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Epstein-Barr virus (EBV) nuclear antigen 1 (EBNA-1) plays key roles in both the regulation of gene expression and the replication of the EBV genome in latently infected cells. To characterize the RNA-binding activity of EBNA-1, it was demonstrated that EBNA-1 binds efficiently to RNA homopolymers that are composed of poly(G) and weakly to those composed of poly(U). All three RGG boxes of EBNA-1 contributed additively to poly(G)-binding activity and could mediate RNA binding when attached to a heterologous protein in an RNA gel mobility-shift assay. In vitro-transcribed EBV and non-EBV RNA probes revealed that EBNA-1 bound to most RNAs examined and the affinity increased as the content of G and U increased, as demonstrated in competition assays. Among these probes, the 59 non-coding region (NCR) (nt 131-278) of hepatitis C virus RNA appeared to be the strongest competitor for EBNA-1 binding to the EBV-encoded small nuclear RNA 1 (EBER1) probe, whereas a mutant 59 NCR RNA with partially disrupted secondary structure was a weak competitor. Furthermore, the interaction of endogenous EBNA-1 and EBER1 in EBV-infected cells was demonstrated by a ribonucleoprotein immunoprecipitation assay. These results revealed that EBNA-1 is a DNA-binding protein with strong binding activity to a relatively broad spectrum of RNA and suggested an additional biological impact of EBNA-1 through its ability to bind to RNA.

Abstract. We evaluate the effectiveness of neural networks as a tool for predicting whether a particular combination of preconditioner and iterative method will correctly solve a given sparse linear system Ax = b. We consider several scenarios corresponding to different assumptions about the relationship between the systems used to train the neural network and those for which the neural network is expected to predict behavior. Greater similarity between those two sets leads to better accuracy, but even when the two sets are very different prediction accuracy can be improved by using additional computation.

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Di(2-ethylhexyl) phthalate (DEHP) is a common plasticizer that is widely used in many consumer products and medical devices. Humans can be exposed to DEHP through ingestion, inhalation, or dermal absorption. Previous studies on DEHP have focused on its role as an endocrine-disrupting chemical leading to endocrine-related diseases. However, the correlation between DEHP exposure and the progression of colorectal cancer (CRC) is largely unknown. The aim of this study was to investigate the effects of mono(2-ethylhexyl) phthalate (MEHP), an active metabolite of DEHP, on the progression of CRC. Our results showed that treatment with MEHP enriched the population of cancer-stem-cell (CSC)-like cells and upregulated IL-8 expression by inducing the AKT-β-catenin-TCF4 signaling pathway. Blocking β-catenin-TCF4-mediated IL-8 expression reversed the MEHP-induced migration and enrichment of CSC-like cells. Consistent with the in vitro data, DEHP treatment increased the levels of nuclear β-catenin, polyp formation, and invasive adenocarcinoma in a mouse model. Our results suggest that MEHP facilitates the progression of CRC through AKT-β-catenin signaling.

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