An integrated transcriptomic analysis of autism spectrum disorder

He, Yi; Zhou, Yuan; Ma, Wei; Wang, Juan

doi:10.1038/s41598-019-48160-x

Cited by 30 publications

(33 citation statements)

References 47 publications

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“…Is it realistic to study the xy interaction independent from the triadic interconnection (xyz) it lives in? Specially in case of heterogeneous brain disorders such as ASD, a complex neurodevelopmental disorder with substantial heterogeneity not only in multiple causes and courses but also in the onset [8][9][10][11] . Recently, this concern has been well addressed by the long-standing structural balance theory (SBT) in variety of scenarios, and triadic interactions are accepted to play…”

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confidence: 99%

Altered structural balance of resting-state networks in autism

Moradimanesh

Khosrowabadi

Gordji

et al. 2021

Sci Rep

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What makes a network complex, in addition to its size, is the interconnected interactions between elements, disruption of which inevitably results in dysfunction. Likewise, the brain networks’ complexity arises from interactions beyond pair connections, as it is simplistic to assume that in complex networks state of a link is independently determined only according to its two constituting nodes. This is particularly of note in genetically complex brain impairments, such as the autism spectrum disorder (ASD), which has a surprising heterogeneity in manifestations with no clear-cut neuropathology. Accordingly, structural balance theory (SBT) affirms that in real-world signed networks, a link is remarkably influenced by each of its two nodes’ interactions with the third node within a triadic interrelationship. Thus, it is plausible to ask whether ASD is associated with altered structural balance resulting from atypical triadic interactions. In other words, it is the abnormal interplay of positive and negative interactions that matters in ASD, besides and beyond hypo (hyper) pair connectivity. To address this question, we explore triadic interactions based on SBT in the weighted signed resting-state functional magnetic resonance imaging networks of participants with ASD relative to healthy controls (CON). We demonstrate that balanced triads are overrepresented in the ASD and CON networks while unbalanced triads are underrepresented, providing first-time empirical evidence for the strong notion of structural balance on the brain networks. We further analyze the frequency and energy distributions of different triads and suggest an alternative description for the reduced functional integration and segregation in the ASD brain networks. Moreover, results reveal that the scale of change in the whole-brain networks’ energy is more narrow in the ASD networks during development. Last but not least, we observe that energy of the salience network and the default mode network are lower in ASD, which may be a reflection of the difficulty in dynamic switching and flexible behaviors. Altogether, these results provide insight into the atypical structural balance of the ASD brain (sub) networks. It also highlights the potential value of SBT as a new perspective in functional connectivity studies, especially in the case of neurodevelopmental disorders.

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confidence: 99%

Altered structural balance of resting-state networks in autism

Moradimanesh

Khosrowabadi

Gordji

et al. 2021

Sci Rep

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“…Interestingly, two of the main hubs, HERC4 and TMEM30A, were included in immune system pathways. These findings suggested that core ASD co-predictors are linked to responses of the immune system and its signal can be detected in blood, as it has been reported earlier (Enstrom et al, 2009;He et al, 2019). To examine nonlinear and linear associations, we compared the most highly ranked co-predictors to the co-expression profiles.…”

Section: Discussionmentioning

confidence: 58%

“…It has been shown that the gene expression profile of NK cells was altered in peripheral blood of children with ASD (Enstrom et al, 2009). Another transcriptomics data analysis of ASD subjects demonstrated similarities in functional enrichment between brain and blood (He et al, 2019). The study revealed significant enrichment in terms of immune response, mitochondrion-related functions, and oxidative phosphorylation.…”

Section: Introductionmentioning

confidence: 88%

Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder

et al. 2021

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Autism spectrum disorder (ASD) is a heterogeneous neuropsychiatric disorder with a complex genetic background. Analysis of altered molecular processes in ASD patients requires linear and nonlinear methods that provide interpretable solutions. Interpretable machine learning provides legible models that allow explaining biological mechanisms and support analysis of clinical subgroups. In this work, we investigated several case-control studies of gene expression measurements of ASD individuals. We constructed a rule-based learning model from three independent datasets that we further visualized as a nonlinear gene-gene co-predictive network. To find dissimilarities between ASD subtypes, we scrutinized a topological structure of the network and estimated a centrality distance. Our analysis revealed that autism is the most severe subtype of ASD, while pervasive developmental disorder-not otherwise specified and Asperger syndrome are closely related and milder ASD subtypes. Furthermore, we analyzed the most important ASD-related features that were described in terms of gene co-predictors. Among others, we found a strong co-predictive mechanism between EMC4 and TMEM30A, which may suggest a co-regulation between these genes. The present study demonstrates the potential of applying interpretable machine learning in bioinformatics analyses. Although the proposed methodology was designed for transcriptomics data, it can be applied to other omics disciplines.

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“…Next-Generation Sequencing (NGS) technology has gifted biomedical science a tool for exploration of big data, especially for complex diseases like ASD. While there have been several rigorous efforts to explore the transcriptome of ASD [7][8][9][10][11][12][13][14], there has been a limited exploration of the proteome of ASD [15][16][17][18][19]. Little has been done to understand the proteome, let alone characterize the functional effectors of the phosphoproteome in syndromic ASD contexts, such as those arising from Mendelian gene variation, here, germline PTEN mutation.…”

Section: Discussionmentioning

confidence: 99%

Transcriptome-(Phospho)proteome Characterization of the Brain of a Constitutional Model of Cytoplasmic-predominant Pten Expression With Autism-Like Phenotypes

Thacker

Eng

2020

Preprint

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BackgroundPTEN, a well-studied tumor suppressor, has one of the strongest Mendelian associations with autism spectrum disorder (ASD), representing a special case in autism’s complex genetic architecture. Animal modeling for constitutional Pten mutation creates an opportunity to study how disruption of Pten affects neurobiology, providing insights that may be generalizable or at least inform our understanding of ASD. Although the neural transcriptome has been well characterized in Pten models, little has been done concerning the proteome and phosphoproteome. This is a critical gap in knowledge given that these –omic landscapes are more proximal to the actively observed biology than the transcriptome.MethodsWe sought to comprehensively characterize the neural proteome and phosphoproteome of the Ptenm3m4/m3m4 mouse, which exhibits cytoplasmic-predominant Pten expression. Proteomic and phosphoproteomic scans of Ptenm3m4/m3m4 and wildtype mouse brain at two-weeks- (P14) and six-weeks-of-age (P40) were performed using liquid chromatography with tandem mass spectrometry technology. Following quantification of differentially expressed/phosphorylated proteins, we performed gene overlap, gene enrichment, pathway, and network analyses to identify the similarity across the various datasets and understand the affected biological landscape.ResultsWe identified numerous differentially expressed/phosphorylated proteins, finding that dysregulation was greater at P40, consistent with the prior neural transcriptome data. We found the affected biological pathways were largely related to PTEN function, neurological processes, or neuroinflammation. Although we found minimal overlap among differentially expressed transcriptome-proteome-phosphoproteome molecules between P14 and P40 brains, there was congruence amongst the affected pathways. Importantly, network analysis identified Pten and Psd-95 as predominant regulatory nodes in the proteome and phosphoproteome, respectively. Moreover, we found overlap between our differentially expressed/phosphorylated proteins and known ASD risk genes.ConclusionsDifferential expression/phosphorylation revealed by transcriptome-proteome/phosphoproteome analyses of a germline Pten mutation model point to ASD risk genes like Pten and Psd-95 as major hubs in the protein networks, highlighting their important regulatory influence. Our observations here suggest Pten and Psd-95, known interactors in biological networks in the brain, are critical to either initiation or maintenance of cellular and perhaps organismal phenotypes related to ASD. Future research should explore rescuing Pten and Psd-95 function in attempts to ameliorate neurological pathologies and behavioral abnormalities.

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An integrated transcriptomic analysis of autism spectrum disorder

Cited by 30 publications

References 47 publications

Altered structural balance of resting-state networks in autism

Altered structural balance of resting-state networks in autism

Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder

Transcriptome-(Phospho)proteome Characterization of the Brain of a Constitutional Model of Cytoplasmic-predominant Pten Expression With Autism-Like Phenotypes

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