Poster Session B
Fibrosing rheumatic diseases (scleroderma, MCTD, IgG4-related disease, scleroderma mimics)
Aurélien Chepy, MD (he/him/his)
INFINTE U186 inserm, univeristy of Lille
Lille, France
No financial relationships with ineligible companies to disclose
Proteomics identified and quantified 2,141 proteins. Principal component analysis (PCA) showed 3 distinct groups of subjects: a first including mostly ATA+ patients, a second including mostly ACA+ patients and third groups more heterogeneous including RNAP+, SLE patients and HC (Figure 1A). The comparison of HUVEC’s proteome in the presence of purified IgG from ATA+ patients vs HC and ACA+ patients vs HC revealed 614 and 288 differentially expressed proteins respectively (Figure 1B). Proteins overexpressed in ATA+ group were enriched in macromolecule localization and protein binding and overexpressed proteins in ACA+ groups were enriched in RNA and mRNA proteins binding.
Transcriptomic identified 16,802 mRNA. PCA revealed 2 groups of subjects: one homogeneous group composed only by 5 ATA positive patients (ATA group 1) and a second heterogeneous group composed by other ATA positive patients (ATA group 2), ACA, RNAP positive SSc patients, SLE and HC (Figure 2A). There were no differentially expressed genes (DEG) in the comparisons RNAP vs HC and SLE vs HC. The comparison of EC transcriptome in the presence of purified IgG from ATA+ patients vs. HC and ATA group 1 vs. HC revealed 2,425 and 7,639 DEG respectively (Figure 2B). DEG in ATA group 1 were enriched in regulation of cell cycle process and DNA replication.
Finally, the FA levels did not differ among the SSc ANA serogroups. Correlation with Partial least squares-discriminant analysis axes of Omics data suggested that FA did not impact the proteome but might influence EC transcriptome (Figure 3).
Conclusion: IgG from SSc patients influenced EC proteome and transcriptome profiles according to ANA status. FA were present in SSc patients but seemed to have a minimal impact on omics profiles. IgG from ATA+ patients induced EC singular and distinct profiles.
Protein expression profiles in LC-MS/MS analysis in presence of IgG from SSc patient
PCA represented HUVEC protein expression according to patients ANA status (A). Volcanoplot representing differential analysis between ATA vs HC (B) and ACA vs HC comparisons (C). ATA: purified IgG from systemic sclerosis anti-topoisomerase-I positive patients; ACA: purified IgG from systemic sclerosis anti-centromere positive patients; RNAP: purified IgG from systemic sclerosis RNA-polymerase III positive patients; SLE: purified IgG from systemic lupus erythematosus patients; HC: purified IgG from healthy controls; HUVEC: Human umbilical vein endothelial cells; PCA: principal component analysis.
mRNA expression profiles in RNA sequencing.
Principal component analysis represented HUVEC mRNA expression according to patients ANA status (A). Volcanoplot representing differential analysis between ATA 2 group vs HC (B) ATA: purified IgG from systemic sclerosis anti-topoisomerase-I positive patients; ACA: purified IgG from systemic sclerosis anti-centromere positive patients; RNAP: purified IgG from systemic sclerosis RNA-polymerase III positive patients; SLE: purified IgG from systemic lupus erythematosus patients; HC: purified IgG from healthy controls; HUVEC: Human umbilical vein endothelial cells; PCA: principal component analysis.
Contribution of functional antibodies.
Anti-AT1R and anti-ETAR antibodies levels among groups (A). PLDS-DA axes according to FA levels (proteomic). FA levels did not significantly correlate with PLS-DA axes in proteomic (B). PLDS-DA axes according to FA levels (transcriptomic) (C).
SSc: systemic sclerosis; ANA: antinuclear antibodies; EC: endothelial cells; ATA+: anti-topoisomerase-I positive patients; ACA+: anti-centromere positive patients; RNAP: anti-RNApolymerase-III positive patients, FA: functional antibodies, SLE: systemic lupus erythematosus patients; HC: healthy controls, AT1R: angiotensin II type 1 receptor; ETAR: endothelin-1 type A receptor, PLS-DA: Partial Least Squares-Discriminant Analysis.
A. Chepy: None; S. Vivier: None; A. Elhannani: None; F. Bray: None; C. Chauvet: None; M. Figeac: None; L. Guilbert: None; E. HACHULLA: None; C. Rolando: None; S. Dubucquoi: None; D. Launay: None; V. sobanski: None.