Deregulation in adult IgA vasculitis skin as the basis for the discovery of novel serum biomarkers

Abstract

Introduction: Immunoglobulin A vasculitis (IgAV) in adults has a variable disease course, with patients often developing gastrointestinal and renal involvement and thus contributing to higher mortality. Due to understudied molecular mechanisms in IgAV currently used biomarkers for IgAV visceral involvement are largely lacking. Our aim was to search for potential serum biomarkers based on the skin transcriptomic signature.

Methods: RNA sequencing analysis was conducted on skin biopsies collected from 6 treatment-naïve patients (3 skin only and 3 renal involvement) and 3 healthy controls (HC) to get insight into deregulated processes at the transcriptomic level. 15 analytes were selected and measured based on the transcriptome analysis (adiponectin, lipopolysaccharide binding protein (LBP), matrix metalloproteinase-1 (MMP1), C-C motif chemokine ligand (CCL) 19, kallikrein-5, CCL3, leptin, C-X-C motif chemokine ligand (CXCL) 5, osteopontin, interleukin (IL)-15, CXCL10, angiopoietin-like 4 (ANGPTL4), SERPIN A12/vaspin, IL-18 and fatty acid-binding protein 4 (FABP4)) in sera of 59 IgAV and 22 HC. Machine learning was used to assess the ability of the analytes to predict IgAV and its organ involvement.

Results: Based on the gene expression levels in the skin, we were able to differentiate between IgAV patients and HC using principal component analysis (PCA) and a sample-to-sample distance matrix. Differential expression analysis revealed 49 differentially expressed genes (DEGs) in all IgAV patient's vs. HC. Patients with renal involvement had more DEGs than patients with skin involvement only (507 vs. 46 DEGs) as compared to HC, suggesting different skin signatures. Major dysregulated processes in patients with renal involvement were lipid metabolism, acute inflammatory response, and extracellular matrix (ECM)-related processes. 11 of 15 analytes selected based on affected processes in IgAV skin (osteopontin, LBP, ANGPTL4, IL-15, FABP4, CCL19, kallikrein-5, CCL3, leptin, IL-18 and MMP1) were significantly higher (p-adj < 0.05) in IgAV serum as compared to HC. Prediction models utilizing measured analytes showed high potential for predicting adult IgAV.

Conclusion: Skin transcriptomic data revealed deregulations in lipid metabolism and acute inflammatory response, reflected also in serum analyte measurements. LBP, among others, could serve as a potential biomarker of renal complications, while adiponectin and CXCL10 could indicate gastrointestinal involvement.

Keywords: Acute inflammatory response; Adults; IgA vasculitis; Lipid metabolism; Machine learning; RNA sequencing; Serum biomarkers.

Fig. 1

PCA (a) and hierarchical clustering of subjects with heatmap showing subject-to-subject distances (b). PCA and subject clustering were performed with the gene expression count data, previously transformed with the regularized logarithm (as implemented in the DESeq2 rlog function). (a) PCA distinguished two patient groups from HC. Each point on the plot represents an individual sample, as HC are in red, IgAVN patients in green and sl-IgAV patients in blue. Samples that cluster closer together on the plot share more similar gene expression patterns. (b) Hierarchical clustering of subjects, utilizing the Euclidean distance and the complete-linkage agglomeration method, revealed distinct transcriptomes for IgAVN, sl-IgAV and HC. However, the subject sl-IgAV [1] was an exception, clustering with the HC subjects instead. Each row and column correspond to a different individual. The matrix depicts the degree of similarity or dissimilarity between pairs of samples based on their gene expression profiles. Darker colours or shorter distances indicate greater similarity, while brighter colours or longer distances represent increased dissimilarity. PCA, principal component analysis; IgAV, Immunoglobulin A vasculitis; HC, healthy controls; IgAVN, IgA with renal involvement; sl-IgAV, skin-limited IgAV.

Fig. 2

Over-Representation analysis (ORA) of enriched KEGG pathways, GO BP and CC in IgAV patients vs. HC, IgAVN vs. HC and sl-IgAV vs. HC performed on the gene sets derived from RNA sequencing data. Each bar on the graph represents a biological term, such as KEGG pathway, BP, or CC. Enriched terms are color-coded, with red colors indicating higher significance. The level of statistical significance is expressed with p-adj values. Hypergeometric test was employed to determine if the observed enrichment exceeded what might be expected by chance. The adjusted p value was computed using the Benjamini-Hochberg procedure, and a q value threshold of 0.05 was set to deem enriched terms as significant. The x-axis shows gene count. KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, Gene Ontology; BP, Biological Processes; CC, Cellular Components; IgAV, immunoglobulin A vasculitis; HC, healthy controls; IgAVN, IgAV with renal involvement; sl-IgAV, skin-limited IgAV.

Fig. 3

Classification of adult IgAV using Random Forest (a) Principal component analysis of serum analyte concentrations distinguished IgAV patients and HC. (b) ROC curve of Random forest algorithm with corresponding AUC (with 95% confidence interval). Red line presents ROC curve of a random classifier (AUC = 0.5). (C) First ten variables according to their relative importance for prediction of adult IgAV. IgAV, Immunoglobulin A vasculitis; HC, healthy controls; PC, principal component; ROC, Receiver operating characteristic; AUC, area under the curve; LBP, Lipopolysaccharide binding protein; ANGPTL4, angiopoietin-like 4; IL, interleukin; FABP4, fatty acid-binding protein 4; CCL, Chemokine (C-C motif) ligand

Fig. 4

Measured analytes distinguishing between sl-IgAV, IgAVN and IgAV_GI. (a) LBP concentration was significantly higher in IgAVN as compared to sl-IgAV patients as calculated using Mann Whitney U Test. (b) ROC curve of LBP for predicting renal involvement. (c) Serum levels of adiponectin and CXCL10 were significantly lower in all patients with GI involvement (IgAV_GI & IgAVN + GI) as compared to those without. (d) ROC curve of adiponectin and CXCL10 for predicting renal involvement. (e) Serum levels of CCL19, osteopontin and ANGPTL4 were significantly elevated in IgAV patients with necrotic skin lesions. P-values were calculated using Mann Whitney U Test. Data are expressed as medians (Q25-Q75) of each group. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001. PC, principal component; ROC, Receiver operating characteristic; IgAV, immunoglobulin A vasculitis; sl-IgAV, skin-limited IgAV; IgAVN, IgAV with renal involvement; IgAV_GI, IgA with gastrointestinal involvement (GI); IgAVN + GI, IgAV with GI and renal involvement; HC, healthy controls; LBP, Lipopolysaccharide binding protein; CXCL, C-X-C motif chemokine ligand; CCL, Chemokine (C-C motif) ligand; ANGPTL4, angiopoietin-like 4

Fig. 5

Associations of measured analytes with histopathological findings. (a) LBP serum level was significantly higher in patients with neutrophil infiltrates as compared to those with mixed infiltrates. (b) Patients with fibrinoid necrosis have increased serum FABP4 and ANGPTL4 levels as compared to those without. P-values were calculated using Mann Whitney U Test. Data are expressed as medians (Q25-Q75) of each group. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001. IgAV, Immunoglobulin A vasculitis; CCL, Chemokine (C-C motif) ligand; ANGPTL4, angiopoietin-like 4; LBP, Lipopolysaccharide binding protein; FABP4, fatty acid-binding protein 4; ANGPTL4, angiopoietin-like 4