Neural tube defects: from a proteomic standpoint

Congenital malformations in the vertebral column: associations and possible embryologic origins

Cases of associations between random spinal congenital defects have previously been reported, yet several questions remain unanswered. Firstly, why are associations between what seems to be random combinations of vertebral malformations observed? Secondly, is there a common event or pattern that connects the associated defects? Therefore, this study aimed to identify congenital defects in the vertebral column and also to determine whether any associations, if present, between vertebral malformations exist. This article consequently discusses the possible embryological disruptions that may lead to the formation of various defects in the vertebral column. A random skeletal sample (n=187) was selected from the Pretoria Bone Collection housed in the Department of Anatomy, University of Pretoria (Ethics 678/2018). The sample was evaluated to determine the frequencies of spinal congenital defects in each set of remains. Identifiable congenital malformations were observed in 48.1% (n=90/187) of the sample. The results demonstrated a high probability of association between the different defects observed in the vertebral column. Findings are of value as they provide a reasonable explanation to why seemingly random cases of associations have been reported by several authors. This study is clinically relevant as severe spinal defects have been shown to have high morbidity in patients and mortality in infants.

Complement factors and alpha-fetoprotein as biomarkers for noninvasive prenatal diagnosis of neural tube defects

Neural tube defects (NTDs) are serious congenital malformations. In this study, we aimed to identify more specific and sensitive maternal serum biomarkers for noninvasive NTD screenings. We collected serum from 37 pregnant women carrying fetuses with NTDs and 38 pregnant women carrying normal fetuses. Isobaric tags for relative and absolute quantitation were conducted for differential proteomic analysis, and an enzyme-linked immunosorbent assay was used to validate the results. We then used a support vector machine (SVM) classifier to establish a disease prediction model for NTD diagnosis. We identified 113 differentially expressed proteins; of these, 23 were either up- or downregulated 1.5-fold or more, including five complement proteins (C1QA, C1S, C1R, C9, and C3); C3 and C9 were downregulated significantly in NTD groups. The accuracy rate of the SVM model of the complement factors (including C1QA, C1S, and C3) was 62.5%, with 60% sensitivity and 67% specificity, while the accuracy rate of the SVM model of alpha-fetoprotein (AFP, an established biomarker for NTDs) was 62.5%, with 75% sensitivity and 50% specificity. Combination of the complement factor and AFP data resulted in the SVM model accuracy of 75%, and receiver operating characteristic curve analysis showed 75% sensitivity and 75% specificity. These data suggest that a disease prediction model based on combined complement factor and AFP data could serve as a more accurate method of noninvasive prenatal NTD diagnosis.