Researchers have identified the cell-specific molecular network of autism spectrum disorder

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Professor Kim Min-sik’s team of the Department of New Biology, DGIST (President: Kuk Yang), succeeded in identifying the cell-specific molecular network of autism spectrum disorder. It is expected to lay the foundation for treating autism spectrum disorder.

Autism spectrum disorder is known to occur from early childhood and is a neuro-developmental disorder characterized by continuous impairment of social communication and interaction-related behaviors leading to limited ranges of behavioral patterns, interests, and activities, and repetitive behaviors.

Most autism spectrum disorder patients have behavioral disorders, sometimes accompanied by other developmental disabilities. Currently, since there is no accurate molecular diagnosis method, early diagnosis of autism spectrum disorder is made at a fairly late period, and there is no appropriate treatment.

Professor Kim Min-sik’s team utilized the Cntnap2 defect model, a spectral disorder mouse model established by Professor Lee Yong-Seok’s team at Seoul National University College of Medicine to extract prefrontal cortex tissue and performed mass spectrometry-based integrated quantitative proteomic and metabolomic analysis.

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Cntnap2

This gene encodes a member of the neurexin family which functions in the vertebrate nervous system as cell adhesion molecules and receptors. This protein, like other neurexin proteins, contains epidermal growth factor repeats and laminin G domains. In addition, it includes an F5/8 type C domain, discoidin/neuropilin- and fibrinogen-like domains, thrombospondin N-terminal-like domains and a putative PDZ binding site.

This protein is localized at the juxtaparanodes of myelinated axons, and mediates interactions between neurons and glia during nervous system development and is also involved in localization of potassium channels within differentiating axons. This gene encompasses almost 1.5% of chromosome 7 and is one of the largest genes in the human genome.

It is directly bound and regulated by forkhead box protein P2, a transcription factor related to speech and language development. This gene has been implicated in multiple neurodevelopmental disorders, including Gilles de la Tourette syndrome, schizophrenia, epilepsy, autism, ADHD and intellectual disability. [provided by RefSeq, Jul 2017]

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In addition, by comparing and analyzing this with previously reported big data of autism spectrum disorder patients, the team confirmed that problems occur in networks such as metabolism and synapses in excitable neurons.

Professor Kim Min-sik of the Department of New Biology said, “The multi-omics integrated analysis technology developed through this study has advanced the pathological understanding of autism spectrum disorder and made it possible to discover an integrated network ranging from molecular-level cell differentiation induced by a specific autism gene to biometric information,“ and added,

“We are trying to find the core network of autism spectrum disorder and discover treatment targets by conducting an integrated analysis of various models.”

Meanwhile, the results of this research were published in ‘Molecular Psychiatry’ on October 17, 2022, and this research was carried out with support from the Brain Science Source Technology Development Project of the Ministry of Science and ICT.


Autism spectrum disorder (ASD) is a cluster of neurodevelopmental conditions characterized by impairments in socio-communicative ability, repetitive behaviors, and abnormal sensory perception [1]. The etiology of ASD is multifactorial and not comprehensively understood, with both genetic and environmental factors playing a role [2, 3].

In the last decade, research in the genetic component of ASD has been fruitful, with hundreds of genes having been identified as associated with the disorder [4]. Moreover, anomalies at the level of the synapse have been identified since long ago as one of the main underlying mechanisms of ASD [5, 6], as well as related to abnormal brain connectivity between neuronal subpopulations and systems [7].

As individuated in a review by Guang and colleagues [5], as well as in a transcriptomic analysis by He and colleagues [6], SHANK3, SHANK1, SHANK2, NLGN3, NGLN4X, MECP2, and CNTNAP2 genes are of particular interest for ASD research due to their role in synaptic formation, maintenance, and transmission. Specifically, the SHANK family encodes for essential scaffold proteins in the postsynaptic density of excitatory synapses [8], with mutations leading to altered levels of postsynaptic density proteins, synapse morphology, and excitatory transmission [8–11].

NLGN3 and NLGN4X encode for postsynaptic cell adhesion proteins, with NLGN3’s mutations having been reported to increase inhibitory transmission and NLGN4X to cause altered excitatory transmission [12–14]. Moreover, defects in NLGN3 and NLGN4X have been reported to lead to impaired synaptogenesis [12]. Presynaptic cell adhesion protein NRXN1’s role in synaptic disruption has been attributed to altered Ca2+ entry at the synapse thus impairing neurotransmitter release [5]. Dysfunction in CNTNAP2 has been linked to impaired axonal growth and reduced dendritic arborisation of inhibitory interneurons as well as impaired synaptic transmission [15, 16]. MECP2 encodes for a regulator of chromatin remodeling mostly responsible for silencing gene expression, with deficiencies in the gene expression having been reported to induce decreased excitatory transmission due to a decrease in synapse plasticity and number [17, 18].

The autistic brain has been studied extensively with various approaches, from cellular resolution to whole-brain imaging. One such technique is voxel-based morphometry (VBM) [19], which allows to quantify regional neuroanatomical differences in volume/concentration in individuals with ASD compared to typically developing controls (TDCs). Moreover, using coordinate-based meta-analytic (CBMA) approaches, different authors have identified spatially consistent gray matter (GM) abnormalities across VBM published findings regarding several ASD groups analyzed worldwide. To note, these abnormal territories tend to encompass a wide set of multimodal, perceptual, subcortical, and cerebellar areas [20–24].

These important findings notwithstanding, the pathophysiological mechanisms and genetic effects that underpin atypical GM patterns in ASD remain largely unappreciated. A more comprehensive insight into the disease may be given by combining the genetic and neuroimaging data. In recent years, studies linking genomic variation to neuroimaging meta-analysis are starting to appear. For example, Grasby and colleagues [25] combined genetic and magnetic resonance imaging data to link genetic variation to cortical surface area and thickness. The authors demonstrated that genetic variants associated with brain morphology are also associated with cognitive function as well as neuropsychiatric diseases [25]. Also, Lau and colleagues [26] related gradients of gene expression to brain structure and development.

Resting-state functional magnetic resonance imaging (rs-fMRI) [27], a technique able to estimate the intrinsic activity interactions between brain regions that occur during the rest (i.e. when no active task is being performed and the brain is not actively engaged), represents another useful tool to further our understanding of brain disorders [28–32]. A number of resting-state networks (RSNs) have been identified via rs-fMRI [27]. As suggested by different studies [33–36], these RSNs reveal patterns of activity that are consistent across subjects and reproducible across participants and time.

In particular, canonical RSNs such as the default mode network (DMN), salience network (SN), dorsal attention network (DAN), and sensorimotor network (SMN) were often found to be functionally or anatomically altered in ASD (for a review on the topic see [37]). Moreover, RSNs are highly inheritable, and influenced by genomic factors [38, 39]. Therefore, RSNs could be a useful perspective to interpret the genetic spatial variation associated with ASD morphological alteration.

In this study, we aim to investigate the relation between brain anatomical alterations and genetic expression of the following genes: SHANK3, SHANK1, SHANK2, NLGN3, NLGN4X, MECP2 and CNTNAP2. These genes are selected because of their relation to ASD pathology and role in synaptic transmission and plasticity [5, 6]. Specifically, we aim to identify the most consistent brain anatomical alterations in ASD and link these alterations with the corresponding genetic expression.

Moreover, we also complement the gene-structure results with a functional interpretation informed by the canonical resting state networks. This choice is motived by an increasing number of experimental efforts suggesting that the development of neuroanatomical alteration patterns in brain disorders are influenced by functional connectivity constraints [29–31, 40, 41]. Therefore, we expect to see the neuronal alteration and the selected genes distributed according to spatial patterns meaningful for brain function and functional connectivity [29]. What is more, we expect that, when assigning those altered regions to canonical RSNs, high-order networks typically associated with ASD, such as the DMN, would be found to be particularly related to our set of genes [42].

reference link : https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0277466


Original Research: Open access.
Cntnap2-dependent molecular networks in autism spectrum disorder revealed through an integrative multi-omics analysis” by Kim Min-sik et al. Molecular Psychiatry

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