Artificial intelligence analysis of newborn leucocyte epigenomic markers for the prediction of autism

Sex-specific DNA methylation signatures of autism spectrum disorder from whole genome bisulfite sequencing of newborn blood

BACKGROUND

Autism spectrum disorder (ASD) is a group of neurodevelopmental conditions currently diagnosed through behavioral assessments in childhood, though neuropathological changes begin in utero. ASD is more commonly diagnosed in males, a disparity attributed to both biological sex differences and diagnostic biases. Identifying molecular biomarkers, such as DNA methylation signatures, could provide more objective screening for ASD-risk in newborns, allowing for early intervention. Epigenetic dysregulation has been reported in multiple tissues from newborns who are later diagnosed with ASD, but this is the first study to investigate sex-specific DNA methylation signatures for ASD in newborn blood, an accessible and widely banked tissue.

METHODS

We assayed DNA methylation from newborn blood of ASD and typically developing (TD) individuals (discovery set n = 196, replication set n = 90) using whole genome bisulfite sequencing (WGBS). Sex-stratified differentially methylated regions (DMRs) were assessed for replication, comparisons by sex, overlaps with DMRs from other tissues, and enrichment for biological processes and SFARI ASD-risk genes.

RESULTS

We found that newborn blood ASD DMRs from both sexes significantly replicated in an independent cohort and were enriched for hypomethylation in ASD compared to TD samples, as well as location in promoters, CpG islands, and CpG island shores. By comparing female to male samples, we found that most sex-associated DMRs in TD individuals were also found in ASD individuals, alongside additional ASD-specific sex differences. Female-specific DMRs were enriched for X chromosomal location. Across both sexes, newborn blood DMRs overlapped significantly with DMRs from umbilical cord blood and placenta but not post-mortem cerebral cortex. DMRs from all tissues were enriched for neurodevelopmental processes (females) and known ASD genes (both sexes).

CONCLUSIONS

Overall, we identified and replicated a sex-specific DNA methylation signature of ASD in newborn blood that supported the female protective effect and highlighted convergence of epigenetic and genetic signatures of ASD in newborns. Despite the study's limitations, particularly in female sample sizes, our results demonstrate the potential of newborn blood in ASD screening and emphasize the importance of sex-stratification in future studies.

AI-assisted early screening, diagnosis, and intervention for autism in young children

Autism is a serious threat to an individual's physical and mental health. Early screening, diagnosis, and intervention can effectively reduce the level of deficits in individuals with autism. However, traditional methods of screening, diagnosis, and intervention rely on the professionalism of psychiatrists and require a great deal of time and effort, resulting in a large proportion of individuals with autism being diagnosed after the age of 6. Artificial intelligence (AI) combined with machine learning is being used to improve the efficiency of early screening, diagnosis, and intervention of autism in young children. This review aims to summarize AI-assisted methods for early screening, diagnosis, and intervention of autism in young children (infants, toddlers, and preschoolers). To achieve early screening and diagnosis of autism in young children, AI methods have built predictive models to improve the automation of early behavioral diagnosis, analyzed brain imaging and genetic data to break the age barrier for diagnosis, and established intelligent screening systems for early mass screening. For early intervention of autism in young children, AI methods built intelligent education systems to optimize the teaching and learning environment and provide individualized interventions, constructed intelligent monitoring systems for dynamic tracking, and created intelligent support systems to provide continuous support and meet the diverse needs of young children with autism. As AI continues to develop, further research is needed to build a large and shared database on autism, to generalize and migrate the effects of AI interventions, and to improve the appearance and performance of AI-powered robots, to reduce failure rates and costs of AI technologies.

Latest clinical frontiers related to autism diagnostic strategies

The diagnosis of autism is currently based on the developmental history, direct observation of behavior, and reported symptoms, supplemented by rating scales/interviews/structured observational evaluations-which is influenced by the clinician's knowledge and experience-with no established diagnostic biomarkers. A growing body of research has been conducted over the past decades to improve diagnostic accuracy. Here, we provide an overview of the current diagnostic assessment process as well as of recent and ongoing developments to support diagnosis in terms of genetic evaluation, telemedicine, digital technologies, use of machine learning/artificial intelligence, and research on candidate diagnostic biomarkers. Genetic testing can meaningfully contribute to the assessment process, but caution is required when interpreting negative results, and more work is needed to strengthen the transferability of genetic information into clinical practice. Digital diagnostic and machine-learning-based analyses are emerging as promising approaches, but larger and more robust studies are needed. To date, there are no available diagnostic biomarkers. Moving forward, international collaborations may help develop multimodal datasets to identify biomarkers, ensure reproducibility, and support clinical translation.

mTOR signaling pathway as a pathophysiologic mechanism in preclinical models of autism spectrum disorder

Autism spectrum disorder (ASD) is a highly prevalent multifactorial disorder characterized by social deficits and stereotypies. Despite extensive research efforts, the etiology of ASD remains poorly understood. However, studies using preclinical models have identified the mechanistic target of rapamycin kinase (mTOR) signaling pathway as a key player in ASD-related features. This review examines genetic and environmental models of ASD, focusing on their association with the mTOR pathway. We organize findings on alterations within this pathway, providing insights about the potential mechanisms involved in the onset and maintenance of ASD symptoms. Our analysis highlights the central role of mTOR hyperactivation in disrupting autophagic processes, neural organization, and neurotransmitter pathways, which collectively contribute to ASD phenotypes. The review also discusses the therapeutic potential of mTOR pathway inhibitors, such as rapamycin, in mitigating ASD characteristics. These insights underscore the importance of the mTOR pathway as a target for future research and therapeutic intervention in ASD. This review innovates by bringing the convergence of disrupted mTOR signaling in preclinical models and clinical data associated with ASD.

The Overexpression of eIF4E Decreases Oxytocin Levels and Induces Social Cognitive Behavioral Disorders in Mice

Overexpression of the eukaryotic initiation factor 4E (eIF4E) gene has been associated with excessive stereotypic behaviors and reduced sociability, which manifest as autism-like social cognitive deficits. However, the precise mechanisms by which eIF4E overexpression insufficiently induces these autism-like behaviors and the specific brain regions implicated remain insufficiently understood. Oxytocin (OXT), a neurotransmitter known for its role in social behavior, has been proposed to modulate certain autism-related symptoms by influencing microglial function and attenuating neuroinflammation. Nonetheless, the contributions of the hippocampus and oxytocin in the content of eIF4E overexpression-induced autistic behaviors remain elucidated. To investigate this issue, researchers utilized the three-chamber social interaction test, the open-field test, and the Morris water maze to evaluate the social cognitive behaviors of the two groups of mice. Additionally, ELISA, immunofluorescence, Western blotting, and qRT-PCR were employed to quantify oxytocin levels and assess hippocampal microglial activation. The results indicate that overexpression of eIF4E in mice is associated with significant impairments in social cognition, alongside pronounced marked hyperactivation of hippocampal microglia.

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

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, persistent organic pollutants, 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.

Early life epigenetics and childhood outcomes: a scoping review

Epigenetics is the study of changes in gene expression, without a change in the DNA sequence that are potentially heritable. Epigenetic mechanisms such as DNA methylation, histone modifications, and small non-coding RNA (sncRNA) changes have been studied in various childhood disorders. Causal links to maternal health and toxin exposures can introduce epigenetic modifications to the fetal DNA, which can be detected in the cord blood. Cord blood epigenetic modifications provide evidence of in-utero stressors and immediate postnatal changes, which can impact both short and long-term outcomes in children. The mechanisms of these epigenetic changes can be leveraged for prevention, early detection, and intervention, and to discover novel therapeutic modalities in childhood diseases. We report a scoping review of early life epigenetics, the influence of maternal health, maternal toxin, and drug exposures on the fetus, and its impact on perinatal, neonatal, and childhood outcomes. IMPACT STATEMENT: Epigenetic changes such as DNA methylation, histone modification, and non-coding RNA have been implicated in the pathophysiology of various disease processes. The fundamental changes to an offspring's epigenome can begin in utero, impacting the immediate postnatal period, childhood, adolescence, and adulthood. This scoping review summarizes current literature on the impact of early life epigenetics, especially DNA methylation on childhood health outcomes.

Transcriptome Study in Sicilian Patients with Autism Spectrum Disorder

ASD is a complex condition primarily rooted in genetics, although influenced by environmental, prenatal, and perinatal risk factors, ultimately leading to genetic and epigenetic alterations. These mechanisms may manifest as inflammatory, oxidative stress, hypoxic, or ischemic damage. To elucidate potential variances in gene expression in ASD, a transcriptome analysis of peripheral blood mononuclear cells was conducted via RNA-seq on 12 ASD patients and 13 healthy controls, all of Sicilian ancestry to minimize environmental confounds. A total of 733 different statistically significant genes were identified between the two cohorts. Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) terms were employed to explore the pathways influenced by differentially expressed mRNAs. GSEA revealed GO pathways strongly associated with ASD, namely the GO Biological Process term "Response to Oxygen-Containing Compound". Additionally, the GO Cellular Component pathway "Mitochondrion" stood out among other pathways, with differentially expressed genes predominantly affiliated with this specific pathway, implicating the involvement of different mitochondrial functions in ASD. Among the differentially expressed genes, FPR2 was particularly highlighted, belonging to three GO pathways. FPR2 can modulate pro-inflammatory responses, with its intracellular cascades triggering the activation of several kinases, thus suggesting its potential utility as a biomarker of pro-inflammatory processes in ASD.

Altered Gut Microbiota and Short-chain Fatty Acids in Chinese Children with Constipated Autism Spectrum Disorder

Gastrointestinal symptoms are more prevalent in children with autism spectrum disorder (ASD) than in typically developing (TD) children. Constipation is a significant gastrointestinal comorbidity of ASD, but the associations among constipated autism spectrum disorder (C-ASD), microbiota and short-chain fatty acids (SCFAs) are still debated. We enrolled 80 children, divided into the C-ASD group (n = 40) and the TD group (n = 40). In this study, an integrated 16S rRNA gene sequencing and gas chromatography-mass spectrometry-based metabolomics approach was applied to explore the association of the gut microbiota and SCFAs in C-ASD children in China. The community diversity estimated by the Observe, Chao1, and ACE indices was significantly lower in the C-ASD group than in the TD group. We observed that Ruminococcaceae_UCG_002, Erysipelotrichaceae_UCG_003, Phascolarctobacterium, Megamonas, Ruminiclostridium_5, Parabacteroides, Prevotella_2, Fusobacterium, and Prevotella_9 were enriched in the C-ASD group, and Anaerostipes, Lactobacillus, Ruminococcus_gnavus_group, Lachnospiraceae_NK4A136_group, Ralstonia, Eubacterium_eligens_group, and Ruminococcus_1 were enriched in the TD group. The propionate levels, which were higher in the C-ASD group, were negatively correlated with the abundance of Lactobacillus taxa, but were positively correlated with the severity of ASD symptoms. The random forest model, based on the 16 representative discriminant genera, achieved a high accuracy (AUC = 0.924). In conclusion, we found that C-ASD is related to altered gut microbiota and SCFAs, especially decreased abundance of Lactobacillus and excessive propionate in faeces, which provide new clues to understand C-ASD and biomarkers for the diagnosis and potential strategies for treatment of the disorder. This study was registered in the Chinese Clinical Trial Registry ( www.chictr.org.cn ; trial registration number ChiCTR2100052106; date of registration: October 17, 2021).

Genome-wide DNA methylation analysis in families with multiple individuals diagnosed with schizophrenia and intellectual disability

OBJECTIVES

Schizophrenia (SZ) and intellectual disability (ID) are both included in the continuum of neurodevelopmental disorders (NDDs). DNA methylation is known to be important in the occurrence of NDDs. The family study is conducive to eliminate the effects of relative epigenetic backgrounds, and to screen for differentially methylated positions (DMPs) and regions (DMRs) that are truly associated with NDDs.

METHODS

Four monozygotic twin families were recruited, and both twin individuals suffered from NDDs (either SZ, ID, or SZ plus ID). Genome-wide methylation analysis was performed in all samples and each family. DMPs and DMRs between NDD patients and unaffected individuals were identified. Functional and pathway enrichment analyses were performed on the annotated genes.

RESULTS

Two significant DMPs annotated to CYP2E1 were found in all samples. In Family One, 1476 DMPs mapped to 880 genes, and 162 DMRs overlapping with 153 unique genes were recognised. Our results suggested that the altered methylation levels of FYN, STAT3, RAC1, and NR4A2 were associated with the development of SZ and ID. Neurodevelopment and the immune system may participate in the occurrence of SZ and ID.

CONCLUSIONS

Our findings suggested that DNA methylation participated in the development of NDDs by affecting neurodevelopment and the immune system.

Artificial intelligence in psychiatry research, diagnosis, and therapy

Psychiatric disorders are now responsible for the largest proportion of the global burden of disease, and even more challenges have been seen during the COVID-19 pandemic. Artificial intelligence (AI) is commonly used to facilitate the early detection of disease, understand disease progression, and discover new treatments in the fields of both physical and mental health. The present review provides a broad overview of AI methodology and its applications in data acquisition and processing, feature extraction and characterization, psychiatric disorder classification, potential biomarker detection, real-time monitoring, and interventions in psychiatric disorders. We also comprehensively summarize AI applications with regard to the early warning, diagnosis, prognosis, and treatment of specific psychiatric disorders, including depression, schizophrenia, autism spectrum disorder, attention-deficit/hyperactivity disorder, addiction, sleep disorders, and Alzheimer's disease. The advantages and disadvantages of AI in psychiatry are clarified. We foresee a new wave of research opportunities to facilitate and improve AI technology and its long-term implications in psychiatry during and after the COVID-19 era.

DNA Methylation Biomarkers for Young Children with Idiopathic Autism Spectrum Disorder: A Systematic Review

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition, the underlying pathological mechanisms of which are not yet completely understood. Although several genetic and genomic alterations have been linked to ASD, for the majority of ASD patients, the cause remains unknown, and the condition likely arises due to complex interactions between low-risk genes and environmental factors. There is increasing evidence that epigenetic mechanisms that are highly sensitive to environmental factors and influence gene function without altering the DNA sequence, particularly aberrant DNA methylation, are involved in ASD pathogenesis. This systematic review aimed to update the clinical application of DNA methylation investigations in children with idiopathic ASD, investigating its potential application in clinical settings. To this end, a literature search was performed on different scientific databases using a combination of terms related to the association between peripheral DNA methylation and young children with idiopathic ASD; this search led to the identification of 18 articles. In the selected studies, DNA methylation is investigated in peripheral blood or saliva samples, at both gene-specific and genome-wide levels. The results obtained suggest that peripheral DNA methylation could represent a promising methodology in ASD biomarker research, although further studies are needed to develop DNA-methylation-based clinical applications.

Integrative Co-methylation Network Analysis Identifies Novel DNA Methylation Signatures and Their Target Genes in Alzheimer's Disease

BACKGROUND

DNA methylation is a key epigenetic marker, and its alternations may be involved in Alzheimer's disease (AD). CpGs sharing similar biological functions or pathways tend to be co-methylated.

METHODS

We performed an integrative network-based DNA methylation analysis on 2 independent cohorts (N = 941) using brain DNA methylation profiles and RNA-sequencing as well as AD pathology data.

RESULTS

Weighted co-methylation network analysis identified 6 modules as significantly associated with neuritic plaque burden. In total, 15 hub CpGs including 3 novel CpGs were identified and replicated as being significantly associated with AD pathology. Furthermore, we identified and replicated 4 target genes (ATP6V1G2, VCP, RAD52, and LST1) as significantly regulated by DNA methylation at hub CpGs. In particular, VCP gene expression was also associated with AD pathology in both cohorts.

CONCLUSIONS

This integrative network-based multiomics study provides compelling evidence for a potential role of DNA methylation alternations and their target genes in AD.

Candidate biomarkers from the integration of methylation and gene expression in discordant autistic sibling pairs

While the genetics of autism spectrum disorders (ASD) has been intensively studied, resulting in the identification of over 100 putative risk genes, the epigenetics of ASD has received less attention, and results have been inconsistent across studies. We aimed to investigate the contribution of DNA methylation (DNAm) to the risk of ASD and identify candidate biomarkers arising from the interaction of epigenetic mechanisms with genotype, gene expression, and cellular proportions. We performed DNAm differential analysis using whole blood samples from 75 discordant sibling pairs of the Italian Autism Network collection and estimated their cellular composition. We studied the correlation between DNAm and gene expression accounting for the potential effects of different genotypes on DNAm. We showed that the proportion of NK cells was significantly reduced in ASD siblings suggesting an imbalance in their immune system. We identified differentially methylated regions (DMRs) involved in neurogenesis and synaptic organization. Among candidate loci for ASD, we detected a DMR mapping to CLEC11A (neighboring SHANK1) where DNAm and gene expression were significantly and negatively correlated, independently from genotype effects. As reported in previous studies, we confirmed the involvement of immune functions in the pathophysiology of ASD. Notwithstanding the complexity of the disorder, suitable biomarkers such as CLEC11A and its neighbor SHANK1 can be discovered using integrative analyses even with peripheral tissues.

Epigenetics of Autism Spectrum Disorders: A Multi-level Analysis Combining Epi-signature, Age Acceleration, Epigenetic Drift and Rare Epivariations Using Public Datasets

BACKGROUND

Epigenetics of Autism Spectrum Disorders (ASD) is still an understudied field. The majority of the studies on the topic used an approach based on mere classification of cases and controls.

OBJECTIVE

The present study aimed at providing a multi-level approach in which different types of epigenetic analysis (epigenetic drift, age acceleration) are combined.

METHODS

We used publicly available datasets from blood (n = 3) and brain tissues (n = 3), separately. Firstly, we evaluated for each dataset and meta-analyzed the differential methylation profile between cases and controls. Secondly, we analyzed age acceleration, epigenetic drift and rare epigenetic variations.

RESULTS

We observed a significant epi-signature of ASD in blood but not in brain specimens. We did not observe significant age acceleration in ASD, while epigenetic drift was significantly higher compared to controls. We reported the presence of significant rare epigenetic variations in 41 genes, 35 of which were never associated with ASD. Almost all genes were involved in pathways linked to ASD etiopathogenesis (i.e., neuronal development, mitochondrial metabolism, lipid biosynthesis and antigen presentation).

CONCLUSION

Our data support the hypothesis of the use of blood epi-signature as a potential tool for diagnosis and prognosis of ASD. The presence of an enhanced epigenetic drift, especially in brain, which is linked to cellular replication, may suggest that alteration in epigenetics may occur at a very early developmental stage (i.e., fetal) when neuronal replication is still high.

Food for Thought: Machine Learning in Autism Spectrum Disorder Screening of Infants

Diagnoses of autism spectrum disorders (ASD) are typically made after toddlerhood by examining behavioural patterns. Earlier identification of ASD enables earlier intervention and better outcomes. Machine learning provides a data-driven approach of diagnosing autism at an earlier age. This review aims to summarize recent studies and technologies utilizing machine learning based strategies to screen infants and children under the age of 18 months for ASD, and identify gaps that can be addressed in the future. We reviewed nine studies based on our search criteria, which includes primary studies and technologies conducted within the last 10 years that examine children with ASD or at high risk of ASD with a mean age of less than 18 months old. The studies must use machine learning analysis of behavioural features of ASD as major methodology. A total of nine studies were reviewed, of which the sensitivity ranges from 60.7% to 95.6%, the specificity ranges from 50% to 100%, and the accuracy ranges from 60.9% to 97.7%. Factors that contribute to the inconsistent findings include the varied presentation of ASD among patients and study design differences. Previous studies have shown moderate accuracy, sensitivity and specificity in the differentiation of ASD and non-ASD individuals under the age of 18 months. The application of machine learning and artificial intelligence in the screening of ASD in infants is still in its infancy, as observed by the granularity of data available for review. As such, much work needs to be done before the aforementioned technologies can be applied into clinical practice to facilitate early screening of ASD.

Artificial intelligence and placental DNA methylation: newborn prediction and molecular mechanisms of autism in preterm children

BACKGROUND

Autism Spectrum Disorder (ASD) represents a heterogeneous group of disorders with a complex genetic and epigenomic etiology. DNA methylation is the most extensively studied epigenomic mechanism and correlates with altered gene expression. Artificial intelligence (AI) is a powerful tool for group segregation and for handling the large volume of data generated in omics experiments.

METHODS

We performed genome-wide methylation analysis for differential methylation of cytosine nucleotide (CpG) was performed in 20 postpartum placental tissue samples from preterm births. Ten newborns went on to develop autism (Autistic Disorder subtype) and there were 10 unaffected controls. AI including Deep Learning (AI-DL) platforms were used to identify and rank cytosine methylation markers for ASD detection. Ingenuity Pathway Analysis (IPA) to identify genes and molecular pathways that were dysregulated in autism.

RESULTS

We identified 4870 CpG loci comprising 2868 genes that were significantly differentially methylated in ASD compared to controls. Of these 431 CpGs met the stringent EWAS threshold (p-value <5 × 10^-8) along with ≥10% methylation difference between CpGs in cases and controls. DL accurately predicted autism with an AUC (95% CI) of 1.00 (1-1) and sensitivity and specificity of 100% using a combination of 5 CpGs [cg13858611 (NRN1), cg09228833 (ZNF217), cg06179765 (GPNMB), cg08814105 (NKX2-5), cg27092191 (ZNF267)] CpG markers. IPA identified five prenatally dysregulated molecular pathways linked to ASD.

CONCLUSIONS

The present study provides substantial evidence that epigenetic differences in placental tissue are associated with autism development and raises the prospect of early and accurate detection of the disorder.

Multiomics metabolic and epigenetics regulatory network in cancer: A systems biology perspective

Genetic, epigenetic, and metabolic alterations are all hallmarks of cancer. However, the epigenome and metabolome are both highly complex and dynamic biological networks in vivo. The interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer therapy. From this perspective, we first review the state of high-throughput biological data acquisition (i.e. multiomics data) and analysis (i.e. computational tools) and then propose a conceptual in silico metabolic and epigenetic regulatory network (MER-Net) that is based on these current high-throughput methods. The conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes, omics data acquisition, analysis of network information, and integration with validated database knowledge. Thus, MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics networks. We propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.

Placental DNA methylation changes and the early prediction of autism in full-term newborns

Autism spectrum disorder (ASD) is associated with abnormal brain development during fetal life. Overall, increasing evidence indicates an important role of epigenetic dysfunction in ASD. The placenta is critical to and produces neurotransmitters that regulate fetal brain development. We hypothesized that placental DNA methylation changes are a feature of the fetal development of the autistic brain and importantly could help to elucidate the early pathogenesis and prediction of these disorders. Genome-wide methylation using placental tissue from the full-term autistic disorder subtype was performed using the Illumina 450K array. The study consisted of 14 cases and 10 control subjects. Significantly epigenetically altered CpG loci (FDR p-value <0.05) in autism were identified. Ingenuity Pathway Analysis (IPA) was further used to identify molecular pathways that were over-represented (epigenetically dysregulated) in autism. Six Artificial Intelligence (AI) algorithms including Deep Learning (DL) to determine the predictive accuracy of CpG markers for autism detection. We identified 9655 CpGs differentially methylated in autism. Among them, 2802 CpGs were inter- or non-genic and 6853 intragenic. The latter involved 4129 genes. AI analysis of differentially methylated loci appeared highly accurate for autism detection. DL yielded an AUC (95% CI) of 1.00 (1.00-1.00) for autism detection using intra- or intergenic markers by themselves or combined. The biological functional enrichment showed, four significant functions that were affected in autism: quantity of synapse, microtubule dynamics, neuritogenesis, and abnormal morphology of neurons. In this preliminary study, significant placental DNA methylation changes. AI had high accuracy for the prediction of subsequent autism development in newborns. Finally, biologically functional relevant gene pathways were identified that may play a significant role in early fetal neurodevelopmental influences on later cognition and social behavior.

Truncating variants in the SHANK1 gene are associated with a spectrum of neurodevelopmental disorders

PURPOSE

In this study, we aimed to characterize the clinical phenotype of a SHANK1-related disorder and define the functional consequences of SHANK1 truncating variants.

METHODS

Exome sequencing (ES) was performed for six individuals who presented with neurodevelopmental disorders. Individuals were ascertained with the use of GeneMatcher and Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources (DECIPHER). We evaluated potential nonsense-mediated decay (NMD) of two variants by making knock-in cell lines of endogenous truncated SHANK1, and expressed the truncated SHANK1 complementary DNA (cDNA) in HEK293 cells and cultured hippocampal neurons to examine the proteins.

RESULTS

ES detected de novo truncating variants in SHANK1 in six individuals. Evaluation of NMD resulted in stable transcripts, and the truncated SHANK1 completely lost binding with Homer1, a linker protein that binds to the C-terminus of SHANK1. These variants may disrupt protein-protein networks in dendritic spines. Dispersed localization of the truncated SHANK1 variants within the spine and dendritic shaft was also observed when expressed in neurons, indicating impaired synaptic localization of truncated SHANK1.

CONCLUSION

This report expands the clinical spectrum of individuals with truncating SHANK1 variants and describes the impact these variants may have on the pathophysiology of neurodevelopmental disorders.

An intelligent Bayesian hybrid approach to help autism diagnosis

This paper proposes a Bayesian hybrid approach based on neural networks and fuzzy systems to construct fuzzy rules to assist experts in detecting features and relations regarding the presence of autism in human beings. The model proposed in this paper works with a database generated through mobile devices that deals with diagnoses of autistic characteristics in human beings who answer a series of questions in a mobile application. The Bayesian model works with the construction of Gaussian fuzzy neurons in the first and logical neurons in the second layer of the model to form a fuzzy inference system connected to an artificial neural network that activates a robust output neuron. The new fuzzy neural network model was compared with traditional state-of-the-art machine learning models based on high-dimensional based on real-world data sets comprising the autism occurrence in children, adults, and adolescents. The results (97.73- Children/94.32-Adolescent/97.28-Adult) demonstrate the efficiency of our new method in determining children, adolescents, and adults with autistic traits (being among the top performers among all ML models tested), can generate knowledge about the dataset through fuzzy rules.

Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children

Neurological disorders dramatically impact patients of any age population, their families, and societies. Pediatrics are among vulnerable age populations who differently experience the devastating consequences of neurological conditions, such as attention-deficit hyperactivity disorders (ADHD), autism spectrum disorders (ASD), cerebral palsy, concussion, and epilepsy. System-level understanding of these neurological disorders, particularly from the brain networks' dynamic perspective, has led to the significant trend of recent scientific investigations. While a dramatic maturation in the network science application domain is evident, leading to a better understanding of neurological disorders, such rapid utilization for studying pediatric neurological disorders falls behind that of the adult population. Aside from the specific technological needs and constraints in studying neurological disorders in children, the concept of development introduces uncertainty and further complexity topping the existing neurologically driven processes caused by disorders. To unravel these complexities, indebted to the availability of high-dimensional data and computing capabilities, approaches based on machine learning have rapidly emerged a new trend to understand pathways better, accurately diagnose, and better manage the disorders. Deep learning has recently gained an ever-increasing role in the era of health and medical investigations. Thanks to its relatively more minor dependency on feature exploration and engineering, deep learning may overcome the challenges mentioned earlier in studying neurological disorders in children. The current scoping review aims to explore challenges concerning pediatric brain development studies under the constraints of neurological disorders and offer an insight into the potential role of deep learning methodology on such a task with varying and uncertain nature. Along with pinpointing recent advancements, possible research directions are highlighted where deep learning approaches can assist in computationally targeting neurological disorder-related processes and translating them into windows of opportunities for interventions in diagnosis, treatment, and management of neurological disorders in children.

Artificial intelligence and leukocyte epigenomics: Evaluation and prediction of late-onset Alzheimer's disease

We evaluated the utility of leucocyte epigenomic-biomarkers for Alzheimer’s Disease (AD) detection and elucidates its molecular pathogeneses. Genome-wide DNA methylation analysis was performed using the Infinium MethylationEPIC BeadChip array in 24 late-onset AD (LOAD) and 24 cognitively healthy subjects. Data were analyzed using six Artificial Intelligence (AI) methodologies including Deep Learning (DL) followed by Ingenuity Pathway Analysis (IPA) was used for AD prediction. We identified 152 significantly (FDR p<0.05) differentially methylated intragenic CpGs in 171 distinct genes in AD patients compared to controls. All AI platforms accurately predicted AD with AUCs ≥0.93 using 283,143 intragenic and 244,246 intergenic/extragenic CpGs. DL had an AUC = 0.99 using intragenic CpGs, with both sensitivity and specificity being 97%. High AD prediction was also achieved using intergenic/extragenic CpG sites (DL significance value being AUC = 0.99 with 97% sensitivity and specificity). Epigenetically altered genes included CR1L & CTSV (abnormal morphology of cerebral cortex), S1PR1 (CNS inflammation), and LTB4R (inflammatory response). These genes have been previously linked with AD and dementia. The differentially methylated genes CTSV & PRMT5 (ventricular hypertrophy and dilation) are linked to cardiovascular disease and of interest given the known association between impaired cerebral blood flow, cardiovascular disease, and AD. We report a novel, minimally invasive approach using peripheral blood leucocyte epigenomics, and AI analysis to detect AD and elucidate its pathogenesis.

Placental DNA methylation profiles in opioid-exposed pregnancies and associations with the neonatal opioid withdrawal syndrome

Opioid abuse during pregnancy can result in Neonatal Opioid Withdrawal Syndrome (NOWS). We investigated genome-wide methylation analyses of 96 placental tissue samples, including 32 prenatally opioid-exposed infants with NOWS who needed therapy (+Opioids/+NOWS), 32 prenatally opioid-exposed infants with NOWS who did not require treatment (+Opioids/-NOWS), and 32 prenatally unexposed controls (-Opioids/-NOWS, control). Statistics, bioinformatics, Artificial Intelligence (AI), including Deep Learning (DL), and Ingenuity Pathway Analyses (IPA) were performed. We identified 17 dysregulated pathways thought to be important in the pathophysiology of NOWS and reported accurate AI prediction of NOWS diagnoses. The DL had an AUC (95% CI) =0.98 (0.95-1.0) with a sensitivity and specificity of 100% for distinguishing NOWS from the +Opioids/-NOWS group and AUCs (95% CI) =1.00 (1.0-1.0) with a sensitivity and specificity of 100% for distinguishing NOWS versus control and + Opioids/-NOWS group versus controls. This study provides strong evidence of methylation dysregulation of placental tissue in NOWS development.