Kong Chao, Bing Zhitong, Yang Lei, Huang Zigang, Wang Wenxu, Grebogi Celso
School of Systems Science, Beijing Normal University, Beijing 100875, China.
Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China.
Genes (Basel). 2024 Dec 25;16(1):11. doi: 10.3390/genes16010011.
BACKGROUND/OBJECTIVES: A prominent endophenotype in Autism Spectrum Disorder (ASD) is the synaptic plasticity dysfunction, yet the molecular mechanism remains elusive. As a prototype, we investigate the postsynaptic signal transduction network in glutamatergic neurons and integrate single-cell nucleus transcriptomics data from the Prefrontal Cortex (PFC) to unveil the malfunction of translation control.
We devise an innovative and highly dependable pipeline to transform our acquired signal transduction network into an mRNA Signaling-Regulatory Network (mSiReN) and analyze it at the RNA level. We employ Cell-Specific Network Inference via Integer Value Programming and Causal Reasoning (CS-NIVaCaR) to identify core modules and Cell-Specific Probabilistic Contextualization for mRNA Regulatory Networks (CS-ProComReN) to quantitatively reveal activated sub-pathways involving MAPK1, MKNK1, RPS6KA5, and MTOR across different cell types in ASD.
The results indicate that specific pivotal molecules, such as EIF4EBP1 and EIF4E, lacking Differential Expression (DE) characteristics and responsible for protein translation with long-term potentiation (LTP) or long-term depression (LTD), are dysregulated. We further uncover distinct activation patterns causally linked to the EIF4EBP1-EIF4E module in excitatory and inhibitory neurons.
Importantly, our work introduces a methodology for leveraging extensive transcriptomics data to parse the signal transduction network, transforming it into mSiReN, and mapping it back to the protein level. These algorithms can serve as potent tools in systems biology to analyze other omics and regulatory networks. Furthermore, the biomarkers within the activated sub-pathways, revealed by identifying convergent dysregulation, illuminate potential diagnostic and prognostic factors in ASD.
背景/目的：自闭症谱系障碍（ASD）中一个突出的内表型是突触可塑性功能障碍，但其分子机制仍不清楚。作为一个范例，我们研究了谷氨酸能神经元中的突触后信号转导网络，并整合了来自前额叶皮质（PFC）的单细胞细胞核转录组学数据，以揭示翻译控制的功能障碍。
我们设计了一种创新且高度可靠的流程，将我们获得的信号转导网络转化为mRNA信号调节网络（mSiReN），并在RNA水平上进行分析。我们采用通过整数值编程和因果推理进行细胞特异性网络推断（CS-NIVaCaR）来识别核心模块，并采用mRNA调节网络的细胞特异性概率情境化（CS-ProComReN）来定量揭示ASD中不同细胞类型中涉及MAPK1、MKNK1、RPS6KA5和MTOR的激活子通路。
结果表明，缺乏差异表达（DE）特征且负责长期增强（LTP）或长期抑制（LTD）蛋白翻译的特定关键分子，如EIF4EBP1和EIF4E，存在失调。我们进一步发现了与兴奋性和抑制性神经元中EIF4EBP1-EIF4E模块有因果关系的不同激活模式。
重要的是，我们的工作引入了一种利用广泛的转录组学数据来解析信号转导网络、将其转化为mSiReN并映射回蛋白质水平的方法。这些算法可作为系统生物学中分析其他组学和调节网络的有力工具。此外，通过识别趋同失调揭示的激活子通路中的生物标志物，为ASD中的潜在诊断和预后因素提供了线索。
