X9 - RelB, but Not NFκB1, Serves as a Specific Marker of Hydrocephalus

Introduction: : Chronic hydrocephalus (CH) is a neurological disorder characterized by cerebrospinal fluid (CSF) accumulation, leading to ventricular enlargement, increased intracranial pressure, and progressive neuronal dysfunction. While histopathological biomarkers like amyloid-beta and tau are well-defined in Alzheimer’s and Parkinson’s disease, CH lacks similarly established markers, limiting diagnostic and therapeutic advancements. Neuroinflammation, particularly periventricular gliosis and microgliosis, plays a central role in CH pathology, with reactive glial markers GFAP and Iba1 indicating astrocyte and microglial activation. However, bulk RNA sequencing may not effectively capture these localized cellular changes due to sample heterogeneity and transcriptomic dilution.

Recent studies highlight the role of NF-κB signaling in CH, particularly RelA (p65) activation, suggesting its involvement in disease progression. Ikk2 activation in astrocytes disrupts ependymal ciliogenesis, contributing to hydrocephalus. While classical NF-κB signaling (p50, p65, c-Rel) is implicated in CH, the role of non-canonical NF-κB member RelB remains unexplored. RelB, essential for immune modulation and CNS inflammatory responses, may contribute to hydrocephalus pathology, warranting further investigation.

Transcriptomic advancements offer insights into CH-associated genetic alterations, particularly in the caudate nucleus, a region vulnerable to oxidative stress, hypoxia, and neuroinflammation. Vascular remodeling in CH suggests hypoxia-driven pathology. Investigating ROS signaling, hypoxia pathways, and inflammatory mediators like TNF and NF-κB may reveal potential therapeutic targets.

This study employed whole transcriptome RNA sequencing of the caudate nucleus in CH patients and controls. Through principal component analysis and pathway enrichment, we characterized CH’s molecular landscape, uncovering novel pathways that may inform future treatment strategies.

Materials and

Methods: : Human Postmortem Tissues and RNA Analysis
Postmortem caudate nucleus tissues were obtained from the NIH NeuroBioBank (NBB) over one year. Samples included n=7 controls, n=7 chronic hydrocephalus (CH) cases, and n=5 Alzheimer’s disease (AD) cases, collected at an average postmortem interval of 16 ± 8 hours. Specimens were transported frozen, with demographic details provided in supplementary data.

Whole Transcriptome RNA-Seq and Data Sorting
RNA sequencing (RNA-Seq) was performed in two sessions, analyzing 62,704 genetic loci. In the first session (N=7), 3.4% of loci (n=2,144) were statistically significant (P < 0.05), while the second session (N=9) identified 10.8% significant loci (n=6,799). Genes related to hemoglobin, glucose, and lipid metabolism were highly ranked.

Primer Design and RNA Isolation
Primers were designed for six target genes and a housekeeping gene using Ensembl and Primer3. RNA was extracted from caudate specimens using the RNeasy Lipid Tissue Mini Kit (QIAGEN) and reverse-transcribed into cDNA (500 ng RNA) using the High-Capacity RNA-to-cDNA Kit (Thermo Fisher Scientific).

RT-PCR and Image Analysis
RT-PCR was performed with GoTaq Green Master Mix (Promega) under standard cycling conditions. PCR products were analyzed via 1.25% agarose gel electrophoresis and quantified using ImageJ, normalizing band intensities to housekeeping genes.

Statistical Analysis
Principal component analysis (PCA) and non-parametric tests (Mann-Whitney U and Kruskal-Wallis) were conducted in GraphPad Prism, with significance set at P< 0.05. Gene set enrichment analysis (GSEA) and hierarchical clustering were used to identify pathways linked to oxidative stress, hypoxia, and neuroinflammation, highlighting transcriptomic changes in CH.

Results, Conclusions, and Discussions:: Transcriptomic Analysis of Chronic Hydrocephalus (CH)
Genetic Profiles and Pathway Dysregulation in CH
Principal component analysis (PCA) of caudate nucleus specimens revealed clear separation between control and CH groups, with PC1 explaining 82.14% of variance. Genes such as CYTB and FKBP5 emerged as significant contributors, emphasizing distinct genetic profiles in CH. Hierarchical clustering further highlighted genetic segregation, suggesting unique disease-associated pathways.

Cluster analysis identified significant expression differences in multiple subgroups, underscoring gene expression heterogeneity in CH. Enrichment scores for reactive oxygen species (ROS) signaling pathways demonstrated fewer highly expressed candidates in CH, implicating oxidative stress in disease progression.

ROS and Hypoxia Pathways in CH Pathogenesis
Key ROS-related genes (GLRX, GCLM, NQO1, PFKP, SOD2) exhibited significant dysregulation, suggesting heightened oxidative stress in CH. Similarly, hypoxia-related genes displayed significant expression changes, emphasizing oxygen imbalance and perfusion deficits as contributing factors to CH pathogenesis.

Neuroinflammatory Responses in CH
Neuroinflammatory genes in the NFκB and TNF superfamilies (RelA, RelB, NFκB1, TNF, LTA, LTB, HBA1, HBA2) were significantly upregulated, indicating chronic inflammation as a key driver of CH pathology. Dysregulation of hemoglobin subunit genes further suggested altered oxygen transport and metabolic dysfunction in affected brain regions.

Implications and Future Directions
Findings highlight oxidative stress, hypoxia, and neuroinflammation as key contributors to CH. This study suggests potential therapeutic targets, including antioxidants and anti-inflammatory agents, for mitigating disease progression.

Limitations and Conclusion
While caudate nucleus analysis provides insights into CH, further research should explore other brain regions and longitudinal studies to establish causality. This study lays a foundation for targeted interventions addressing oxidative damage, inflammation, and impaired perfusion in CH.

Acknowledgements and/or References (Optional):: This research was made possible by the West Virginia Space Grant Consortium (WVSGC) NASA Research Initiation Grant, # 80NSSC20M0055 (to JWS) and the WVSGC NASA undergraduate research fellowship (to EB). We have used the Genomics Core supported by funding from the WV-INBRE grant (NIH P20GM103434), the COBRE ACCORD grant (P20GM121299) and the West Virginia Clinical and Translational Science Institute (WV-CTSI) grant (2U54GM104942).