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Related Experiment Video

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Studying Protein Function and the Role of Altered Protein Expression by Antibody Interference and Three-dimensional Reconstructions

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Transcriptomic Evidence Reveals the Dysfunctional Mechanism of Synaptic Plasticity Control in ASD.

Chao Kong¹, Zhitong Bing², Lei Yang²

¹School of Systems Science, Beijing Normal University, Beijing 100875, China.

|January 25, 2025

Summary

This study reveals dysregulated translation control in Autism Spectrum Disorder (ASD) by analyzing synaptic plasticity networks. Novel algorithms identify key molecular players and cell-specific patterns, offering potential diagnostic biomarkers for ASD.

Keywords:

ASD causal network logic model signal transduction network single cell sequence synapse plasticity transcriptome

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Area of Science:

Neuroscience
Systems Biology
Computational Biology

Background:

Autism Spectrum Disorder (ASD) is characterized by synaptic plasticity dysfunction, but the underlying molecular mechanisms are not fully understood.
Investigating postsynaptic signal transduction in glutamatergic neurons is crucial for understanding ASD pathophysiology.

Purpose of the Study:

To unveil the malfunction of translation control in Autism Spectrum Disorder (ASD) using single-cell nucleus transcriptomics data from the Prefrontal Cortex (PFC).
To analyze the postsynaptic signal transduction network and identify dysregulated molecular mechanisms in ASD.

Main Methods:

Developed a pipeline to convert signal transduction networks into mRNA Signaling-Regulatory Networks (mSiReN) for RNA-level analysis.
Employed Cell-Specific Network Inference via Integer Value Programming and Causal Reasoning (CS-NIVaCaR) and Cell-Specific Probabilistic Contextualization for mRNA Regulatory Networks (CS-ProComReN) to identify core modules and activated sub-pathways in different cell types.

Main Results:

Identified dysregulated pivotal molecules (EIF4EBP1, EIF4E) involved in protein translation, crucial for long-term potentiation (LTP) and long-term depression (LTD), despite lacking differential expression.
Uncovered distinct, causally linked activation patterns of the EIF4EBP1-EIF4E module in excitatory and inhibitory neurons.

Conclusions:

Introduced a novel methodology (mSiReN) to analyze transcriptomics data for parsing signal transduction networks and mapping them to the protein level.
Highlighted potential diagnostic and prognostic biomarkers within identified convergent dysregulation pathways in ASD.
Proposed algorithms as powerful tools for systems biology research in analyzing omics and regulatory networks.