Background. Currently, the data available on the utility of miRNAs in noninvasive prenatal testing is insufficient in the literature. We evaluated the expression levels of 14 miRNAs located on chromosome 21 in maternal plasma and their utility in noninvasive prenatal testing of Down Syndrome. Method. A total of 56 patients underwent invasive prenatal testing; 23 cases were carrying Down Syndrome affected fetuses, and 33 control cases carrying unaffected, normal karyotype fetuses were included for comparison. Indications for invasive prenatal testing were advanced maternal age, increased risk of Down Syndrome in screening tests, and abnormal finding in the sonographic examination. In both the study and control groups, all the pregnant women were at 17th and 18th week of gestation. miRNA expression levels were measured using real-time RT-PCR. Results. Significantly increased maternal plasma levels of miR-3156 and miR-99a were found in the women carrying a fetus with Down Syndrome. Conclusion. Our results provide a basis for multicenter studies with larger sample groups and microRNA profiles, particularly with the microRNAs which were found to be variably expressed in our study. Through this clinical research, the utility of microRNAs in noninvasive prenatal testing can be better explored in future studies.

Background:Down syndrome, which is the most common human chromosomal anomaly that can affect people of any race and age, can be diagnosed prenatally in most cases. Prenatal diagnosis via culture method is time-consuming; thus, genetic analysis has thus been introduced and is continually being developed for rapid prenatal diagnosis. For this reason, the effective use of microRNA profiling for the rapid analysis of prenatal amniotic fluid samples for the diagnosis of Down syndrome was investigated.Aims:To evaluate the expression levels of 14 microRNAs encoded by chromosome 21 in amniotic fluid samples and their utility for prenatal diagnosis of Down syndrome.Study Design:Case-control study.Methods:We performed invasive prenatal testing for 56 pregnant women; 23 carried fetuses with Down syndrome, and 33 carried fetuses with a normal karyotype. Advanced maternal age and increased risk for Down syndrome in the screening tests were indications for invasive prenatal testing. The age of gestation in the study and control groups ranged between 17 and 18 weeks. The expression levels of microRNA were measured by real-time polymerase chain reaction.Results:The expression levels of microRNA-125b-2, microRNA-155, and microRNA-3156 were significantly higher in the study group than in the control group.Conclusion:The presence of significantly dysregulated microRNAs may be associated with either the phenotype or the result of abnormal development. Further large-scale comparative studies conducted in a variety of conditions may bring novel insights in the field of abnormal prenatal conditions.

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Background Previous large-scale studies of de novo variants identified a number of genes associated with neurodevelopmental disorders (NDDs); however, it was also predicted that many NDD-associated genes await discovery. Such genes can be discovered by integrating copy number variants (CNVs), which have not been fully considered in previous studies, and increasing the sample size. Methods We first constructed a model estimating the rates of de novo CNVs per gene from several factors such as gene length and number of exons. Second, we compiled a comprehensive list of de novo single-nucleotide variants (SNVs) in 41,165 individuals and de novo CNVs in 3675 individuals with NDDs by aggregating our own and publicly available datasets, including denovo-db and the Deciphering Developmental Disorders study data. Third, summing up the de novo CNV rates that we estimated and SNV rates previously established, gene-based enrichment of de novo deleterious SNVs and CNVs were assessed in the 41,165 cases. Significantly enriched genes were further prioritized according to their similarity to known NDD genes using a deep learning model that considers functional characteristics (e.g., gene ontology and expression patterns). Results We identified a total of 380 genes achieving statistical significance (5% false discovery rate), including 31 genes affected by de novo CNVs. Of the 380 genes, 52 have not previously been reported as NDD genes, and the data of de novo CNVs contributed to the significance of three genes (GLTSCR1, MARK2, and UBR3). Among the 52 genes, we reasonably excluded 18 genes [a number almost identical to the theoretically expected false positives (i.e., 380 × 0.05 = 19)] given their constraints against deleterious variants and extracted 34 “plausible” candidate genes. Their validity as NDD genes was consistently supported by their similarity in function and gene expression patterns to known NDD genes. Quantifying the overall similarity using deep learning, we identified 11 high-confidence (> 90% true-positive probabilities) candidate genes: HDAC2, SUPT16H, HECTD4, CHD5, XPO1, GSK3B, NLGN2, ADGRB1, CTR9, BRD3, and MARK2. Conclusions We identified dozens of new candidates for NDD genes. Both the methods and the resources developed here will contribute to the further identification of novel NDD-associated genes.