Identifying neuroinflammation in omics datasets with a computational biological network model of reactive astrogliosis. (December 2021)

• Record Type: Journal Article
• Authors: Barkhuizen, Melinda; Mathis, Carole; Peitsch, Manuel C; Talikka, Marja
• Is Part Of: Alzheimer's & dementia. Volume 17(2021)Supplement 3
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.051001
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Neuroimmune responses by astrocytes and microglia play a key role in the pathogenesis of neurodegenerative diseases. Reactive astrocytes have diverse responses to triggers and can release a wide variety of effector molecules in an insult‐specific manner. Identifying key mechanisms of reactive astrogliosis in large datasets is key to finding new therapeutic targets, but the interpretation of such datasets is challenging. Causal biological network (CBNs) models assemble available biological knowledge in a structured computable format that facilitates mechanistic interpretation of molecular data in a well‐defined biological context. Here, we present a CBN model of pan‐reactive astrogliosis genes as a new tool to explore and quantify the extent of reactive astrogliosis based on transcriptomic data and identify key molecular drivers of the phenotype. Methods: Mechanistic knowledge from 133 original publications on astrogliosis were used to build this CBN model. The statements were translated into computational statements using the Biological Expression Language and assembled into a CBN. Biological entities are represented by molecular nodes connected with edges that indicate the causal relationship between these nodes. A collection of these causal relationships were assembled to form a CBN. Information about gene expression regulation by a subset of the nodes was used to build a second scorable layer to infer the activity of the CBN nodes in transcriptomic datasets. The extent of perturbation of the astrogliosis CBN in transcriptomic datasets was quantified with a network perturbation amplitude algorithm. Results: The astrogliosis CBN model is composed of 879 nodes and 1687 edges. Highly connected nodes within the network included the S1PR family of Sphingosine 1‐phosphate receptors, extracellular matrix proteins, inflammatory regulator nuclear factor NF‐κB complex and the glial fibrillary acidic protein. Assessment of transcriptomic dataset GSE75246 in the context of the CBN showed increased astrogliosis in astrocytes from LPS‐treated mice compared with vehicle‐treated mouse astrocytes. Increased signaling of the toll‐like receptor 4, interferons and interleukin‐6 family cytokines and decreased acetylcholine esterase and histone deacetylase activity were inferred as key drivers of LPS‐induced astrogliosis in this dataset. Conclusion: The astrogliosis CBN is a novel tool to quantify astrogliosis in transcriptomic datasets and identify insult‐specific molecular drivers of the phenotype.