The neonatal Fc receptor expression during macrophage differentiation is related to autophagy

Albumin influences leucocyte FcRn expression in the early days of kidney transplantation

FcRn, a receptor originally known for its involvement in IgG and albumin transcytosis and recycling, is also important in the establishment of the innate and adaptive immune response. Dysregulation of the immune response has been associated with variations in FcRn expression, as observed in cancer. Recently, a link between autophagy and FcRn expression has been demonstrated. Knowing that autophagy is strongly involved in the development of reperfusion injury in kidney transplantation and that albuminemia is transiently decreased in the first 2 weeks after transplantation, we investigated variations in FcRn expression after kidney transplantation. We monitored FcRn levels by flow cytometry in leukocytes from 25 renal transplant patients and considered parameters such as albumin concentrations, estimated glomerular filtration rate, serum creatinine, serum IgG levels, and ischaemia/reperfusion time. Two groups of patients could be distinguished according to their increased or non-increased FcRn expression levels between days 2 and 6 (d2-d6) post-transplantation. Leukocyte FcRn expression at d2-d6 was correlated with albumin concentrations at d0-d2. These results suggest that albumin concentrations at d0-d2 influence FcRn expression at d2-d6, raising new questions about the mechanisms underlying these original observations.

Differential effects of FcRn antagonists on the subcellular trafficking of FcRn and albumin

The homeostasis of IgG is maintained by the neonatal Fc receptor, FcRn. Consequently, antagonism of FcRn to reduce endogenous IgG levels is an emerging strategy for treating antibody-mediated autoimmune disorders using either FcRn-specific antibodies or an engineered Fc fragment. For certain FcRn-specific antibodies, this approach has resulted in reductions in the levels of serum albumin, the other major ligand transported by FcRn. Cellular and molecular analyses of a panel of FcRn antagonists have been carried out to elucidate the mechanisms leading to their differential effects on albumin homeostasis. These analyses have identified 2 processes underlying decreases in albumin levels during FcRn blockade: increased degradation of FcRn and competition between antagonist and albumin for FcRn binding. These findings have potential implications for the design of drugs to modulate FcRn function.

Antibody Drug Clearance: An Underexplored Marker of Outcomes with Checkpoint Inhibitors

Immune-checkpoint inhibitor (ICI) therapy has dramatically changed the clinical landscape for several cancers, and ICI use continues to expand across many cancer types. Low baseline clearance (CL) and/or a large reduction of CL during treatment correlates with better clinical response and longer survival. Similar phenomena have also been reported with other monoclonal antibodies (mAb) in cancer and other diseases, highlighting a characteristic of mAb clinical pharmacology that is potentially shared among various mAbs and diseases. Though tempting to attribute poor outcomes to low drug exposure and arguably low target engagement due to high CL, such speculation is not supported by the relatively flat exposure-response relationship of most ICIs, where a higher dose or exposure is not likely to provide additional benefit. Instead, an elevated and/or increasing CL could be a surrogate marker of the inherent resistant phenotype that cannot be reversed by maximizing drug exposure. The mechanisms connecting ICI clearance, therapeutic efficacy, and resistance are unclear and likely to be multifactorial. Therefore, to explore the potential of ICI CL as an early marker for efficacy, this review highlights the similarities and differences of CL characteristics and CL-response relationships for all FDA-approved ICIs, and we compare and contrast these to selected non-ICI mAbs. We also discuss underlying mechanisms that potentially link mAb CL with efficacy and highlight existing knowledge gaps and future directions where more clinical and preclinical investigations are warranted to clearly understand the value of baseline and/or time-varying CL in predicting response to ICI-based therapeutics.

Bioinformatics identification of potential biomarkers and therapeutic targets for ischemic stroke and vascular dementia

Ischemic stroke and vascular dementia, as common cerebrovascular diseases, with the former causing irreversible neurological damage and the latter causing cognitive and memory impairment, are closely related and have long received widespread attention. Currently, the potential causative genes of these two diseases have yet to be investigated, and effective early diagnostic tools for the diseases have not yet emerged. In this study, we screened new potential biomarkers and analyzed new therapeutic targets for both diseases from the perspective of immune infiltration. Two gene expression profiles on ischemic stroke and vascular dementia were obtained from the NCBI GEO database, and key genes were identified by LASSO regression and SVM-RFE algorithms, and key genes were analyzed by GO and KEGG enrichment. The CIBERSORT algorithm was applied to the gene expression profile species of the two diseases to quantify the 24 subpopulations of immune cells. Moreover, logistic regression modeling analysis was applied to illustrate the stability of the key genes in the diagnosis. Finally, the key genes were validated using RT-PCR assay. A total of 105 intersecting DEGs genes were obtained in the 2 sets of GEO datasets, and bioinformatics functional analysis of the intersecting DEGs genes showed that GO was mainly involved in the purine ribonucleoside triphosphate metabolic process，respiratory chain complex，DNA-binding transcription factor binding and active transmembrane transporter activity. KEGG is mainly involved in the Oxidative phosphorylation, cAMP signaling pathway. The LASSO regression algorithm and SVM-RFE algorithm finally obtained three genes, GAS2L1, ARHGEF40 and PFKFB3, and the logistic regression prediction model determined that the three genes, GAS2L1 (AUC: 0.882), ARHGEF40 (AUC: 0.867) and PFKFB3 (AUC: 0.869), had good diagnostic performance. Meanwhile, the two disease core genes and immune infiltration were closely related, GAS2L1 and PFKFB3 had the highest positive correlation with macrophage M1 (p < 0.001) and the highest negative correlation with mast cell activation (p = 0.0017); ARHGEF40 had the highest positive correlation with macrophage M1 and B cells naive (p < 0.001), the highest negative correlation with B cell memory highest correlation (p = 0.0047). RT-PCR results showed that the relative mRNA expression levels of GAS2L1, ARHGEF40, and PFKFB3 were significantly elevated in the populations of both disease groups (p < 0.05). Immune infiltration-based models can be used to predict the diagnosis of patients with ischemic stroke and vascular dementia and provide a new perspective on the early diagnosis and treatment of both diseases.