bioRxivSystems BiologyPreprint
The potential role of collagens in congenital Zika syndrome: A systems biology approach
Renato Santana AguiariD, Fabio Baldivia PohliD, Guilherme Loss de MoraisiD, Fábio César Sousa NogueiraiD, Joseane Biso de CarvalhoiD, Letícia da Cunha GuidaiD, Luis WP Arge, Adriana MeloiD, Maria EL Moreira, Daniela Prado CunhaiD, Leonardo Henrique Ferreira GomesiD, Elyzabeth Avvad PortariiD, Erika VelásqueziD, Rafael Donadelli MelaniiD, Paula PezzutoiD, Fernanda Luz de Castro, Victor Emmanuel Viana GeddesiD, Alexandra Lehmkuhl GerberiD, Girlene Souza de AzevedoiD, Bruno Luiz Fonseca Schamber‐ReisiD, Alessandro L. Gonçalves, Inácio L.M. Junqueira-de-AzevedoiD, Milton Yutaka NishiyamaiD, Paulo Lee HoiD, Alessandra Soares SchanoskiiD, , Helder Imoto NakayaiD
Abstract
Zika virus (ZIKV) infection during pregnancy could cause a set of severe abnormalities in the fetus known as congenital Zika syndrome (CZS). Experiments using animal models and in vitro systems significantly contributed to our understanding of the physiopathology of ZIKV infection. However, the molecular basis of CZS is not yet studied in humans. Here, we used a systems biology approach to integrate transcriptomic, proteomic and genomic data from post-mortem brains of neonates with CZS. We observed that collagen genes were greatly reduced in CZS brains at both the RNA and protein levels and that neonates with CZS have several polymorphisms in collagen genes associated with osteogenesis imperfect and arthrogryposis. These findings were validated using immunohistochemistry and collagen staining of ZIKV infected and non-infected samples. Additionally, it was found that cell adhesion genes that are essential for neurite outgrowth and axon guidance were up-regulated and thereby confirmed the neuronal migration defects observed. This work provided new insights into the underlying mechanisms of CZS and revealed host genes associated with CZS susceptibility.
- organism: human
- assay: bulk RNA-seq
- assay: proteomics
- assay: DNA methylation
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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