Serum protein pattern associated with organ damage and lupus nephritis in systemic lupus erythematosus revealed by PEA immunoassay

Abstract

Background: Systemic lupus erythematosus (SLE) is a remarkably heterogeneous autoimmune disease. Despite tremendous efforts, our knowledge of serum protein patterns in severe SLE phenotypes is still limited. We investigated the serum protein pattern of SLE, with special emphasis on irreversible organ damage and active lupus nephritis (LN) as assessed by renal Systemic Lupus Erythematosus Disease Activity Index.

Methods: We used proximity extension immunoassay (PEA, Proseek Multiplex, Olink) to assess the serum levels of ninety-two inflammation-related proteins in Czech patients with SLE (n = 75) and age-matched healthy control subjects (n = 23). Subgroup analysis was carried out on the basis of organ damage (with/without, 42/33) and biopsy-proven LN (with/without, 27/48; active LN, n = 13; inactive LN, n = 14).

Results: Of thirty deregulated proteins between SLE and the healthy controls (P_corr < 0.05), the top upregulated proteins in SLE were sirtuin 2, interleukin 18 (IL18), and caspase 8 (P_corr < 0.0006). Of these, sirtuin 2 and caspase 8 had not yet been reported with SLE. Elevated levels of IL8, CCL2/MCP1, CCL11, and MMP10 (P_corr < 0.05) were detected in patients with organ damage for which the serum levels of CCL11 and MMP10 were particularly informative in organ damage prediction. Comparing patients based on LN, elevated levels of CSF1, sIL15RA, sCD40, sCX3CL1, caspase 8, sIL18R1, bNGF, and GDNF (P_corr < 0.05) were detected in active LN. Except GDNF, all LN-associated markers showed usefulness in prediction of active renal disease.

Conclusions: This highly sensitive PEA analysis identified the serum pattern of SLE, organ damage, and active LN, with many novel candidate proteins detected. Their exact role and suitability as biomarkers in SLE deserve further investigation.

Keywords: Lupus nephritis; Organ damage; Proximity extension immunoassay; Serum pattern; Systemic lupus erythematosus.

Fig. 1

Distribution of serum levels for top-deregulated proteins between healthy controls and SLE. Group means are indicated by horizontal bars, error bars indicate 95% CI; P _corr values after multiple corrections are stated

Fig. 2

Protein serum fingerprints associated with a SLE, b organ damage, and c active lupus nephritis (LN). Fingerprints are presented as FC (foldchange of group medians) of serum levels of all deregulated serum proteins between particular groups (P _corr < 0.05)

Fig. 3

Changes in protein levels for top-deregulated analytes between a SLE and controls, b patients with/without organ damage, and c patients with active lupus nephritis and without lupus nephritis (no LN). Changes are presented as percentage of changes between group medians of particular groups

Fig. 4

Distribution of serum levels of proteins distinguishing SLE patients with/without organ damage. Group means are indicated by horizontal bars, error bars indicate 95% CI; P _corr values for differences after multiple corrections are stated

Fig. 5

Distribution of serum levels of proteins distinguishing SLE patients without lupus nephritis (LN), with inactive lupus nephritis (inactive LN) and active lupus nephritis (active LN). Group means are indicated by horizontal bars, error bars indicate 95% CI

Fig. 6

Probability plots of serum analytes associated with organ damage in SLE patients. The grey curve represents a simulated model based on the individual patient serum levels and the black line represents overall trend calculated by the Bayesian statistical approach. The increasing overall trend the higher probability of organ damage. Higher serum levels of CCL11 and MMP10 correspond to higher probability of organ damage, lower serum levels of these analytes to lower probability of organ damage. IL8 and IL6 serum levels were not informative for organ damage prediction

Fig. 7

Probability plots of serum analytes associated with active lupus nephritis (LN) in SLE patients. The grey curve represents a simulated model based on the individual patient serum levels and the black line represents overall trend calculated by the Bayesian statistical approach. The increasing overall trend the higher probability of active LN. Higher serum levels correspond to higher probability of active LN, lower serum levels of these analytes to lower probability of active LN. The best predictive value was observed for CSF1, sIL15RA, sCD40, sCX3CL1, caspase 8 (CASP8), and sIL18R1