International Journal of Molecular Sciences

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10 pages, 1768 KiB

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

by Irena Voinsky, Oleg Y. Fridland, Adi Aran, Richard E. Frye and David Gurwitz

Int. J. Mol. Sci. 2023, 24(3), 2082; https://doi.org/10.3390/ijms24032082 - 20 Jan 2023

Cited by 8 | Viewed by 2164

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue RNA Biomarkers in Neuropsychiatric Disorders)

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21 pages, 898 KiB

Open AccessReview

Can RNA Affect Memory Modulation? Implications for PTSD Understanding and Treatment

by Tehila Cohen and Noam Shomron

Int. J. Mol. Sci. 2023, 24(16), 12908; https://doi.org/10.3390/ijms241612908 - 17 Aug 2023

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Abstract

Memories are a central aspect of our lives, but the mechanisms underlying their formation, consolidation, retrieval, and extinction remain poorly understood. In this review, we explore the molecular mechanisms of memory modulation and investigate the effects of RNA on these processes. Specifically, we [...] Read more.

Memories are a central aspect of our lives, but the mechanisms underlying their formation, consolidation, retrieval, and extinction remain poorly understood. In this review, we explore the molecular mechanisms of memory modulation and investigate the effects of RNA on these processes. Specifically, we examine the effects of time and location on gene expression alterations. We then discuss the potential for harnessing these alterations to modulate memories, particularly fear memories, to alleviate post-traumatic stress disorder (PTSD) symptoms. The current state of research suggests that transcriptional changes play a major role in memory modulation and targeting them through microRNAs may hold promise as a novel approach for treating memory-related disorders such as PTSD. Full article

(This article belongs to the Special Issue RNA Biomarkers in Neuropsychiatric Disorders)

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23 pages, 332 KiB

Open AccessReview

RNA Biomarkers in Bipolar Disorder and Response to Mood Stabilizers

by Claudia Pisanu and Alessio Squassina

Int. J. Mol. Sci. 2023, 24(12), 10067; https://doi.org/10.3390/ijms241210067 - 13 Jun 2023

Cited by 1 | Viewed by 1552

Abstract

Bipolar disorder (BD) is a severe chronic disorder that represents one of the main causes of disability among young people. To date, no reliable biomarkers are available to inform the diagnosis of BD or clinical response to pharmacological treatment. Studies focused on coding [...] Read more.

Bipolar disorder (BD) is a severe chronic disorder that represents one of the main causes of disability among young people. To date, no reliable biomarkers are available to inform the diagnosis of BD or clinical response to pharmacological treatment. Studies focused on coding and noncoding transcripts may provide information complementary to genome-wide association studies, allowing to correlate the dynamic evolution of different types of RNAs based on specific cell types and developmental stage with disease development or clinical course. In this narrative review, we summarize findings from human studies that evaluated the potential utility of messenger RNAs and noncoding transcripts, such as microRNAs, circular RNAs and long noncoding RNAs, as peripheral markers of BD and/or response to lithium and other mood stabilizers. The majority of available studies investigated specific targets or pathways, with large heterogeneity in the included type of cells or biofluids. However, a growing number of studies are using hypothesis-free designs, with some studies also integrating data on coding and noncoding RNAs measured in the same participants. Finally, studies conducted in neurons derived from induced-pluripotent stem cells or in brain organoids provide promising preliminary findings supporting the power and utility of these cellular models to investigate the molecular determinants of BD and clinical response. Full article

(This article belongs to the Special Issue RNA Biomarkers in Neuropsychiatric Disorders)

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