Impact of COVID-19 during pregnancy on infant neurodevelopment

• Grant number: 22/11795-3
• Support Opportunities: Regular Research Grants
• Field of knowledge: Health Sciences - Medicine - Psychiatry
• Principal Investigator: Helena Paula Brentani
• Grantee: Helena Paula Brentani
• Host Institution: Faculdade de Medicina (FM). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated researchers: Alexandra Valéria Maria Brentani ; Caroline Perez Camilo ; David Corrêa Martins Junior ; Gisele Rodrigues Gouveia ; Lea Campos de Oliveira ; MARINA CARVALHO DE MORAES BARROS ; Rossana Pulcineli Vieira Francisco ; Sérgio Nery Simões ; Silvia Maria Ibidi

Abstract

A growing body of literature focus on women that has COVID-19 during pregnancy. Many of these studies analyse the impact of the infection on the infant neurodevelopment, whilst other works study the transcriptome of this group with RNA sequencing (RNA-Seq). Up to now, no study performed both transcriptome analysis and neurodevelopmental tests. Furthermore, only one work on pregnant women with COVID-19 researched microRNA expression, but its methodology was not as broad as RNA-Seq. In this context, the objective of this project is to obtain the microRNA expression profiles in the cord blood plasma of infants whose mothers had COVID-19 during pregnancy, and to perform neurobehavioral tests in these children. The case group will consist of 35 pregnant women with positive COVID-19 serological test in the delivery, and the control group will have 65 pregnant women with negative COVID-19 serological test. In this sample, we have already measured the infant neurobehavioral patterns in the 1st month of life with NNNS tests, and we will apply the CREDI and Bayley-III tests in the 12nd month. Cord blood samples have been collected during delivery. We will assess the levels of the cytokines IL-6, C-reactive protein, and TNF, and we will perform microRNA RNA-Seq on the plasma from the cord blood. Through bioinformatic analyses, we will compare the expression profiles between the case and control groups, and we will evaluate the association between neurodevelopmental scores and the levels of the altered transcripts. By using machine learning, we will develop a prediction model of neurodevelopment scores from the expression levels of selected transcripts. This project will fill a knowledge gap on the molecular mechanisms that link maternal viral infection and infant neurodevelopment. (AU)