Intestinal dysbiosis is implicated in Systemic Lupus Erythematosus (SLE). However, the evidence of gut microbiome changes in SLE is limited, and the association of changed gut microbiome with the activity of SLE, as well as its functional relevance with SLE still remains unknown. Here, we sequenced 16S rRNA amplicon on fecal samples from 40 SLE patients (19 active patients, 21 remissive patients), 20 disease controls (Rheumatoid Arthritis (RA) patients), and 22 healthy controls (HCs), and investigated the association of functional categories with taxonomic composition by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt). We demonstrated SLE patients, particularly the active patients, had significant dysbiosis in gut microbiota with reduced bacterial diversity and biased community constitutions. Amongst the disordered microbiota, the genera Streptococcus, Campylobacter, Veillonella, the species anginosus and dispar, were positively correlated with lupus activity, while the genus Bifidobacterium was negatively associated with the disease activity. PICRUSt analysis showed metabolic pathways were different between SLE and HCs, and also between active and remissive SLE patients. Moreover, we revealed that a random forest model could distinguish SLE from RA and HCs (area under the curve (AUC) = 0.792), and another random forest model could well predict the activity of SLE patients (AUC = 0.811). In summary, SLE patients, especially the active patients, show an apparent dysbiosis in gut microbiota and its related metabolic pathways. Amongst the disordered microflora, four genera and two species are associated with lupus activity. Furthermore, the random forest models are able to diagnose SLE and predict disease activity.

Systemic lupus erythematosus (SLE) is a common autoimmune disease, which features the secretion of antibodies directed against autoantigens in vivo. In the present study, a peptide microarray was developed to detect the epitopes recognized by autoantibodies in patients with SLE for an effective method of diagnosis. SLE-associated epitopes in 14 autoantigens were predicted using the antigenic epitope prediction software DNA star. Peptides were synthesized based on the predicted antigenic epitopes and immobilized on a slide surface and developed into a peptide microarray. Using this peptide microarray the autoantibodies in 120 patients with SLE and 110 healthy subjects were detected. A total of 73 potential antigenic epitopes in 14 autoantigens were predicted and screened. The peptide microarray based on the 73 epitopes was used to detect the autoantibodies in patients with SLE. A total of 14 epitopes with potential diagnostic values were screened out. The sensitivity and specificity of the 14 epitopes for the diagnosis of SLE were 71.6 and 85.8%, respectively. An optimal set of epitopes for SLE diagnosis was obtained. As individual patients had a specific autoantibody spectrum it was possible to detect autoantibodies in SLE and perform the diagnosis of SLE using the peptide microarray.