No abstract

Uncertainties remain concerning the pathophysiology, epidemiology, and potential therapeutics for COVID‐19. Among unsettled controversies is whether tobacco smoking increases or protects from severe COVID‐19. Several epidemiological studies reported reduced COVID‐19 hospitalizations among smokers, while other studies reported the opposite trend. Some authors assumed that smokers have elevated airway expression of ACE2, the cell recognition site of the SARS‐Cov‐2 spike protein, but this suggestion remains unverified. We therefore performed data mining of two independent NCBI GEO genome‐wide RNA expression files (GSE7894 and GSE994) and report that in both data sets, current smokers and never smokers have, on average, closely similar bronchial epithelial cell mRNA levels of ACE2 , as well as TMPRSS2 , coding for a serine protease priming SARS‐Cov‐2 for cell entry, and ADAM17 , coding for a protease implicated in ACE2 membrane shedding. In contrast, the expression levels of TMPRSS4 , coding for a protease that primes SARS‐CoV‐2 for cell entry similarly to TMPRSS2 , were elevated in bronchial epithelial cells from current smokers compared with never smokers, suggesting that higher bronchial TMPRSS4 levels in smokers might put them at higher SARS‐Cov‐2 infection risk. The effects of smoking on COVID‐19 severity need clarification with larger studies. Additionally, the postulated protective effects of nicotine and nitric oxide, which may presumably reduce the risk of a “cytokine storm” in infected individuals, deserve assessment by controlled clinical trials.

We observed higher expression levels of HDGFRP3 and ID2 in BD patients who favourably respond to lithium. Both of these genes are involved in neurogenesis, and HDGFRP3 has been suggested to be a neurotrophic factor. Additional studies in larger BD cohorts are needed to confirm the potential of HDGFRP3 and ID2 expression levels in blood cells as tentative favourable lithium response biomarkers.

The peptide hormone oxytocin is an established regulator of social function in mammals, and dysregulated oxytocin signaling is implicated in autism spectrum disorder (ASD). Several clinical trials examining the effects of intranasal oxytocin for improving social and behavioral function in ASD have had mixed or inclusive outcomes. The heterogeneity in clinical trials outcomes may reflect large inter-individual expression variations of the oxytocin and/or vasopressin receptor genes OXTR and AVPR1A, respectively. To explore this hypothesis we examined the expression of both genes in peripheral blood mononuclear cells (PBMC) from ASD children, their non-ASD siblings, and age-matched neurotypical children aged 3 to 16 years of age as well as datamined published ASD datasets. Both genes were found to have large inter-individual variations. Higher OXTR and AVPR1A expression was associated with lower Aberrant Behavior Checklist (ABC) scores. OXTR expression was associated with less severe behavior and higher adaptive behavior on additional standardized measures. Combining the sum expression levels OXTR, AVPR1A, and IGF1 yielded the strongest correlation with ABC scores. We propose that future clinical trials in ASD children with oxytocin, oxytocin mimetics and additional tentative therapeutics should assess the prognostic value of their PBMC mRNA expression of OXTR, AVPR1A, and IGF1.

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.