Downregulation of IGF2 expression in third trimester placental tissues from Zika virus infected women in Brazil

OBJECTIVES

Screening for genes differentially expressed in placental tissues, aiming to identify transcriptional signatures that may be involved in ZIKV congenital pathogenesis.

METHODS

Transcriptome data from placental tissues of pregnant women naturally infected with Zika virus during the third trimester were compared to those from women who tested negative for Zika infection. The findings were validated using both a cell culture model and an immunohistochemistry/morphological analysis of naturally infected placental tissues.

RESULTS

Transcriptome analysis revealed that Zika virus infection induces downregulation of insulin-like growth factor II (IGF2) gene, an essential factor for fetal development. The Caco-2 cell culture model that constitutively expresses IGF2 was used for the transcriptome validation. Asiatic and African Zika virus strains infection caused downregulated IGF2 gene expression in Caco-2 cells, whereas other flaviviruses, such as dengue serotype 1, West Nile and wild-type yellow fever viruses, had no effect on this gene expression. Immunohistochemical assays on decidual tissues corroborated our transcriptome analysis, showing that IGF2 is reduced in the decidua of Zika virus-infected women.

CONCLUSIONS

Our results draw attention to IGF2 modulation in uterine tissues, and this finding is expected to support future studies on strategies to ameliorate the harmful effects of Zika virus infection during pregnancy.

Two- and three-dimensional quantitative structure–activity relationships for a series of purine nucleoside phosphorylase inhibitors

Comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis, and hologram quantitative structure-activity relationship (HQSAR) studies were conducted on a series of 52 training set inhibitors of calf spleen purine nucleoside phosphorylase (PNP). Significant cross-validated correlation coefficients (CoMFA, q(2)=0.68; CoMSIA, q(2)=0.66; and HQSAR, q(2)=0.70) were obtained, indicating the potential of the models for untested compounds. The models were then used to predict the inhibitory potency of 16 test set compounds that were not included in the training set, and the predicted values were in good agreement with the experimental results. The final QSAR models along with the information gathered from 3D contour and 2D contribution maps should be useful for the design of novel inhibitors of PNP having improved potency.