Abstract
Astrocytes play a central role in the neuroimmune response by responding to CNS pathologies with diverse molecular and morphological changes during the process of reactive astrogliosis. Here, we used a computational biological network model and mathematical algorithms that allow the interpretation of high-throughput transcriptomic datasets in the context of known biology to study reactive astrogliosis. We gathered available mechanistic information from the literature into a comprehensive causal biological network (CBN) model of astrocyte reactivity. The CBN model was built in the Biological Expression Language, which is both human-readable and computable. We characterized the CBN with a network analysis of highly connected nodes and demonstrated that the CBN captures relevant astrocyte biology. Subsequently, we used the CBN and transcriptomic data to identify key molecular pathways driving the astrocyte phenotype in four CNS pathologies: samples from mouse models of lipopolysaccharide-induced endotoxemia, Alzheimer’s disease, and amyotrophic lateral sclerosis; and samples from multiple sclerosis patients. The astrocyte CBN provides a new tool to identify causal mechanisms and quantify astrogliosis based on transcriptomic data.
Highlights
Astrocytes play a central role in the neuroimmune response by responding to central nervous system (CNS) pathologies with diverse molecular and morphological changes during the process of reactive astrogliosis
Transcriptome-profiling studies identified an “A1” molecular signature of 12 genes that was associated with a neurotoxic astrocyte subtype that emerged after exposure to specific cytokines secreted by microglia exposed to lipopolysaccharide (LPS), an “A2” molecular signature of 12 genes that was associated with a neurotrophic subtype after ischemic stroke, and a core set of pan-reactive genes that were not pathology-specific[2,4,5]
We show how transcriptomic data from models of amyotrophic lateral sclerosis (ALS), neuroinflammation caused by systemic LPS-induced endotoxemia, Alzheimer’s disease (AD), and multiple sclerosis (MS) can be assessed with this network model to get a mechanistic understanding of gene expression changes
Summary
Astrocytes play a central role in the neuroimmune response by responding to CNS pathologies with diverse molecular and morphological changes during the process of reactive astrogliosis. The categories of non-immune genes that contribute to the neurotoxicity/neuroprotective effects are vast, including epigenetic modifiers[7], genes which encode toxic saturated lipids[8], and genes which influence astrocyte-microglia c ommunications[9] Given this broad heterogeneity, there is a need for a tool to distinguish the molecular mechanisms driving the astrocyte phenotype in different CNS pathologies. Signatures associated with a subset of nodes in the network and comparing the signatures of these nodes to pathology-induced changes in gene expression values to infer the activity of the node in a dataset This approach considers changes in the abundance of transcripts as a downstream response to node activity without assuming these transcripts produce active proteins. A single transcriptomic profile (instead of many measurements at different biological levels) can be used to determine the activity of the whole n etwork[11,14]
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