B-Cell Targeted Therapeutics in Systemic Lupus Erythematosus: From Paradox to Synergy?

Biomimetic liposomes hybrid with erythrocyte membrane modulate dendritic cells to ameliorate systemic lupus erythematosus

Dendritic cells (DCs)-based immunotherapy has shown immense promise in systemic lupus erythematosus (SLE) treatment. However, existing carrier strategies such as polymers, liposomes, and polypeptides, are difficult to achieve active targeting to DCs due to their intricate interaction with biological systems. Since DCs represent a class of phagocytes responsible for the removal of senescent or damaged erythrocytes, we hypothesize that hybrid vesicles containing erythrocytes membrane components could be presented to be potent drug carriers to target DCs specifically. Herein, inspired by the cell membrane fusion technique, we develop hybrid biomimetic liposomes (R-Lipo) by fusing natural erythrocyte membrane vesicles and artificial liposomes for DCs-targeted SLE therapy. The resultant R-Lipo exhibited excellent biocompatibility and was shown to be effectively internalized by DCs both in vitro and in vivo. Using an immunosuppressant, mycophenolic acid (MPA), as the model drug, MPA-loaded R-Lipo powerfully suppressed DCs maturation and efficiently controlled the duration of lupus nephritis without apparent side effects. Our findings provide a safe, effective, and easy-to-prepare biomimetic vesicle platform for the treatment of SLE and other DC-associated diseases.

Accurate Machine Learning Model to Diagnose Chronic Autoimmune Diseases Utilizing Information From B Cells and Monocytes

Heterogeneity and limited comprehension of chronic autoimmune disease pathophysiology cause accurate diagnosis a challenging process. With the increasing resources of single-cell sequencing data, a reasonable way could be found to address this issue. In our study, with the use of large-scale public single-cell RNA sequencing (scRNA-seq) data, analysis of dataset integration (3.1 × 10⁵ PBMCs from fifteen SLE patients and eight healthy donors) and cellular cross talking (3.8 × 10⁵ PBMCs from twenty-eight SLE patients and eight healthy donors) were performed to identify the most crucial information characterizing SLE. Our findings revealed that the interactions among the PBMC subpopulations of SLE patients may be weakened under the inflammatory microenvironment, which could result in abnormal emergences or variations in signaling patterns within PBMCs. In particular, the alterations of B cells and monocytes may be the most significant findings. Utilizing this powerful information, an efficient mathematical model of unbiased random forest machine learning was established to distinguish SLE patients from healthy donors via not only scRNA-seq data but also bulk RNA-seq data. Surprisingly, our mathematical model could also accurately identify patients with rheumatoid arthritis and multiple sclerosis, not just SLE, via bulk RNA-seq data (derived from 688 samples). Since the variations in PBMCs should predate the clinical manifestations of these diseases, our machine learning model may be feasible to develop into an efficient tool for accurate diagnosis of chronic autoimmune diseases.