The beneficial effects of probiotics have been described in many diseases, but the mechanism by which they modulate the immune system is poorly understood. In this study, we identified a mixture of probiotics that up-regulates CD4 ⁺ Foxp3 ⁺ regulatory T cells (Tregs). Administration of the probiotics mixture induced both T-cell and B-cell hyporesponsiveness and down-regulated T helper (Th) 1, Th2, and Th17 cytokines without apoptosis induction. It also induced generation of CD4 ⁺ Foxp3 ⁺ Tregs from the CD4 ⁺ CD25 ⁻ population and increased the suppressor activity of naturally occurring CD4 ⁺ CD25 ⁺ Tregs. Conversion of T cells into Foxp3 ⁺ Tregs is directly mediated by regulatory dendritic cells (rDCs) that express high levels of IL-10, TGF-β, COX-2, and indoleamine 2,3-dioxygenase. Administration of probiotics had therapeutical effects in experimental inflammatory bowel disease, atopic dermatitis, and rheumatoid arthritis. The therapeutical effect of the probiotics is associated with enrichment of CD4 ⁺ Foxp3 ⁺ Tregs in the inflamed regions. Collectively, the administration of probiotics that enhance the generation of rDCs and Tregs represents an applicable treatment of inflammatory immune disorders.

Immune checkpoint inhibitors (ICIs) have substantially improved the survival of cancer patients over the past several years. However, only a minority of patients respond to ICI treatment (~30% in solid tumors), and current ICI-response-associated biomarkers often fail to predict the ICI treatment response. Here, we present a machine learning (ML) framework that leverages network-based analyses to identify ICI treatment biomarkers (NetBio) that can make robust predictions. We curate more than 700 ICI-treated patient samples with clinical outcomes and transcriptomic data, and observe that NetBio-based predictions accurately predict ICI treatment responses in three different cancer types—melanoma, gastric cancer, and bladder cancer. Moreover, the NetBio-based prediction is superior to predictions based on other conventional ICI treatment biomarkers, such as ICI targets or tumor microenvironment-associated markers. This work presents a network-based method to effectively select immunotherapy-response-associated biomarkers that can make robust ML-based predictions for precision oncology.

Cell wall components of probiotic bacterium Bifidobacterium bifidum drive induction of regulatory T cells in the gut.

Considerable evidence points to the critical role of the gut microbiota in physiology and disease. The administration of live microbes as a therapeutic modality is increasingly being considered. However, key questions such as how to identify candidate microorganisms and which preclinical models are relevant to recapitulate human microbiota remain largely unanswered. The establishment of a humanized gnotobiotic mouse model through the fecal microbiota transplantation of human feces into germ-free mice provides an innovative and powerful tool to mimic the human microbial system. However, numerous considerations are required in designing such a model, as various elements, ranging from the factors pertaining to human donors to the mouse genetic background, affect how microbes colonize the gut. Thus, it is critical to match the murine context to that of human donors to provide a continuous and faithful progression of human flora in mice. This is of even greater importance when the need for accuracy and reproducibility across global research groups are taken into account. Here, we review the key factors that affect the formulation of a humanized mouse model representative of the human gut flora and propose several approaches as to how researchers can effectively design such models for clinical relevance.