Peptidome Analysis Reveals Novel Serum Biomarkers for Children with Autism Spectrum Disorder in China

Yang, Juan; Xiong, Xiaofan; Zhou, Xiaobo; Lin, Han; Ni, Lei; Wang, Wenjing; Wang, Xiaofei; Zhao, Lingyu; Shao, Dongdong; Huang, Chen

doi:10.1002/prca.201700164

Cited by 33 publications

(23 citation statements)

References 34 publications

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“…Fewer proteomics studies on ASD have been published than those based on other disciplines, such as genomics or transcriptomics. Many of them rely on post-mortem brain tissues [ 50 , 51 , 52 ], serum [ 53 , 54 , 55 , 56 ], plasma [ 57 , 58 , 59 ], urine [ 63 ], saliva [ 60 , 61 , 62 ] direct samples or lymphoblastoid cell lines [ 64 ] ( Table 1 ).…”

Section: Translational and Clinical Proteomicsmentioning

confidence: 99%

“…In another study, apolipoproteins involved in cholesterol metabolism were found to be increased in an ASD cohort with respect to the control subjects [ 54 ]. In addition, two studies reported an alteration in proteins implicated in lipid metabolism, in the inflammation process and cell growth [ 55 , 56 ] ( Table 1 ).…”

Section: Translational and Clinical Proteomicsmentioning

confidence: 99%

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Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery

Ristori

Mortera

Marzano

et al. 2020

IJMS

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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.

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Section: Translational and Clinical Proteomicsmentioning

confidence: 99%

Section: Translational and Clinical Proteomicsmentioning

confidence: 99%

Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery

Ristori

Mortera

Marzano

et al. 2020

IJMS

View full text Add to dashboard Cite

show abstract

“…These findings suggest that TAARs might play an important role in autism spectrum disorders (Huang et al 2016). Accordingly, the peptidome analysis of 68 Han Chinese children with autism and 80 healthy controls allowed the identification of 8 potential serum biomarker peaks, including TAAR6 (Yang et al 2018). A significantly different peak area of the TAAR6 fragment was also reported in 30 patients with vascular cognitive impairment, relative to 30 healthy controls (Xi et al 2009).…”

Section: Exome Sequencing and Changes In Taar Gene Expressionmentioning

confidence: 92%

Molecular Variants in Human Trace Amine-Associated Receptors and Their Implications in Mental and Metabolic Disorders

Rutigliano

Zucchi

2019

Cell Mol Neurobiol

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We provide a comprehensive review of the available evidence on the pathophysiological implications of genetic variants in the human trace amine-associated receptor (TAAR) superfamily. Genes coding for trace amine-associated receptors (taars) represent a multigene family of G-protein-coupled receptors, clustered to a small genomic region of 108 kb located in chromosome 6q23, which has been consistently identified by linkage analyses as a susceptibility locus for schizophrenia and affective disorders. Most TAARs are expressed in brain areas involved in emotions, reward and cognition. TAARs are activated by endogenous trace amines and thyronamines, and evidence for a modulatory action on other monaminergic systems has been reported. Therefore, linkage analyses were followed by fine mapping association studies in schizophrenia and affective disorders. However, none of these reports has received sufficient universal replication, so their status remains uncertain. Single nucleotide polymorphisms in taars have emerged as susceptibility loci from genome-wide association studies investigating migraine and brain development, but none of the detected variants reached the threshold for genome-wide significance. In the last decade, technological advances enabled single-gene or whole-exome sequencing, thus allowing the detection of rare genetic variants, which may have a greater impact on the risk of complex disorders. Using these approaches, several taars (especially taar1) variants have been detected in patients with mental and metabolic disorders, and in some cases, defective receptor function has been demonstrated in vitro. Finally, with the use of transcriptomic and peptidomic techniques, dysregulations of TAARs (especially TAAR6) have been identified in brain disorders characterized by cognitive impairment.

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“…Transcriptomics studies use cDNA microarrays and high-throughput RNA sequencing technology to obtain gene expression data, combining with gene co-expression network analysis to discover the molecular mechanisms related to ASD [15,16]. Proteomics studies have found that the clinical manifestations of ASD may be related to changes in proteins, such as mutations leading to changes in protein and amino acid sequences, and mutations in gene regulatory regions leading to changes in protein expression or abnormal modification of proteins [17,18]. In recent years, the acquisition of specific biomarkers through metabolomics research is effective for early ASD screening and diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Design Of ASD Subtyping Approach based on Multi-Omics Data to Promote Personalized Healthcare

Chen¹,

Lu²,

Lu³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder that has been confirmed to be related to some genetics risk factors which can lead to different clinical phenotypes. At present, ASD is mainly diagnosed based on some behavior and cognitive scales, which can not reveal the mechanism of disease occurrence, development and prognosis. In recent years, some studies have applied omics techniques into ASD research, but these studies are only based on single omics data source such as genomics, proteomics or transcriptomic without investigating ASD subtypes from integration of multiomics data. In this study, we proposed an ASD subtyping framework that integrates clinical and multiomics data to identify and analyze ASD subtypes at the molecular level. Due to the heterogeneity of different data modalities, a fusion clustering strategy was used to produce more accurate and interpretable clusters. Based on ASD subtyping results, we also proposed a classification framework to predict the subtype of new ASD patients. Deep learning method was used to extract features from each data modality, then all extracted features were integrated by the multiple kernel learning method to improve the classification accuracy.

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Peptidome Analysis Reveals Novel Serum Biomarkers for Children with Autism Spectrum Disorder in China

Abstract: These findings reveal the exceptional disease etiology of ASD from a serum proteomic perspective, and the identified proteins might be potential biomarkers for ASD diagnosis.

Cited by 33 publications

References 34 publications

Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery

Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery

Molecular Variants in Human Trace Amine-Associated Receptors and Their Implications in Mental and Metabolic Disorders

Design Of ASD Subtyping Approach based on Multi-Omics Data to Promote Personalized Healthcare

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