Treg-targeted efficient-inducible platform for collagen-induced arthritis treatment

CRISPR-GEM: A Novel Machine Learning Model for CRISPR Genetic Target Discovery and Evaluation

CRISPR gene editing strategies are shaping cell therapies through precise and tunable control over gene expression. However, limitations in safely delivering high quantities of CRISPR machinery demand careful target gene selection to achieve reliable therapeutic effects. Informed target gene selection requires a thorough understanding of the involvement of target genes in gene regulatory networks (GRNs) and thus their impact on cell phenotype. Effective decoding of these complex networks has been achieved using machine learning models, but current techniques are limited to single cell types and focus mainly on transcription factors, limiting their applicability to CRISPR strategies. To address this, we present CRISPR-GEM, a multilayer perceptron (MLP) based synthetic GRN constructed to accurately predict the downstream effects of CRISPR gene editing. First, input and output nodes are identified as differentially expressed genes between defined experimental and target cell/tissue types, respectively. Then, MLP training learns regulatory relationships in a black-box approach allowing accurate prediction of output gene expression using only input gene expression. Finally, CRISPR-mimetic perturbations are made to each input gene individually, and the resulting model predictions are compared to those for the target group to score and assess each input gene as a CRISPR candidate. The top scoring genes provided by CRISPR-GEM therefore best modulate experimental group GRNs to motivate transcriptomic shifts toward a target group phenotype. This machine learning model is the first of its kind for predicting optimal CRISPR target genes and serves as a powerful tool for enhanced CRISPR strategies across a range of cell therapies.

CRISPR-GEM: A Novel Machine Learning Model for CRISPR Genetic Target Discovery and Evaluation

CRISPR gene editing strategies are shaping cell therapies through precise and tunable control over gene expression. However, achieving reliable therapeutic effects with improved safety and efficacy requires informed target gene selection. This depends on a thorough understanding of the involvement of target genes in gene regulatory networks (GRNs) that regulate cell phenotype and function. Machine learning models have been previously used for GRN reconstruction using RNA-seq data, but current techniques are limited to single cell types and focus mainly on transcription factors. This restriction overlooks many potential CRISPR target genes, such as those encoding extracellular matrix components, growth factors, and signaling molecules, thus limiting the applicability of these models for CRISPR strategies. To address these limitations, we have developed CRISPR-GEM, a multi-layer perceptron (MLP)-based synthetic GRN constructed to accurately predict the downstream effects of CRISPR gene editing. First, input and output nodes are identified as differentially expressed genes between defined experimental and target cell/tissue types respectively. Then, MLP training learns regulatory relationships in a black-box approach allowing accurate prediction of output gene expression using only input gene expression. Finally, CRISPR-mimetic perturbations are made to each input gene individually and the resulting model predictions are compared to those for the target group to score and assess each input gene as a CRISPR candidate. The top scoring genes provided by CRISPR-GEM therefore best modulate experimental group GRNs to motivate transcriptomic shifts towards a target group phenotype. This machine learning model is the first of its kind for predicting optimal CRISPR target genes and serves as a powerful tool for enhanced CRISPR strategies across a range of cell therapies.

Advanced application of carbohydrate-based micro/nanoparticles for rheumatoid arthritis

Rheumatoid arthritis (RA) is a kind of synovitis and progressive joint destruction disease. Dysregulated immune cell activation, inflammatory cytokine overproduction, and subsequent reactive oxidative species (ROS) production contribute to the RA process. Carbohydrates, including cellulose, chitosan, alginate and dextran, are among the most abundant and important biomolecules in nature and are widely used in biomedicine. Carbohydrate-based micro/nanoparticles(M/NPs) as functional excipients have the ability to improve the bioavailability, solubility and stability of numerous drugs used in RA therapy. For on-demand therapy, smart reactive M/NPs have been developed to respond to a variety of chemical and physical stimuli, including light, temperature, enzymes, pH and ROS, alternating their physical and macroscopic properties, resulting in innovative new drug delivery systems. In particular, advanced products with targeted dextran or hyaluronic acid are exploiting multiple beneficial properties at the same time. In addition to those that respond, there are promising new derivatives in development with microenvironment and chronotherapy effects. In this review, we provide an overview of these recent developments and an outlook on how this class of agents will further shape the landscape of drug delivery for RA treatment.

Tailoring Biomaterials Ameliorate Inflammatory Bone Loss

Inflammatory diseases, such as rheumatoid arthritis, periodontitis, chronic obstructive pulmonary disease, and celiac disease, disrupt the delicate balance between bone resorption and formation, leading to inflammatory bone loss. Conventional approaches to tackle this issue encompass pharmaceutical interventions and surgical procedures. Nevertheless, pharmaceutical interventions exhibit limited efficacy, while surgical treatments impose trauma and significant financial burden upon patients. Biomaterials show outstanding spatiotemporal controllability, possess a remarkable specific surface area, and demonstrate exceptional reactivity. In the present era, the advancement of emerging biomaterials has bestowed upon more efficacious solutions for combatting the detrimental consequences of inflammatory bone loss. In this review, the advances of biomaterials for ameliorating inflammatory bone loss are listed. Additionally, the advantages and disadvantages of various biomaterials-mediated strategies are summarized. Finally, the challenges and perspectives of biomaterials are analyzed. This review aims to provide new possibilities for developing more advanced biomaterials toward inflammatory bone loss.

Immunosuppressive microvesicles-mimetic derived from tolerant dendritic cells to target T-lymphocytes for inflammation diseases therapy

The utilization of extracellular vesicles (EV) in immunotherapy, aiming at suppressing peripheral immune cells responsible for inflammation, has demonstrated significant efficacy in treating various inflammatory diseases. However, the clinical application of EV has faced challenges due to their inadequate targeting ability. In addition, most of the circulating EV would be cleared by the liver, resulting in a short biological half-life after systemic administration. Inspired by the natural microvesicles (MV, as a subset of large size EV) are originated and shed from the plasma membrane, we developed the immunosuppressive MV-mimetic (MVM) from endotoxin tolerant dendritic cells (DC) by a straightforward and effective extrusion approach, in which DC surface proteins were inherited for providing the homing ability to the spleen, while αCD3 antibodies were conjugated to the MVM membranes for specific targeting of T cells. The engineered MVM carried a large number of bioactive cargos from the parental cells, which exhibited a remarkable ability to promote the induction of regulatory T cells (Treg) and polarization of anti-inflammatory M2 macrophages. Mechanistically, the elevated Treg level by MVM was mediated due to the upregulation of miR-155-3p. Furthermore, it was observed that systemic and local immunosuppression was induced by MVM in models of sepsis and rheumatoid arthritis through the improvement of Treg and M2 macrophages. These findings reveal a promising cell-free strategy for managing inflammatory responses to infections or tissue injury, thereby maintaining immune homeostasis.

Augmenting regulatory T cells: new therapeutic strategy for rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic, systemic autoimmune condition marked by inflammation of the joints, degradation of the articular cartilage, and bone resorption. Recent studies found the absolute and relative decreases in circulating regulatory T cells (Tregs) in RA patients. Tregs are a unique type of cells exhibiting immunosuppressive functions, known for expressing the Foxp3 gene. They are instrumental in maintaining immunological tolerance and preventing autoimmunity. Increasing the absolute number and/or enhancing the function of Tregs are effective strategies for treating RA. This article reviews the studies on the mechanisms and targeted therapies related to Tregs in RA, with a view to provide better ideas for the treatment of RA.

The Role of NKG2D and Its Ligands in Autoimmune Diseases: New Targets for Immunotherapy

Natural killer (NK) cells and CD8+ T cells can clear infected and transformed cells and generate tolerance to themselves, which also prevents autoimmune diseases. Natural killer group 2 member D (NKG2D) is an important activating immune receptor that is expressed on NK cells, CD8+ T cells, γδ T cells, and a very small percentage of CD4+ T cells. In contrast, the NKG2D ligand (NKG2D-L) is generally not expressed on normal cells but is overexpressed under stress. Thus, the inappropriate expression of NKG2D-L leads to the activation of self-reactive effector cells, which can trigger or exacerbate autoimmunity. In this review, we discuss the role of NKG2D and NKG2D-L in systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), multiple sclerosis (MS), type I diabetes (T1DM), inflammatory bowel disease (IBD), and celiac disease (CeD). The data suggest that NKG2D and NKG2D-L play a pathogenic role in some autoimmune diseases. Therefore, the development of strategies to block the interaction of NKG2D and NKG2D-L may have therapeutic effects in some autoimmune diseases.