Alterations in Serum miR-126-3p Levels over Time: A Marker of Pituitary Insufficiency following Head Trauma

Trans Species RNA Activity: Sperm RNA of the Father of an Autistic Child Programs Glial Cells and Behavioral Disorders in Mice

Recently, we described the alteration of six miRNAs in the serum of autistic children, their fathers, mothers, siblings, and in the sperm of autistic mouse models. Studies in model organisms suggest that noncoding RNAs participate in transcriptional modulation pathways. Using mice, approaches to alter the amount of RNA in fertilized eggs enable in vivo intervention at an early stage of development. Noncoding RNAs are very numerous in spermatozoa. Our study addresses a fundamental question: can the transfer of RNA content from sperm to eggs result in changes in phenotypic traits, such as autism? To explore this, we used sperm RNA from a normal father but with autistic children to create mouse models for autism. Here, we induced, in a single step by microinjecting sperm RNA into fertilized mouse eggs, a transcriptional alteration with the transformation in adults of glial cells into cells affected by astrogliosis and microgliosis developing deficiency disorders of the 'autism-like' type in mice born following these manipulations. Human sperm RNA alters gene expression in mice, and validates the possibility of non-Mendelian inheritance in autism.

From Data to Insights: Machine Learning Empowers Prognostic Biomarker Prediction in Autism

Autism Spectrum Disorder (ASD) poses significant challenges to society and science due to its impact on communication, social interaction, and repetitive behavior patterns in affected children. The Autism and Developmental Disabilities Monitoring (ADDM) Network continuously monitors ASD prevalence and characteristics. In 2020, ASD prevalence was estimated at 1 in 36 children, with higher rates than previous estimates. This study focuses on ongoing ASD research conducted by Erciyes University. Serum samples from 45 ASD patients and 21 unrelated control participants were analyzed to assess the expression of 372 microRNAs (miRNAs). Six miRNAs (miR-19a-3p, miR-361-5p, miR-3613-3p, miR-150-5p, miR-126-3p, and miR-499a-5p) exhibited significant downregulation in all ASD patients compared to healthy controls. The current study endeavors to identify dependable diagnostic biomarkers for ASD, addressing the pressing need for non-invasive, accurate, and cost-effective diagnostic tools, as current methods are subjective and time-intensive. A pivotal discovery in this study is the potential diagnostic value of miR-126-3p, offering the promise of earlier and more accurate ASD diagnoses, potentially leading to improved intervention outcomes. Leveraging machine learning, such as the K-nearest neighbors (KNN) model, presents a promising avenue for precise ASD diagnosis using miRNA biomarkers.