Seroprevalence of Cytomegalovirus among pregnant women and hospitalized children in Palestine.

BACKGROUND: Human Cytomegalovirus (HCMV) is the most common cause of congenital infections. The maternal immune status plays a major role in the likelihood of congenital infection. The aim of this study is to shed light on the seroprevalence of HCMV in pregnant women, hospitalized children and newborns including cases of congenital infections in Palestine. METHODS: We analyzed HCMV IgG and IgM test results that had been ordered for pregnant women, hospitalized children and newborns in the years 2006-2012 at Al-Makassed Islamic Charitable Hospital (MICH) in East Jerusalem. Furthermore, we reviewed the medical charts of newborns and HCMV IgM-positive children. RESULTS: HCMV IgG was positive in 96.6% of pregnant women, in 88% of hospitalized children and in 98.4% of hospitalized newborns. HCMV IgM was positive in 11.5% of pregnant women, in 11.7% of hospitalized children and in 2% of hospitalized newborns respectively. The HCMV avidity assay revealed that 95% of IgM-positive pregnant women had high avidity (>60%) indicating that most Palestinian women were undergoing a recurrent HCMV infection. Real time PCR on limited number of cases indicated that 62.5% of infants, mostly born to IgM-positive mothers and 83.3% of HCMV IgM-positive children had detectable HCMV DNA in their urine. Out of the 249 newborns tested during this study period, four (1.6%) were subjected to Gancyclovir treatment because of symptomatic congenital HCMV infection. CONCLUSIONS: This is the first report to provide an insight into HCMV seroprevalence in Palestine. Despite the high rate of seropositivity, the importance of HCMV testing during pregnancy should not be underestimated. A comprehensive study with a long term follow-up examination of offspring born to HCMV IgM-positive mothers would be required to provide estimates of an accurate percentage of symptomatic congenital HCMV infection in Palestine.

GENI: A web server to identify gene set enrichments in tumor samples.

The Cancer Genome Atlas (TCGA) and analogous projects have yielded invaluable tumor-associated genomic data. Despite several web-based platforms designed to enhance accessibility, certain analyses require prior bioinformatic expertise. To address this need, we developed Gene ENrichment Identifier (GENI, https://www.shaullab.com/geni), which is designed to promptly compute correlations for genes of interest against the entire transcriptome and rank them against well-established biological gene sets. Additionally, it generates comprehensive tables containing genes of interest and their corresponding correlation coefficients, presented in publication-quality graphs. Furthermore, GENI has the capability to analyze multiple genes simultaneously within a given gene set, elucidating their significance within a specific biological context. Overall, GENI's user-friendly interface simplifies the biological interpretation and analysis of cancer patient-associated data, advancing the understanding of cancer biology and accelerating scientific discoveries.

Large-Scale Differential Gene Expression Transcriptomic Analysis Identifies a Metabolic Signature Shared by All Cancer Cells.

Cancer-dependent metabolic rewiring is often manifested by selective expression of enzymes essential for the transformed cells' viability. However, the metabolic variations between normal and transformed cells are not fully characterized, and therefore, a systematic analysis will result in the identification of unknown cellular mechanisms crucial for tumorigenesis. Here, we applied differential gene expression transcriptome analysis to examine the changes in metabolic gene profiles between a wide range of normal tissues and cancer samples. We found that, in contrast to normal tissues which exhibit a tissue-specific expression profile, cancer samples are more homogenous despite their diverse origins. This similarity is due to a "proliferation metabolic signature" (PMS), composed of 158 genes (87 upregulated and 71 downregulated gene sets), where 143 are common to all proliferative cells but 15 are cancer specific. Intriguingly, the PMS gene set is enriched for genes encoding rate-limiting enzymes, and its upregulated set with genes associated with poor patient outcome and essential genes. Among these essential genes is ribulose-5-phosphate-3-epimerase (RPE), which encodes a pentose phosphate pathway enzyme and whose role in cancer is still unclear. Collectively, we identified a set of metabolic genes that can serve as novel cancer biomarkers and potential targets for drug development.