The single-stranded RNA Flavivirus, Zika virus (ZIKV), has recently re-emerged and spread rapidly across the western hemispheres equatorial countries, primarily through Aedes mosquito transmission. While symptoms in adult infections appear to be self-limiting and mild, severe birth defects, such as microcephaly, have been linked to infection during early pregnancy. Recently, Tang et al. (Cell Stem Cell 2016, doi: 10.1016/j.stem.2016.02.016 ) demonstrated that ZIKV efficiently infects induced pluripotent stem cell (iPSC) derived human neural progenitor cells (hNPCs), resulting in cell cycle abnormalities and apoptosis. Consequently, hNPCs are a suggested ZIKV target. We analyzed the transcriptomic sequencing (RNA-seq) data (GEO: GSE78711) of ZIKV (Strain: MR766) infected hNPCs. For comparison to the ZIKV-infected hNPCs, the expression data from hNPCs infected with human cytomegalovirus (CMV) (Strain: AD169) was used (GEO: GSE35295). Utilizing a combination of Gene Ontology, database of human diseases, and pathway analysis, we generated a putative systemic model of infection supported by known molecular pathways of other highly related viruses. We analyzed RNA-sequencing data for transcript expression alterations in ZIKV-infected hNPCs, and then compared them to expression patterns of iPSC-derived hNPCs infected with CMV, a virus that can also induce severe congenital neurological defects in developing fetuses. We demonstrate for the first time that many of cellular pathways correlate with clinical pathologies following ZIKV infection such as microcephaly, congenital nervous

The quantitative comparison of two or more microarrays can reveal, for example, the distinct patterns of gene expression that define different cellular phenotypes or the genes that are induced in the cellular response to certain stimulations. Normalization of the measured intensities is a prerequisite of such comparisons. However, a fundamental problem in cDNA microarray analysis is the lack of a common standard to compare the expression levels of different samples. Several normalization protocols have been proposed to overcome the variabilities inherent in this technology. We have developed a normalization procedure based on within-array replications via a semilinear in-slide model, which adjusts objectively experimental variations without making critical biological assumptions. The significant analysis of gene expressions is based on a weighted <i>t</i> statistic, which accounts for the heteroscedasticity of the

It has been reported that the plasma levels of VEGF in tumor patients decreased during dendritic cell (DC)-based immunotherapy, but the underlying mechanism remains unclear. Our current report demonstrates that VEGF levels were significantly decreased in the supernatants of DCs incubated with rhVEGF or tumor conditioned medium (TCM) while the intracellular VEGF in DCs was increased. The increased intracellular VEGF was not due to the <i>de novo</i> VEGF synthesis by DCs because exogenous VEGF inhibited the mRNA expression of VEGF in DCs. More direct evidence was provided to demonstrate that Cy3-labeled VEGF could be internalized by DCs specifically and efficiently. In addition, the activity of DCs to internalize VEGF was abolished by neutralizing antibody against VEGF receptor-1 (Flt-1) and inhibitors of endocytosis such as carbonyl cyanide m-chlorophenyl hydrazone (CCCP) and genistein. This

Microarray techniques have been widely used to monitor gene expression in many areas of biomedical research. They have been widely used for tumor diagnosis and classification, prediction of prognoses and treatment, and understanding of molecular mechanisms, biochemical pathways, and gene networks. Statistical methods are vital for these scientific endeavors. This article reviews recent developments of statistical methods for analyzing data from microarray experiments. Emphasis has been given to normalization of expression from multiple arrays, selecting significantly differentially expressed genes, tumor classifications, and gene expression pathways and networks.

Severe acute respiratory syndrome (SARS) is a new infectious disease with a global impact. Understanding its pathogenesis and developing specific diagnostic methods for its early diagnosis are crucial for the effective management and control of this disease. By using proteomic technology, truncated forms of ₁antitrypsin (TF₁AT) were found to increase significantly and consistently in sera of SARS patients compared to control subjects. The result showed a sensitivity of 100% for SARS patients and a specificity of 92.8% for controls. Furthermore, the levels of these proteins significantly correlated with certain clinicopathological parameters. The dramatic increase in TF₁AT may be the result of degradation of ₁AT. As ₁AT plays an important role in the protection of lung function, its degradation may be an important factor in the pathogenesis of SARS. These findings indicate that increased TF₁AT