AUTHOR=Li Shensuo , Lu Changhao , Zhao Zhenzhen , Lu Dong , Zheng Guangyong 

TITLE=Uncovering neuroinflammation-related modules and potential repurposing drugs for Alzheimer's disease through multi-omics data integrative analysis

JOURNAL=Frontiers in Aging Neuroscience

VOLUME=Volume 15 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2023.1161405

DOI=10.3389/fnagi.2023.1161405

ISSN=1663-4365

ABSTRACT=Background: Neuroinflammation is one of key factors leading to neuron death and synapse dysfunction in Alzheimer’s disease (AD). Amyloid-β (Aβ) is thought having association with microglia activation and triggering neuroinflammation in AD. However, inflammation response in brain disorders is heterogenous and thus it is necessary to unveil the specific gene module of neuroinflammation caused by Aβ in AD, which might provide novel biomarkers for AD diagnosis and help understand mechanism of the disease.
Methods: Transcriptomic datasets of brain region tissues from AD patients and corresponding normal tissues were first used to identify gene modules through the weighted gene co-expression network analysis (WGCNA) method. Then key modules highly associated with Aβ accumulation and neuroinflammatory response were pinpointed by combining module expression score and functional information. Meanwhile, relationship of the Aβ-associated module to neuron and microglia was explored based on snRNA-seq data. Afterwards, transcription factor (TF) enrichment and the SCENIC analysis were performed on the Aβ-associated module to discover the related upstream regulators and then a PPI network proximity method was employed to repurpose potential approved drugs for AD. 
Results: A total of 16 co-expression modules were primarily obtained by the WGCNA method. Among them, the green module was significantly correlated with Aβ accumulation and its function was mainly involved in neuroinflammation response and neuron death. Thus, the module was termed as Amyloid-β induced neuroinflammation module (AIM). Moreover, the module was negatively correlated with neuron percentage and showed a close association with inflammatory microglia. Finally, based on the module, several important TFs were recognized as potential diagnosis biomarkers for AD and then 20 possible drugs including Ibrutinib and Ponatinib were picked out for the disease.
Conclusion: In this study, a specific gene module, termed AIM, was identified as key sub-network of Aβ accumulation and neuroinflammation in AD. Besides, the module was verified having association with neuron degeneration and inflammatory microglia transformation. Moreover, some promising TFs and potential repurposing drugs were presented for AD based on the module. Finding of the study shed new light on mechanistic investigation of AD and might make benefits to treatment of the disease.