6 - MULTI-MODAL PROFILING OF KAWASAKI DISEASE AND IMMUNE DYNAMICS FOLLOWING INTRAVENOUS IMMUNOGLOBULIN

Background: KD is a medium-size vasculitis affecting young children and often leading to acquired heart disease Despite progresses, the molecular mechanisms underlying KD and IVIG ability to mitigate the inflammatory process remain incompletely understood.

Methods: In this prospective study, plasma proteomic profile, phenotype, and gene-expression of sorted T cell subsets along with full clinical characteristics, were investigated in KD patients and compared with age-sex-matched healthy and febrile controls (FC). Blood samples from KD patients and control groups were collected at three timepoints: in the acute phase (T0), 48 hours (T1), and 4 weeks after IVIG therapy (T2).

Plasma samples were analyzed for proteomics using Olink technology. PBMCs were isolated and analyzed by flow cytometry. After sorting Tregs, CD4+ Central memory (CM) and CD4+ Effector memory (EM) T cells were amplified and loaded on a Fluidigm 96.96 standard chip to assess gene expression.

Results: Proteomic analysis of 184 proteins from 43 KD children in the acute phase revealed distinctive inflammatory features (Fig.1A), involving mainly inflammatory Th-1 and Th-17 mediators, but also immune cell regulation proteins (Fig.1B) as highlighted by principal component analysis (Fig.1C). This approach unveiled a potential disease severity signature, as APBB1IP demonstrated a positive association with echocardiogram values [left anterior descending (LAD) coronary value expressed mm and LAD Z-SCORE; Fig 1D]. Furthermore, this protein was found to be elevated in CAI+ compared to CAI- patients (p=0.008, Fig.1E). Phenotype analysis of T cell compartment revealed a transient reduction in CD4+ EM T cells and CD4+ TEMRA T cells and a comprehensive immune activation and exhaustion with an increase of PD1+Treg cells respect controls (Fig 2A). To further characterize KD immunological profile, we investigated gene expression in sorted CD4+ EM, CD4+ CM and Treg cells. Differential analysis between KD and FC, revealed 17 differentially expressed genes, 11/17 within the EM subset and 6/17 within the CM T cells subset (Fig. 2B) suggesting that qualitative perturbation, identified by transcriptional analysis, coupled with quantitative T cell subsets analysis may define distinctive signatures of KD as compared to FC. Following IVIG, both quantitative (Fig 2C),  and gene expression analyses (Fig 2.D) revealed Tregs immune dynamics of recovery. Leveraging machine learning methods, we identified pivotal contributors distinguishing KD from control groups with an accuracy of 0.87 (Fig. 3).
Conclusions: By providing a comprehensive insight into KD's immunological and proteomic aspects, our data may offer valuable information on prospective biomarkers and possible targets for novel treatments