Oral Fc-Coupled Preproinsulin Achieves Systemic and Thymic Delivery Through the Neonatal Fc Receptor and Partially Delays Autoimmune Diabetes

Targeted delivery of oral vaccine antigens to aminopeptidase N protects pigs against pathogenic E. coli challenge infection

Oral subunit vaccines are an interesting alternative strategy to traditional live-attenuated or inactivated vaccines for conferring protection against gut pathogens. Despite being safer and more cost-effective, the development of oral subunit vaccines remains challenging due to barriers imposed by the gastrointestinal tract, such as digestive enzymes, a tolerogenic immune environment and the inability of larger proteins to cross the epithelial barrier. Recent advances have focused on overcoming these barriers by using potent mucosal adjuvants or pH-responsive delivery vehicles to protect antigens from degradation and promote their release in the intestinal lumen. A promising approach to allow vaccine antigens to pass the epithelial barrier is by their targeting towards aminopeptidase N (APN; CD13), an abundant membrane protein present on small intestinal enterocytes. APN is a peptidase involved in digestion, but also a receptor for several enteric pathogens. In addition, upon antibody-mediated crosslinking, APN facilitated the transport of antibody-antigen fusion constructs across the gut epithelium. This epithelial transport resulted in antigen-specific immune responses. Here, we present evidence that oral administration of APN-specific antibody-antigen fusion constructs comprising the porcine IgA Fc-domain and the FedF tipadhesin of F18-fimbriated E. coli elicited both mucosal and systemic immune responses and provided at least partial protection to piglets against a subsequent challenge infection with an F18-fimbriated STEC strain. Altogether, these findings will contribute to the further development of new oral subunit vaccines and provide a first proof-of-concept for the protective efficacy of APN-targeted vaccine antigens.

Maternal provisions in type 1 diabetes: Evidence for both protective & pathogenic potential

Maternal influences on the immune health and development of an infant begin in utero and continue well into the postnatal period, shaping and educating the child's maturing immune system. Two maternal provisions include early microbial colonizers to initiate microbiota establishment and the transfer of antibodies from mother to baby. Maternal antibodies are a result of a lifetime of antigenic experience, reflecting the infection history, health and environmental exposure of the mother. These same factors are strong influencers of the microbiota, inexorably linking the two. Together, these provisions help to educate the developing neonatal immune system and shape lymphocyte repertoires, establishing a role for external environmental influences even before birth. In the context of autoimmunity, the transfer of maternal autoantibodies has the potential to be harmful for the child, sometimes targeting tissues and cells with devastating consequences. Curiously, this does not seem to apply to maternal autoantibody transfer in type 1 diabetes (T1D). Moreover, despite the rising prevalence of the disease, little research has been conducted on the effects of maternal dysbiosis or antibody transfer from an affected mother to her offspring and thus their relevance to disease development in the offspring remains unclear. This review seeks to provide a thorough evaluation of the role of maternal microorganisms and antibodies within the context of T1D, exploring both their pathogenic and protective potential. Although a definitive understanding of their significance in infant T1D development remains elusive at present, we endeavor to present what has been learned with the goal of spurring further interest in this important and intriguing question.

Identification of two potential immune-related biomarkers of Graves' disease based on integrated bioinformatics analyses

BACKGROUND

Graves' disease (GD) is an autoimmune disease, the incidence of which is increasing yearly. GD requires long-life therapy. Therefore, the potential immune-related biomarkers of GD need to be studied.

METHOD

In our study, differentially expressed genes (DEGs) were derived from the online Gene Expression Omnibus (GEO) microarray expression dataset GSE71956. Protein‒protein interaction (PPI) network analyses were used to identify hub genes, which were validated by qPCR. GSEA was used to screen potential pathways and related immune cells. Next, CIBERSORT analysis was used to further explore the immune subtype distribution pattern among hub genes. ROC curves were used to analyze the specificity and sensitivity of hub genes.

RESULT

44 DEGs were screened from the GEO dataset. Two hub genes, EEF1A1 and EIF4B, were obtained from the PPI network and validated by qPCR (p < 0.05). GSEA was conducted to identify potential pathways and immune cells related to these the two hub genes. Immune cell subtype analysis revealed that hub genes had extensive associations with many different types of immune cells, particularly resting memory CD4⁺ T cells. AUCs of ROC analysis were 0.687 and 0.733 for EEF1A1 and EIF4B, respectively.

CONCLUSION

Our study revealed two hub genes, EEF1A1 and EIF4B, that are associated with resting memory CD4⁺ T cells and potential immune-related molecular biomarkers and therapeutic targets of GD.

Personalized Immunotherapies for Type 1 Diabetes: Who, What, When, and How?

Our understanding of the immunopathological features of type 1 diabetes (T1D) has greatly improved over the past two decades and has shed light on disease heterogeneity dictated by multiple immune, metabolic, and clinical parameters. This may explain the limited effects of immunotherapies tested so far to durably revert or prevent T1D, for which life-long insulin replacement remains the only therapeutic option. In the era of omics and precision medicine, offering personalized treatment could contribute to turning this tide. Here, we discuss how to structure the selection of the right patient at the right time for the right treatment. This individualized therapeutic approach involves enrolling patients at a defined disease stage depending on the target and mode of action of the selected drug, and better stratifying patients based on their T1D endotype, reflecting intrinsic disease aggressiveness and immune context. To this end, biomarker screening will be critical, not only to help stratify patients and disease stage, but also to select the best predicted responders ahead of treatment and at early time points during clinical trials. This strategy could contribute to increase therapeutic efficacy, notably through the selection of drugs with complementary effects, and to further develop precision multi-hit medicine.