Junhong Hou

Hyperuricemia (HUA) is a common immunometabolic disorder associated with gout, renal dysfunction, and systemic inflammation, yet the molecular targets through which non-starch polysaccharides (NSPs) may modulate HUA-related inflammation remain unclear. Here, we applied an integrative bioinformatics and computational workflow combining public transcriptomic datasets, curated NSP-related targets, protein–protein interaction analysis, enrichment analysis, single-cell RNA sequencing, and Mendelian randomization. We further included GutMGene-based orthogonal support analysis, guided docking, structural dynamics analysis, exploratory ADMET profiling, and in silico TLR4 knockout to extend target prioritization. This approach prioritized a TLR4-centered inflammatory module, with TLR4, MSR1, TIRAP, and CXCL8 emerging as candidate genes. Enrichment analyses linked these genes to innate immune and NF-$\kappa$B-related pathways, whereas single-cell analyses localized the prioritized signals mainly to myeloid compartments during gout flares. Mendelian randomization suggested positive associations between genetically predicted expression of TLR4-axis genes and serum uric acid levels. Under electrostatic-guided docking conditions, fucoidan and alginate yielded plausible interaction models with TLR4, and normal mode and RMSF analyses suggested altered flexibility in the MD-2 region. In silico Tlr4 knockout further perturbed urate-handling programs in renal proximal tubule-enriched cells. Together, these findings do not establish TLR4 as a newly discovered hyperuricemia gene or confirm direct receptor antagonism by NSPs, but they provide an NSP-oriented integrative framework that prioritizes the TLR4 axis, highlights myeloid-cell relevance, and nominates fucoidan and alginate for experimental follow-up.