Systemic lupus erythematosus (SLE) is a systemic autoimmune disease that affects thousands of people worldwide. Recently, alterations in metabolism and gut microbiome have emerged as key regulators of SLE pathogenesis. However, it is not clear about the coordination of gut commensal bacteria and SLE metabolism. Here, by integrating 16S sequencing and metabolomics data, we characterized the gut microbiome and fecal and serum metabolome alterations in patients with SLE. Microbial diversity sequencing revealed gut microflora dysbiosis in SLE patients with significantly increased beta diversity. The metabolomics profiling identified 43 and 55 significantly changed metabolites in serum and feces samples in SLE patients. Notably, lipids accounted for about 65% altered metabolites in serum, highlighted the disruption of lipid metabolism. Integrated correlation analysis provided a link between the gut microbiome and lipid metabolism in patients with SLE, particularly according to regulate the conversion of primary bile acids to secondary bile acids. Overall, our results illustrate the perturbation of the gut microbiome and metabolome in SLE patients which may facilitate the development of new SLE interventions.

Rheumatoid arthritis (RA) is an autoimmune disease described by joint destruction, synovitis and pannus formation. The gut microbiota acts as an environmental factor that plays an important role in RA, but little research regarding the etiopathogenic mechanisms of the microbiome in RA has been carried out. We used an integrated approach of 16S rRNA gene sequencing and ultrahigh-performance liquid chromatography-mass spectrometry-based metabolomics to analyze the structure and diversity of the intestinal flora and metabolites of the gut microbiota in RA patients compared with healthy subjects. In this study, α-diversity analysis of the gut microbiota showed that there was no significant difference between the healthy control (HC) and RA groups. However, β-diversity analysis showed that there was a significant difference between the two groups. Further analysis of alteration of the gut microbiota revealed that at the phylum level, the relative abundance of p_Bacteroidetes was significantly decreased in the RA group, while that of Verrucomicrobia and Proteobacteria was significantly increased in the RA group. At the genus level, Bacteroides, Faecalibacterium and some probiotics were decreased in the RA group, while 97 genera, including Lactobacillus, Streptococcus and Akkermansia, were increased in the RA group. Seventy-four differentially abundant metabolites were identified between the HC and RA groups, and we identified two potential biomarkers (9,12-octadecadiynoic acid and 10Z-nonadecenoic acid) in RA.

Introduction: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease for which there is no cure. Effective diagnosis and precise assessment of disease exacerbation remains a major challenge.Methods: We performed peripheral blood mononuclear cell (PBMC) proteomics of a discovery cohort, including patients with active SLE and inactive SLE, patients with rheumatoid arthritis (RA), and healthy controls (HC). Then, we performed a machine learning pipeline to identify biomarker combinations. The biomarker combinations were further validated using enzyme-linked immunosorbent assays (ELISAs) in another cohort. Single-cell RNA sequencing (scRNA-seq) data from active SLE, inactive SLE, and HC PBMC samples further elucidated the potential immune cellular sources of each of these PBMC biomarkers.Results: Screening of the PBMC proteome identified 1023, 168, and 124 proteins that were significantly different between SLE vs. HC, SLE vs. RA, and active SLE vs. inactive SLE, respectively. The machine learning pipeline identified two biomarker combinations that accurately distinguished patients with SLE from controls and discriminated between active and inactive SLE. The validated results of ELISAs for two biomarker combinations were in line with the discovery cohort results. Among them, the six-protein combination (IFIT3, MX1, TOMM40, STAT1, STAT2, and OAS3) exhibited good performance for SLE disease diagnosis, with AUC of 0.723 and 0.815 for distinguishing SLE from HC and RA, respectively. Nine-protein combination (PHACTR2, GOT2, L-selectin, CMC4, MAP2K1, CMPK2, ECPAS, SRA1, and STAT2) showed a robust performance in assessing disease exacerbation (AUC=0.990). Further, the potential immune cellular sources of nine PBMC biomarkers, which had the consistent changes with the proteomics data, were elucidated by PBMC scRNAseq.Discussion: Unbiased proteomic quantification and experimental validation of PBMC samples from two cohorts of patients with SLE were identified as biomarker combinations for diagnosis and activity monitoring. Furthermore, the immune cell subtype origin of the biomarkers in the transcript expression level was determined using PBMC scRNAseq. These findings present valuable PBMC biomarkers associated with SLE and may reveal potential therapeutic targets.

Protein post‐translational modifications (PTMs) of histones are ubiquitous regulatory mechanisms involved in many biological processes, including replication, transcription, DNA damage repair and ontogenesis. Recently, many short‐chain acylation histone modifications have been identified by mass spectrometry (MS). Lysine succinylation (Ksuc or Ksucc) is a newly identified histone PTM that changes the chemical environment of histones and is similar to other acylation modifications; lysine succinylation appears to accumulate at transcriptional start sites and to correlate with gene expression. Although numerous studies are ongoing, there is a lack of reviews on the Ksuc of histones. Here, we review lysine succinylation sites on histones, including the chemical characteristics and the mechanism by which lysine succinylation influences nucleosomal structure, chromatin dynamics and several diseases and then discuss lysine succinylation regulation to identify theoretical and experimental proof of Ksuc on histones and in diseases to inspire further research into histone lysine succinylation as a target of disease treatment in the future.

Primary Sjögren’s syndrome (pSS) is a chronic autoimmune disease that is estimated to affect 35 million people worldwide and is characterized by lymphocytic infiltration, elevated circulating autoantibodies, and proinflammatory cytokines. The key immune cell subset changes and the TCR/BCR repertoire alterations in pSS patients remain unclear. In this study, we sought to comprehensively characterize the transcriptional changes in PBMCs of pSS patients by single-cell RNA sequencing and single-cell V(D)J sequencing. Naive CD8⁺ T cells and mucosal-associated invariant T cells were markedly decreased but regulatory T cells were increased in pSS patients. There were a large number of differentially expressed genes shared by multiple subpopulations of T cells and B cells. Abnormal signaling pathways, including Ag processing and presentation, the BCR signaling pathway, the TCR signaling pathway, and Epstein–Barr virus infection, were highly enriched in pSS patients. Moreover, there were obvious differences in the CD30, FLT3, IFN-II, IL-1, IL-2, IL-6, IL-10, RESISTIN, TGF-β, TNF, and VEGF signaling networks between pSS patients and healthy controls. Single-cell TCR and BCR repertoire analysis showed that there was a lower diversity of T cells in pSS patients than in healthy controls; however, there was no significant difference in the degree of clonal expansion, CDR3 length distribution, or degree of sequence sharing. Notably, our results further emphasize the functional importance of αβ pairing in determining Ag specificity. In conclusion, our analysis provides a comprehensive single-cell map of gene expression and TCR/BCR profiles in pSS patients for a better understanding of the pathogenesis, diagnosis, and treatment of pSS. Key Points The proportions of immune cells and autoimmune and virus-related genes change in pSS. Cell communication of TGF-β and CD30 among immune cells was abnormal in pSS patients. Single-cell TCR and BCR repertoires do not change much in pSS patients.

The immune cells and the repertoire of T cells and B cells play an important role in the pathogenesis of systemic lupus erythematosus (SLE). Exploring their expression and distribution in SLE can help us better understand this lethal autoimmune disease. In this study, we used a single-cell 5’ RNA sequence and single-cell T cell receptor (TCR)/B cell receptor (BCR) to study the immune cells and the repertoire from ten SLE patients and the paired normal controls (NC). The results showed that 9732 cells correspondence to 12 cluster immune cell types were identified in NC, whereas 11042 cells correspondence to 16 cluster immune cell types were identified in SLE. The results demonstrated that neutrophil, macrophage, and dendritic cells were accumulated in SLE by annotating the immune cell types. Besides, the bioinformatics analysis of differentially expressed genes (DEGs) in these cell types indicates their role in inflammation response. In addition, patients with SLE showed increased TCR and BCR clonotypes compared with the healthy controls. Furthermore, patients with SLE showed biased usage of TCR and BCR V(D)J genes. Taken together, we characterized the transcriptome and TCR/BCR immune repertoire profiles of SLE patients, which may provide a new avenue for the diagnosis and treatment of SLE.