DNA Methylation Level of Transcription Factor Binding Site in the Promoter Region of Acyl-CoA Synthetase Family Member 3 (ACSF3) in Saudi Autistic Children

Algothmi, Khloud; Alqurashi, Amal; Alrofaidi, Aisha; Alharbi, Mona G.; Farsi, Reem M.; Alburae, Najla Ali; Ganash, Magdah; Azhari, Sheren; Basingab, Fatemah; Almuhammadi, Asma; Alqosaibi, Amany I.; Alkhatabi, Heba; Elaimi, Aisha; Jan, Mohammed M.; Aldhalaan, Hesham; Alrafiah, Aziza; Alhazmi, Safiah

doi:10.2147/pgpm.s346187

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…Additionally, the involvement of synaptic components and to a lesser degree the immune function was also highlighted by the largest depression GWAS today [ 4 ]. The eQTM data indicates that methylation at the identified CpGs is in correlation with expression of the genes that have been already linked to neurological function and disorders, such as HOTAIRM1 - dopamine neuron differentiation [ 78 ], NLGN2 - depression [ 79 ], ACSF3 [ 80 ] and HOXA1 [ 81 ] - autism spectrum disorders, KLHDC7B - hearing loss [ 82 ] and depression [ 22 ]. We emphasize that subsequent analyses on the larger data corpus with more cohorts, when available, could lead to the identification of even more promising CpGs and proteins that could be used as biomarkers for depression as well as additional features for model construction.…”

Section: Discussionmentioning

confidence: 99%

Decoding depression: a comprehensive multi-cohort exploration of blood DNA methylation using machine learning and deep learning approaches

Sokolov,

Schiöth

2024

Transl Psychiatry

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The causes of depression are complex, and the current diagnosis methods rely solely on psychiatric evaluations with no incorporation of laboratory biomarkers in clinical practices. We investigated the stability of blood DNA methylation depression signatures in six different populations using six public and two domestic cohorts (n = 1942) conducting mega-analysis and meta-analysis of the individual studies. We evaluated 12 machine learning and deep learning strategies for depression classification both in cross-validation (CV) and in hold-out tests using merged data from 8 separate batches, constructing models with both biased and unbiased feature selection. We found 1987 CpG sites related to depression in both mega- and meta-analysis at the nominal level, and the associated genes were nominally related to axon guidance and immune pathways based on enrichment analysis and eQTM data. Random forest classifiers achieved the highest performance (AUC 0.73 and 0.76) in CV and hold-out tests respectively on the batch-level processed data. In contrast, the methylation showed low predictive power (all AUCs < 0.57) for all classifiers in CV and no predictive power in hold-out tests when used with harmonized data. All models achieved significantly better performance (>14% gain in AUCs) with pre-selected features (selection bias), with some of the models (joint autoencoder-classifier) reaching AUCs of up to 0.91 in the final testing regardless of data preparation. Different algorithmic feature selection approaches may outperform limma, however, random forest models perform well regardless of the strategy. The results provide an overview over potential future biomarkers for depression and highlight many important methodological aspects for DNA methylation-based depression profiling including the use of machine learning strategies.

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Section: Discussionmentioning

confidence: 99%

Decoding depression: a comprehensive multi-cohort exploration of blood DNA methylation using machine learning and deep learning approaches

Sokolov,

Schiöth

2024

Transl Psychiatry

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“…We report the downregulation of SP1 regulated genes in Shank3∆11(−/−) cells, which functionally annotated to ECM and cell cycle processes. SP1 signaling has been described as dysfunctional in post-mortem brains from ASD [ 124 , 125 ] individuals and its transcription factor activity is essential to prevent degeneration of neurons by regulating microtubule related gene expression [ 77 ].…”

Section: Discussionmentioning

confidence: 99%

Disrupted extracellular matrix and cell cycle genes in autism-associated Shank3 deficiency are targeted by lithium

Ioannidis,

Pandey,

Bauer

et al. 2023

Mol Psychiatry

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The Shank3 gene encodes the major postsynaptic scaffolding protein SHANK3. Its mutation causes a syndromic form of autism spectrum disorder (ASD): Phelan-McDermid Syndrome (PMDS). It is characterized by global developmental delay, intellectual disorders (ID), ASD behavior, affective symptoms, as well as extra-cerebral symptoms. Although Shank3 deficiency causes a variety of molecular alterations, they do not suffice to explain all clinical aspects of this heterogenic syndrome. Since global gene expression alterations in Shank3 deficiency remain inadequately studied, we explored the transcriptome in vitro in primary hippocampal cells from Shank3∆11(−/−) mice, under control and lithium (Li) treatment conditions, and confirmed the findings in vivo. The Shank3∆11(−/−) genotype affected the overall transcriptome. Remarkably, extracellular matrix (ECM) and cell cycle transcriptional programs were disrupted. Accordingly, in the hippocampi of adolescent Shank3∆11(−/−) mice we found proteins of the collagen family and core cell cycle proteins downregulated. In vitro Li treatment of Shank3∆11(−/−) cells had a rescue-like effect on the ECM and cell cycle gene sets. Reversed ECM gene sets were part of a network, regulated by common transcription factors (TF) such as cAMP responsive element binding protein 1 (CREB1) and β-Catenin (CTNNB1), which are known downstream effectors of synaptic activity and targets of Li. These TFs were less abundant and/or hypo-phosphorylated in hippocampi of Shank3∆11(−/−) mice and could be rescued with Li in vitro and in vivo. Our investigations suggest the ECM compartment and cell cycle genes as new players in the pathophysiology of Shank3 deficiency, and imply involvement of transcriptional regulators, which can be modulated by Li. This work supports Li as potential drug in the management of PMDS symptoms, where a Phase III study is ongoing.

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Intercontinental insights into autism spectrum disorder: a synthesis of environmental influences and DNA methylation

Kuodza,

Kawai,

LaSalle

2024

Environmental Epigenetics

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Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by a broad range of symptoms. The etiology of ASD is thought to involve complex gene-environment interactions, which are crucial to understanding its various causes and symptoms. DNA methylation is an epigenetic mechanism that potentially links genetic predispositions to environmental factors in the development of ASD. This review provides a global perspective on ASD, focusing on how DNA methylation studies may reveal gene-environment interactions characteristic of specific geographical regions. It delves into the role of DNA methylation in influencing the causes and prevalence of ASD in regions where environmental influences vary significantly. We also address potential explanations for the high ASD prevalence in North America, considering lifestyle factors, environmental toxins, and diagnostic considerations. Asian and European studies offer insights into endocrine-disrupting compounds (EDC), persistent organic pollutants (POP), maternal smoking, and their associations with DNA methylation alterations in ASD. In areas with limited data on DNA methylation and ASD, such as Africa, Oceania, and South America, we discuss prevalent environmental factors based on epidemiological studies. Additionally, the review integrates global and country-specific prevalence data from various studies, providing a comprehensive picture of the variables influencing ASD diagnoses over region and year of assessment. This prevalence data, coupled with regional environmental variables and DNA methylation studies, provides a perspective on the complexities of ASD research. Integrating global prevalence data, we underscore the need for a comprehensive global understanding of ASD’s complex etiology. Expanded research into epigenetic mechanisms of ASD is needed, particularly in underrepresented populations and locations, to enhance biomarker development for diagnosis and intervention strategies for ASD that reflect the varied environmental and genetic landscapes worldwide.

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DNA Methylation Level of Transcription Factor Binding Site in the Promoter Region of Acyl-CoA Synthetase Family Member 3 (ACSF3) in Saudi Autistic Children

Cited by 3 publications

References 27 publications

Decoding depression: a comprehensive multi-cohort exploration of blood DNA methylation using machine learning and deep learning approaches

Decoding depression: a comprehensive multi-cohort exploration of blood DNA methylation using machine learning and deep learning approaches

Disrupted extracellular matrix and cell cycle genes in autism-associated Shank3 deficiency are targeted by lithium

Intercontinental insights into autism spectrum disorder: a synthesis of environmental influences and DNA methylation

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