Blood RNA Sequencing Indicates Upregulated BATF2 and LY6E and Downregulated ISG15 and MT2A Expression in Children with Autism Spectrum Disorder

Compensatory upregulation of MT2A alleviates neurogenic intermittent claudication through inhibiting activated p38 MAPK-mediated neuronal apoptosis

Neurogenic intermittent claudication (NIC), a classic symptom of lumbar spinal stenosis (LSS), is associated with neuronal apoptosis. To explore the novel therapeutic target of NIC treatment, we constructed the rat model of NIC by cauda equina compression (CEC) method and collected dorsal root ganglion (DRG) tissues, a region responsible for sensory and motor function, for mRNA sequencing. Bioinformatic analysis of mRNA sequencing indicated that upregulated metallothionein 2A (MT2A), an apoptosis-regulating gene belonging to the metallothionein family, might participate in NIC progression. Activated p38 MAPK mediated motor dysfunction following LSS and it was also found in DRG tissues of rats with NIC. Therefore, we supposed that MT2A might affect NIC progression by regulating p38 MAPK pathway. Then the rat model of NIC was used to explore the exact role of MT2A. Rats at day 7 post-CEC exhibited poorer motor function and had two-fold MT2A expression in DRG tissues compared with rats with sham operation. Co-localization analysis showed that MT2A was highly expressed in neurons, but not in microglia or astrocytes. Subsequently, neurons isolated from DRG tissues of rats were exposed to hypoxia condition (3% O2, 92% N2, 5% CO2) to induce cell damage. Gain of MT2A function in neurons was performed by lentivirus-mediated overexpression. MT2A overexpression inhibited apoptosis by inactivating p38 MAPK in hypoxia-exposed neurons. Our findings indicated that high MT2A expression was related to NIC progression, and MT2A overexpression protected against NIC through inhibiting activated p38 MAPK-mediated neuronal apoptosis in DRG tissues.

Nitrosative Stress in Autism: Supportive Evidence and Implications for Mitochondrial Dysfunction

A recent study by the Amal team published in this journal in May 2023 proved for the first time the link of nitric oxide (NO) with autism spectrum disorder (ASD), thereby opening new venues for the potential use of neuronal nitric oxide synthase (nNOS) inhibitors as therapeutics for improving the neurological and behavioral symptoms of ASD. The authors conclude that their findings demonstrate that NO plays a significant role in ASD. Indeed, earlier studies support elevated NO and its metabolites, nitrite, and peroxynitrite, in individuals diagnosed with ASD. Dysregulated NOS activity may underlie the well-documented mitochondrial dysfunction in a subset of individuals with ASD. Strategies for treating ASD shall also consider NO effects on mitochondrial respiration in modulating NOS activity. Further experimental evidence and controlled clinical trials with NOS modifiers are required for assessing their therapeutic potential for individuals with ASD.

High purity and integrity RNA from human cell lines stored in liquid nitrogen for over 20 years

Biobanks are a key resource for obtaining human cell lines for biomedical research, including for drug development projects. Such projects often include comparative RNA-sequencing of large panels of human cell lines from individuals affected by certain disorders and healthy controls, or from individuals with different drug response phenotypes. RNA extractions are typically done from growing cell cultures, a process that may take several weeks. However, maintaining large numbers of cell lines in parallel increases the project workload. Here, we show that extracting RNAs directly from frozen vials of human cell lines stored for over 20 years in a liquid nitrogen freezer yields RNAs with the high purity and integrity parameters that conform to those required for optimal RNA-sequencing and are closely similar to those obtained for RNAs extracted from growing human cell lines.

Machine Learning-Based Blood RNA Signature for Diagnosis of Autism Spectrum Disorder

Early diagnosis of autism spectrum disorder (ASD) is crucial for providing appropriate treatments and parental guidance from an early age. Yet, ASD diagnosis is a lengthy process, in part due to the lack of reliable biomarkers. We recently applied RNA-sequencing of peripheral blood samples from 73 American and Israeli children with ASD and 26 neurotypically developing (NT) children to identify 10 genes with dysregulated blood expression levels in children with ASD. Machine learning (ML) analyzes data by computerized analytical model building and may be applied to building diagnostic tools based on the optimization of large datasets. Here, we present several ML-generated models, based on RNA expression datasets collected during our recently published RNA-seq study, as tentative tools for ASD diagnosis. Using the random forest classifier, two of our proposed models yield an accuracy of 82% in distinguishing children with ASD and NT children. Our proof-of-concept study requires refinement and independent validation by studies with far larger cohorts of children with ASD and NT children and should thus be perceived as starting point for building more accurate ML-based tools. Eventually, such tools may potentially provide an unbiased means to support the early diagnosis of ASD.