virtual Screening and Hit Optimization

PRMT5 plays important roles in diverse cellular processes and is upregulated in several human malignancies. Besides, PRMT5 has been validated as an anticancer target in mantle cell lymphoma. In this study, we found a potent and selective PRMT5 inhibitor by performing structure-based virtual screening and hit optimization. The identified compound <b>17</b> (IC₅₀ = 0.33 μM) exhibited a broad selectivity against a panel of other methyltransferases. The direct binding of <b>17</b> to PRMT5 was validated by surface plasmon resonance experiments, with a <i>K_d</i> of 0.987 μM. Kinetic experiments indicated that <b>17</b> was a SAM competitive inhibitor other than the substrate. In addition, <b>17</b> showed selective antiproliferative effects against MV4-11 cells, and further studies indicated that the mechanism of cellular antitumor activity was due to the inhibition of PRMT5 mediated SmD3 methylation. <b>17</b> may represent a promising lead compound to understand more about PRMT5 and potentially assist the development of treatments for leukemia indications

Comparison of 8 gene expression levels for both uninfected and infected DPL pigs with the uninfected and infected DLY pigs.

<p>* or ** indicates a statistically significant difference at <i>p</i><0.05 or 0.01, respectively.</p

Volcano plots depicting estimated fold changes (log2, x-axis) and statistically significant differences (−log10, <i>p</i>-value, y-axis).

<p>Each point represents a gene, and colors correspond to the ranges of the negative log₁₀<i>P</i> and log₂ fold change values. Blue circles: differentially expressed genes. Fold changes >0 indicate up-regulated genes, whereas fold changes <0 indicate down-regulated genes. Black circles: no statistically significant expressed genes.</p

Clustered heat map of the statistically significant expressed genes identified between DPL- and DLY-infected pigs.

<p>Each column represents one pig, and each horizontal line refers to one gene. The three columns on the left and right represent the DPL and the DLY pigs, respectively. The color bar at the bottom of the figure indicates the expression level of the genes, those in the lightest blue have lower expression relative to the geometrical means, while dark blue indicates genes with higher expression relative to the geometrical means.</p

Building Quantitative Prediction Models for Tissue Residue of Two Explosives Compounds in Earthworms from Microarray Gene Expression Data

Soil contamination near munitions plants and testing grounds is a serious environmental concern that can result in the formation of tissue chemical residue in exposed animals. Quantitative prediction of tissue residue still represents a challenging task despite long-term interest and pursuit, as tissue residue formation is the result of many dynamic processes including uptake, transformation, and assimilation. The availability of high-dimensional microarray gene expression data presents a new opportunity for computational predictive modeling of tissue residue from changes in expression profile. Here we analyzed a 240-sample data set with measurements of transcriptomic-wide gene expression and tissue residue of two chemicals, 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitro-1,3,5-triazacyclohexane (RDX), in the earthworm <i>Eisenia fetida</i>. We applied two different computational approaches, LASSO (Least Absolute Shrinkage and Selection Operator) and RF (Random Forest), to identify predictor genes and built predictive models. Each approach was tested alone and in combination with a prior variable selection procedure that involved the Wilcoxon rank-sum test and HOPACH (Hierarchical Ordered Partitioning And Collapsing Hybrid). Model evaluation results suggest that LASSO was the best performer of minimum complexity on the TNT data set, whereas the combined Wilcoxon-HOPACH-RF approach achieved the highest prediction accuracy on the RDX data set. Our models separately identified two small sets of ca. 30 predictor genes for RDX and TNT. We have demonstrated that both LASSO and RF are powerful tools for quantitative prediction of tissue residue. They also leave more unknown than explained, however, allowing room for improvement with other computational methods and extension to mixture contamination scenarios

Potent, Selective, and Cell Active Protein Arginine Methyltransferase 5 (PRMT5) Inhibitor Developed by Structure-Based Virtual Screening and Hit Optimization

PRMT5 plays important roles in diverse cellular processes and is upregulated in several human malignancies. Besides, PRMT5 has been validated as an anticancer target in mantle cell lymphoma. In this study, we found a potent and selective PRMT5 inhibitor by performing structure-based virtual screening and hit optimization. The identified compound <b>17</b> (IC₅₀ = 0.33 μM) exhibited a broad selectivity against a panel of other methyltransferases. The direct binding of <b>17</b> to PRMT5 was validated by surface plasmon resonance experiments, with a <i>K_d</i> of 0.987 μM. Kinetic experiments indicated that <b>17</b> was a SAM competitive inhibitor other than the substrate. In addition, <b>17</b> showed selective antiproliferative effects against MV4-11 cells, and further studies indicated that the mechanism of cellular antitumor activity was due to the inhibition of PRMT5 mediated SmD3 methylation. <b>17</b> may represent a promising lead compound to understand more about PRMT5 and potentially assist the development of treatments for leukemia indications