Abstract
Autism spectrum disorders (ASDs) are neurodevelopmental disorders characterized by behavioral alterations and currently affect about 1% of children. Significant genetic factors and mechanisms underline the causation of ASD. Indeed, many affected individuals are diagnosed with chromosomal abnormalities, submicroscopic deletions or duplications, single-gene disorders or variants. However, a range of metabolic abnormalities has been highlighted in many patients, by identifying biofluid metabolome and proteome profiles potentially usable as ASD biomarkers. Indeed, next-generation sequencing and other omics platforms, including proteomics and metabolomics, have uncovered early age disease biomarkers which may lead to novel diagnostic tools and treatment targets that may vary from patient to patient depending on the specific genomic and other omics findings. The progressive identification of new proteins and metabolites acting as biomarker candidates, combined with patient genetic and clinical data and environmental factors, including microbiota, would bring us towards advanced clinical decision support systems (CDSSs) assisted by machine learning models for advanced ASD-personalized medicine. Herein, we will discuss novel computational solutions to evaluate new proteome and metabolome ASD biomarker candidates, in terms of their recurrence in the reviewed literature and laboratory medicine feasibility. Moreover, the way to exploit CDSS, performed by artificial intelligence, is presented as an effective tool to integrate omics data to electronic health/medical records (EHR/EMR), hopefully acting as added value in the near future for the clinical management of ASD.
Highlights
Subgroups can be individuated in Autism spectrum disorders (ASDs) patient cohorts, including functioning levels based on different parameters such as IQ values, language, and/or reading impairment, and this could help in the identification of gene/protein candidates or molecular mechanisms that can be associated with one or more of these subpopulations
To better understand ASD and be more able to stratify the population of ASD patients into subgroups, it is necessary to integrate all of the data from the omics with the data collected by the clinician and analyze them through machine learning models
Genetic and environmental factors are implicated in changes in the brain and metabolism, such as mitochondrial dysfunction, neurotransmitters alteration, abnormal neuron development, neuroinflammation and immune dysregulation and oxidative stress
Summary
Autism spectrum disorders (ASDs) are a complex set of neurodevelopmental diseases, behaviourally that affect (ASDs) several spheres of mental set development. Given the growing interest in identifying new functional traits of the disease and novel biomarkers, Targeted-metagenomics studies on the ecology of the microbiota, performed using big data obtained by metabolomics and proteomics approaches from blood, urine or saliva specimens next-generation sequencing (NGS), have revealed that specific signatures, such as Prevotella, should be collected and stored in open source digital biobanks available to omics scientists and Enterococcus, Lactobacillus, Ruminococcus, Faecalibacterium prausnitzii, Sutterella, and Bifidobacterium, clinicians for deep phenotyping This system biology-based approach will allow multidimensional are overrepresented in children with ASD compared to healthy controls [28,44,45,46]. Future to approach ASD phenotype stratification and hopefully to disentangle the complexity of this disease due to multifactorial components
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