Exploration of regions of Coordinated Gene Co-Expression in patients with Systemic Lupus Erythematosus under Belimumab treatment
In this study, we investigated Domains of Coordinated Expression (DCEs) using whole blood RNAseq dataset (Moysidou et al., 2024) of 58 SLE patients undergoing Belimumab treatment, assessed at three time points: baseline, 1 month and 6 months. Patients were classified as responders or nonresponders based on clinical measures of disease activity. With that being said, we applied the same pipeline as described by Ntasis et al. (2020) for DCE detection.
Briefly, chromosomes were divided into 10 kb bins, and mean gene expression per bin was calculated for responders and non-responders. Spearman correlations between bins were computed, and a sliding-window approach was used to assign a bin signal reflecting local co-expression. Domains of Coordinated Expression (DCEs) were defined as consecutive bins with above-average correlations, separated by statistically significant boundaries, highlighting genomic regions with tightly coordinated gene expression.Furthermore,we examined differences in expression patterns between responders and non-responders at each time point, both at the transcriptomic level and within these coordinated expression regions.
DCE analysis revealed no significant quantitative differences in size or number across responders and non-responders at all tree time points, but notable qualitative changes, including boundary shifts and extensive reshuffling. A marked drop in genome coverage at time point 1 suggests a transitional phase of co-expression reorganization. Non-responders showed stronger DCE co-expressions, indicating greater structural stability, while responders exhibited more dynamic changes, particularly at time point 6, with increased DCE emergence and deletion. These findings highlight distinct patterns of genomic reorganization linked to treatment response.
🔹 Streamlined data preparation and cleaning preprocessing.
🔹 Differential Expression Analysis (DEA) and Enrichment Analysis (GSEA and KEGG Enrichment Analysis) between responders vs. non-responders at each time point.
🔹 Weighted Gene Co-expression Network Analysis (WGCNA) using the whole dataset (responders and non-responders at all time points).
🔹 Construction of Gene Co-expression matrix using genomic topology for all groups, and robust statistical evaluation via Permutation correlation test (19,999 permutations).
🔹 Detection of Domains of Coordinated Expression of all groups.
🔹 Analysis of Domains of Coordinated Expression of all groups.