281 - Neuroinflammatory and Synaptic Dysregulation in Transcriptomic Analysis of Frontal Cortex Brain Tissues of Human Patients with Rett Syndrome

Publication Number: 4277.281

Marton Rohaly Medved, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada; Kazem Nejati-Koshki, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada; Shervin Pejhan, Western University, Ontario, London, ON, Canada; Liam Mitchell, University of Manitoba, Winnipeg, MB, Canada; Athanasios zovoilis, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada; Mojgan Rastegar, University of Manitoba, Winnipeg, MB, Canada

Background: Rett Syndrome (RTT) is a debilitating X-linked neurodevelopmental disorder characterized in part by microcephaly, loss of purposeful motor control, and seizures. 95% of RTT cases result from mutations in the MECP2 gene, however, the rarity of the disorder means few transcriptomic studies have been performed in human brain tissues. Therefore, the dysfunction of intermediary processes that result in symptoms remains unclear.

Objective: We performed RNA sequencing on frontal cortex samples from five female human patients with RTT and age- and sex-matched controls to identify the processes that are affected by MECP2 mutations.

Design/Methods: Gene Set Enrichment Analysis (GSEA) on the Human Phenotype Ontology (HPO) and Gene Ontology (GO) databases identified sets of genes with known functions (pathways) that are dysregulated in RTT samples. GSEA on the HPO database confirmed dysregulations of RTT relevant phenotypes, while the GO database identified affected biological process, molecular function, and cellular component pathways. Weighted Gene Co-Expression Network Analysis (WGCNA) was run as an alternative analysis to extract greater biological signal by identifying modules of highly correlated genes. Pathway analysis of these modules confirmed the GSEA results.

Results: Several RTT relevant seizure and motor control pathways were dysregulated at adjusted p-value < 0.001 in the HPO database. GO results showed numerous robust dysregulations of synaptic and immune response pathways at adjusted p-value < 1x10-6. Interrogation of the WGCNA modules confirmed the synaptic and immune dysregulations at adjusted p-value < 0.01.

Conclusion(s): Patients with RTT show fewer mature neurons, which may be associated with impaired synaptic function. Our study demonstrates the downregulation of synaptic pathways, thus supporting the theory that RTT phenotypes are neuronal in origin.

Although neuroinflammation in patients with RTT has not been well documented, Mecp2-null mice show unbalanced neuroimmune homeostasis (PMID: 34040112) and increased inflammatory activation in microglia (PMID: 32119978). The upregulation of immune pathways in our samples is suggestive of neuroimmune disruption in patients with RTT. However, further, ongoing proteomic and molecular validation in our lab will identify the specific interplay of synaptic and immune dysregulation to further expand our understanding of RTT neurobiology.

We appreciate the donation of the patient brain tissues and additional tissues for RTT and control samples from the NIH NeuroBioBank Program: neurobiobank.nih.gov, and CIHR Project Grant 202209PJT-486512-CIA-CDAA111824.

Figure 1. GSEA: Human Phenotype Ontology (HPO)
Consistent dysregulation of gene sets (pathways) associated with RTT relevant phenotypes including several seizure and motor control pathways. Results significant at adjusted p < 0.001.


Figure 2. GSEA: Gene Ontology (GO)
Consistent dysregulation of gene sets (pathways) associated with synapse types, synaptic functions, and both adaptive and innate immune response. Results significant at adjusted p < 1x10-6.


Figure 3. Dysregulated Gene Ontology (GO) Pathways in WGCNA Modules
(3ab) Network visualizations showing hub genes (red) and nonHub genes (grey) in the WGCNA modules. Edges between genes are red for hub-hub, black for hub-nonHub, and grey-dashed for nonHub-nonHub. (3c) Hypergeometric-based enrichment analysis showing pathways that are overrepresented in the up- and down-regulated modules. All results significant at adjusted p < 0.01.