Effects of Immunoglobulins G From Systemic Sclerosis Patients in Normal Dermal Fibroblasts: A Multi-Omics Study

Abstract

Autoantibodies (Aabs) are frequent in systemic sclerosis (SSc). Although recognized as potent biomarkers, their pathogenic role is debated. This study explored the effect of purified immunoglobulin G (IgG) from SSc patients on protein and mRNA expression of dermal fibroblasts (FBs) using an innovative multi-omics approach. Dermal FBs were cultured in the presence of sera or purified IgG from patients with diffuse cutaneous SSc (dcSSc), limited cutaneous SSc or healthy controls (HCs). The FB proteome and transcriptome were explored using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) and microarray assays, respectively. Proteomic analysis identified 3,310 proteins. SSc sera and purified IgG induced singular protein profile patterns. These FB proteome changes depended on the Aab serotype, with a singular effect observed with purified IgG from anti-topoisomerase-I autoantibody (ATA) positive patients compared to HC or other SSc serotypes. IgG from ATA positive SSc patients induced enrichment in proteins involved in focal adhesion, cadherin binding, cytosolic part, or lytic vacuole. Multi-omics analysis was performed in two ways: first by restricting the analysis of the transcriptomic data to differentially expressed proteins; and secondly, by performing a global statistical analysis integrating proteomics and transcriptomics. Transcriptomic analysis distinguished 764 differentially expressed genes and revealed that IgG from dcSSc can induce extracellular matrix (ECM) remodeling changes in gene expression profiles in FB. Global statistical analysis integrating proteomics and transcriptomics confirmed that IgG from SSc can induce ECM remodeling and activate FB profiles. This effect depended on the serotype of the patient, suggesting that SSc Aab might play a pathogenic role in some SSc subsets.

Keywords: autoantibodies; multi-omics analysis; proteomics; systemic sclerosis; transcriptomics.

Figure 1

Data analysis workflow. (A) We first performed principal component analysis on proteomics data and then hierarchical clustering results were visualized on heatmap, see Figure 2. (B) We explored effect of SSc sera and SSc IgG (all serotypes) by conducting differential analysis, see in Figure 3. (C) Transcriptomic analyses of FB cultured in the presence of IgG from dcSSc (ATA+ and ATA−) and HC were conducted by proteomics results, see in Figure 4. (D) We then focused on the singular group “purified IgG from ATA+ patients” in proteomics, see in Figure 5. (E) Finally, we performed a second way of multi-omics integration from whole proteomics and transcriptomics data, see in Figure 6.

Figure 2

Protein expression profiles in LC-MS/MS analysis. (A, B): two different views of 3D-PCA scatter plots for the analyzed cell samples. PCA highlighted FB protein expression according to the patient serotype of the purified IgG. (C) Heatmap representing the 3,310 differentially expressed proteins in all samples. Cluster analysis identified 5 different clusters of protein expression. [HC] [Sera], sera from healthy controls; [HC] [IgG], IgG from healthy controls; [SSc] [Sera] [ATA+], sera from dcSSc anti-topoisomerase-I positive patients; [SSc] [IgG] [ATA+], IgG from dcSSc anti-topoisomerase-I positive patients; [SSc] [Sera] [ATA−], sera from dcSSc anti-topoisomerase-I negative patients; [SSc] [IgG] [ATA−], IgG from dcSSc anti-topoisomerase-I negative patients; [SSc] [Sera] [ACA+], from sera lcSSc anti-centromere positive patients; [SSc] [IgG] [ACA+], IgG from lcSSc anti-centromere patients; [TGF-B], FB stimulated in the presence of TGF-β; [NS], non stimulated FB. PCA, principal component analysis.

Figure 3

SSc purified IgG induced ECM remodeling profile in FB at the protein level. (A) "volcano plot" representing the comparison [SSc] [IgG] vs [SSc] [Sera]; (B) Enriched clusters GO terms in the comparison [SSc] [IgG] vs [SSc] [Sera] according to upregulated proteins (overexpressed in [SSc] [sera]); (C) volcano plot representing the comparison [SSc] [IgG] vs [HC] [IgG]; (D) Enriched clusters GO terms in the comparison [SSc] [IgG] vs [HC] [Sera] according to upregulated proteins (overexpressed in [SSc] [IgG]); (E) Venn diagram representing commonly or exclusively overexpressed proteins in the comparison [SSc] [sera] vs [HC] [Sera]. Proteins are shown by their gene names. Proteins with adjusted p-value < 0.05 and Log Fold Change >1.4 or <−1.4 were considered for each comparison (volcano plot). Enriched clusters GO terms and network were obtained using Metascape. [SSc] [sera], sera from SSc; [SSc] [IgG], IgG from SSc; [HC] [IgG], IgG from heathy controls; [SSc] [IgG] [ATA+], IgG from dcSSc anti-topoisomerase-I positive patients; [SSc] [IgG] [ATA−], IgG from dcSSc anti-topoisomerase-I negative patients; [SSc] [IgG] [ACA+], IgG from lcSSc anti-centromere positive patients; GO terms, gene ontology terms.

Figure 4

Transcriptomics identified FB ECM remodeling changes induced by dcSSc-purified IgG. (A) PCA representing the 629 DNA probes (transcriptomics) dysregulated in proteomic analysis highlights genes expression by the FB according to SSc serotype. Purified IgG from ATA+ patients induced a singular gene expression profile compared to ATA− SSc patients and HC; (B) Heatmap representing genes dysregulated in the contrast [dcSSc] [IgG] vs [NS] with FC >1.5; (C) Heatmap representing top-ranked genes dysregulated with FC >1.5. FC, fold change; [dcSSc] [IgG], IgG from dcSSc (anti-topoisomerase-I positive and anti-topoisomerase-I negative patients); [SSc] [IgG] [ATA+], IgG from dcSSc anti-topoisomerase-I positive patients; [SSc] [IgG]; [ATA−], IgG from dcSSc anti-topoisomerase-I negative patients; [NS], non stimulated fibroblasts; [HC] [IgG], IgG from healthy controls.

Figure 5

Proteomic analysis revealed that dcSSc ATA+ purified IgG induced singular modifications in normal dermal FB. (A) volcano plot representing the comparison [SSc] [IgG] [ATA+] vs [HC] [IgG]; (B) volcano plot representing the comparison [SSc] [IgG] [ATA+] vs [SSc] [IgG] [ATA−]; (C) volcano plot representing the comparison [SSc] [IgG] [ATA+] vs [SSc] [IgG] [ACA+]; (D) Venn diagram representing commonly overexpressed proteins in [IgG] [ATA+] vs other groups. Proteins with adjusted p-value <0.05 and Log Fold Change >1.4 or <−1.4 were considered for each comparison (Volcanoplot). Enriched clusters GO terms and network were obtained using Metascape. [SSc] [sera], sera from SSc; [SSc] [IgG], IgG from SSc; [HC] [IgG], IgG from heathy controls; [SSc] [IgG] [ATA+], IgG from dcSSc anti-topoisomerase-I positive patients; [SSc] [IgG] [ATA−], IgG from dcSSc anti-topoisomerase-I negative patients; [SSc] [IgG] [ACA+], IgG from lcSSc anti-centromere positive patients.

Figure 6

Multi-omics data integration. (A) Individual plots representing patients’ projections into the 2D proteomics and transcriptomics space. X and Y-axes correspond to the first and second latent components of the DIABLO algorithm related to either proteomics (Block: Proteomics) or transcriptomics (Block: Transcriptomics) variables. (B) Circle plot representing variables with positive and negative correlation between proteomics and transcriptomics with r > 0.8. Only variables with r > 0.9 are tagged. (C) Heatmap representing clustering results of multi-omics signature identified by the DIABLO algorithm. [SSc] [IgG] [ATA+], IgG from dcSSc anti-topoisomerase-I positive patients; [SSc] [IgG] [ATA−]: IgG from dcSSc anti-topoisomerase-I negative patients; [SSc] [IgG] [ACA+], IgG from dcSSc anti-topoisomerase-I negative patients; [HC] [IgG], IgG from healthy controls.