Au18(L-NIBC)14 targets ASC oligomerization for autoimmune disease treatment
Gold nanoclusters (AuNCs), unlike conventional nanoparticles, possess molecular characteristics besides ultrasmall nano-features. Recently, we and others showed that AuNCs are promising in treatments of various major diseases. However, the AuNCs used were usually mixtures, and the specific target and their relationship with AuNC structures are unclear, which largely restrict their druggability. Multiple sclerosis (MS) is an autoimmune disease (AID) implicating central nervous system (CNS), in which drug discovery is challenging. Here we used Au18(L-N-isobutyryl-L-cysteine)14 (Au18(L-NIBC)14) and Au25(L-NIBC)18, two AuNCs with the same ligand, to report their much different therapeutic effects in experimental autoimmune encephalomyelitis (EAE). We show that, Au18(L-NIBC)14, but not Au25(L-NIBC)18, specifically targets apoptosis_x0002_associated speck-like protein containing a CARD (ASC) oligomerization, thus inhibit the activation of ASC-dependent inflammasomes, resulting in comprehensive restoration of cytokine homeostasis in the CNS of EAE mice. Au18(L-NIBC)14 significantly prevents axon demyelination, protects blood-brain barrier, blocks immune cell infiltration into CNS, and completely prevents motor deficits and relieve the early-cognitive impairments of EAE mice. Remarkable efficacies were also observed in animal models of inflammatory bowel disease, psoriasis, systemic lupus erythematosus, indicating a broad prospect in AIDs treatments. Especially, definite molecular structure, specific target, clear mechanism, and exact therapeutic effects imply a good druggability of Au18(L-NIBC)14. Overall design: Cortical tissues were collected, and total RNA was extracted for sequencing. Quality control was performed using FastQC, followed by adapter trimming and low-quality read removal with Fastp. The cleaned reads were aligned to the reference genome using Hisat2, and gene expression levels were quantified with bowtie2. Differentially expressed genes (DEGs) were identified using DESeq2, applying a threshold of |log2FC| > 1 and false discovery rate (FDR) < 0.05. A Venn diagram was generated to visualize overlapping and unique DEGs between the Naive vs.EAE and EAE vs.Au18(L-NIBC)14 comparisons. KEGG pathway enrichment analysis was performed, with key pathways such as the NOD-like receptor signaling pathway mapped via KEGG Mapper. The expression levels of selected genes were quantified using FPKM normalization, and statistical significance was assessed using student t-tests, with results visualized in a bar plot.