Zika virus infection histories in brain development.

An outbreak of births of microcephalic patients in Brazil motivated multiple studies on this incident. The data left no doubt that infection by Zika virus (ZIKV) was the cause, and that this virus promotes reduction in neuron numbers and neuronal death. Analysis of patients' characteristics revealed additional aspects of the pathology alongside the decrease in neuronal number. Here, we review the data from human, molecular, cell and animal model studies attempting to build the natural history of ZIKV in the embryonic central nervous system (CNS). We discuss how identifying the timing of infection and the pathways through which ZIKV may infect and spread through the CNS can help explain the diversity of phenotypes found in congenital ZIKV syndrome (CZVS). We suggest that intraneuronal viral transport is the primary mechanism of ZIKV spread in the embryonic brain and is responsible for most cases of CZVS. According to this hypothesis, the viral transport through the blood-brain barrier and cerebrospinal fluid is responsible for more severe pathologies in which ZIKV-induced malformations occur along the entire anteroposterior CNS axis.

Zika Virus Changes Methylation of Genes Involved in Immune Response and Neural Development in Brazilian Babies Born With Congenital Microcephaly.

The recent increase in babies born with brain and eye malformations in Brazil is associated with Zika virus (ZIKV) infection in utero. ZIKV alters host DNA methylation in vitro. Using genome-wide DNA methylation profiling we compared 18 babies born with congenital ZIKV microcephaly with 20 controls. We found ZIKV-associated alteration of host methylation patterns, notably at RABGAP1L which is important in brain development, at viral host immunity genes MX1 and ISG15, and in an epigenetic module containing the causal microcephaly gene MCPH1. Our data support the hypothesis that clinical signs of congenital ZIKV are associated with changes in DNA methylation.

Association between brain morphology and electrophysiological features in Congenital Zika Virus Syndrome: A cross-sectional, observational study.

BACKGROUND: Intrauterine infection with the Zika virus (ZIKV) has been connected to severe brain malformations, microcephaly, and abnormal electrophysiological activity. METHODS: We describe the interictal electroencephalographic (EEG) recordings of 47 children born with ZIKV-derived microcephaly. EEGs were recorded in the first year of life and correlated with brain morphology. In 31 subjects, we tested the association between computed tomography (CT) findings and interictal epileptiform discharges (IED). In eighteen, CTs were used for correlating volumetric measurements of the brainstem, cerebellum, and prosencephalon with the rate of IED. FINDINGS: Twenty-nine out of 47 (62%) subjects were diagnosed as having epilepsy. Those subjects presented epileptiform discharges, including unilateral interictal spikes (26/29, 90%), bilateral synchronous and asynchronous interictal spikes (21/29, 72%), and hypsarrhythmia (12/29, 41%). Interestingly, 58% of subjects with clinical epilepsy were born with rhombencephalon malformations, while none of the subjects without epilepsy showed macroscopic abnormalities in this region. The presence of rhombencephalon malformation was associated with epilepsy (odds ratio of 34; 95% CI: 2 - 654). Also, the presence of IED was associated with smaller brain volumes. Age-corrected total brain volume was inversely correlated with the rate of IED during sleep. Finally, 11 of 44 (25%) subjects presented sleep spindles. We observed an odds ratio of 0·25 (95% CI: 0·06 - 1·04) for having sleep spindles given the IED presence. INTERPRETATION: The findings suggest that certain CT imaging features are associated with an increased likelihood of developing epilepsy, including higher rates of IED and impaired development of sleep spindles, in the first year of life of CZVS subjects. FUNDING: This work was supported by the Brazilian Federal Government through a postdoctoral fellowship for EBS (Talented Youth, Science without Borders), an undergraduate scholarship for AJR (Institutional Program of Science Initiation Scholarships, Federal University of Rio Grande do Norte, Brazil), by International Centre for Genetic Engineering and Biotechnology (CRP/BRA18-05_EC) and by CAPES (Grant number 440893/2016-0), and CNPq (Grant number 88881.130729/2016-01).

Chest circumference in full-term newborns: how can it be predicted?

BACKGROUND: Although over the years a number of studies have used chest circumference (CC) as a sensitive tool to identify the health status of infants, a particularly important aspect for this population is the lack of data on normal values and prediction equations. In order to facilitate and validate the interpretation of CC data in newborn (NB), the aim was to study the relation between CC and other anthropometric variables and develop a predictive equation for CC in a population of full-term newborns. METHODS: Cross-sectional study, carried out with full-term infants. The anthropometric (CC, head circumference - HC, length, age and weight) and hemodynamic variables were evaluated during the first 24 h of life. Bivariate analysis was performed between CC and HC, weight, length and type of delivery, followed by multiple linear regression analysis, including variables that were significant in the bivariate analysis. For data analysis, we used the SPSS program, considering p < 0.05 and 95% CI. RESULTS: The birth weight of the 120 NB varied between 2580 and 4225 g (mean 3360 g) and the gestational age between 37 and 42 weeks (mean 39 weeks). Approximately 61% of the sample were delivered vaginally and 67 (56%) were boys. The variables that remained statistically associated with CC after multivariate analysis were weight (ß 0.003, CI: 0.002: 0.003, p = 0.001) and HC (ß 0.287, CI: 0.156: 0.417, p = 0.001). For the linear regression model, the predictive equation of CC was 14.87+ (0.003 x weight) + (0.287 x HC), with a prediction of 76%. CONCLUSION: The results show a positive correlation between CC and weight, length and HC, and based on the linear regression model, the predictive equation for CC is based only on weight and HC.