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Identification of Transcription Factors Differentially Expressed under Neuroinflammatory Conditions: Analysis of RNA-Seq Datasets Combined with an Unconventional Exploratory Approach.

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Neuroinflammation, the inflammatory process of the brain or peripheral nervous system, is mediated by various chemokines, cytokines, and other secondary messengers. Just like any other physiological process, transcription factors (TFs)… Click to show full abstract

Neuroinflammation, the inflammatory process of the brain or peripheral nervous system, is mediated by various chemokines, cytokines, and other secondary messengers. Just like any other physiological process, transcription factors (TFs) lie at the core of neuroinflammatory process too due to their direct effects on the control of gene expression. Although targeted studies are being done on some of the already known TFs involved in neuroinflammation, still the gap exists in profiling the whole repertoire from transcriptomics data which was the main aim of this study. Therefore, we retrieved RNA-sequencing (RNA-seq) datasets for lipopolysaccharide-treated mice brain tissues as well as three brain cell types - neurons, microglia, and astrocytes. The screening of differentially expressed genes resulted in identification of 15, 50, 98, and 29 TFs in brain, neurons, microglia, and astrocytes, respectively. Further exploration of the brain data with respect to the expression of identified TFs in normal tissues revealed interesting patterns of their expression along with the computational identification of the microRNAs (miRNAs) targeting the down-regulated TFs. Also, quite surprisingly, zf-C2H2 domain was found to be the most prevalent in all the TFs identified, i.e., brain tissue, neuronal, microglial, and astrocytic cells. Therefore, this study not only identified new TFs but also miRNA targets to explore in the process of neuroinflammation.

Keywords: seq datasets; rna seq; identification; brain; differentially expressed; transcription factors

Journal Title: Critical reviews in eukaryotic gene expression
Year Published: 2022

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