The thoracic and peritoneal cavities are lined by serous membranes and are home of the serosal immune system. This immune system fuses innate and adaptive immunity, to maintain local homeostasis and repair local tissue damage, and to cooperate closely with the mucosal immune system. Innate lymphoid cells (ILCs) are found abundantly in the thoracic and peritoneal cavities, and they are crucial in first defense against pathogenic viruses and bacteria. Nanomaterials (NMs) can enter the cavities intentionally for medical purposes, or unintentionally following environmental exposure; subsequent serosal inflammation and cancer (mesothelioma) has gained significant interest. However, reports on adverse effects of NMon ILCs and other components of the serosal immune systemare scarce or even lacking. As ILCs are crucial in the first defense against pathogenic viruses and bacteria, it is possible that serosal exposure to NMmay lead to a reduced resistance against pathogens. Additionally, affected serosal lymphoid tissues and cells may disturb adipose tissue homeostasis. This review aims to provide insight into key effects of NMon the serosal immune system.
BACKGROUND: Prednisolone and other glucocorticoids (GCs) are potent anti-inflammatory and immunosuppressive drugs. However, prolonged use at a medium or high dose is hampered by side effects of which the metabolic side effects are most evident. Relatively little is known about their effect on gene-expression in vivo, the effect on cell subpopulations and the relation to the efficacy and side effects of GCs.AIM: To identify and compare prednisolone-induced gene signatures in CD4⁺ T lymphocytes and CD14⁺ monocytes derived from healthy volunteers and to link these signatures to underlying biological pathways involved in metabolic adverse effects.MATERIALS & METHODS: Whole-genome expression profiling was performed on CD4⁺ T lymphocytes and CD14⁺ monocytes derived from healthy volunteers treated with prednisolone. Text-mining analyses was used to link genes to pathways involved in metabolic adverse events.RESULTS: Induction of gene-expression was much stronger in CD4⁺ T lymphocytes than in CD14⁺ monocytes with respect to fold changes, but the number of truly cell-specific genes where a strong prednisolone effect in one cell type was accompanied by a total lack of prednisolone effect in the other cell type, was relatively low. Subsequently, a large set of genes was identified with a strong link to metabolic processes, for some of which the association with GCs is novel.CONCLUSION: The identified gene signatures provide new starting points for further study into GC-induced transcriptional regulation in vivo and the mechanisms underlying GC-mediated metabolic side effects.
BACKGROUND: T lymphocytes are orchestrators of adaptive immunity. Naïve T cells may differentiate into Th1, Th2, Th17 or iTreg phenotypes, depending on environmental co-stimulatory signals. To identify genes and pathways involved in differentiation of Jurkat T cells towards Th1 and Th2 subtypes we performed comprehensive transcriptome analyses of Jurkat T cells stimulated with various stimuli and pathway inhibitors. Results from these experiments were validated in a human experimental setting using whole blood and purified CD4+ Tcells.RESULTS: Calcium-dependent activation of T cells using CD3/CD28 and PMA/CD3 stimulation induced a Th1 expression profile reflected by increased expression of T-bet, RUNX3, IL-2, and IFNγ, whereas calcium-independent activation via PMA/CD28 induced a Th2 expression profile which included GATA3, RXRA, CCL1 and Itk. Knock down with siRNA and gene expression profiling in the presence of selective kinase inhibitors showed that proximal kinases Lck and PKCθ are crucial signaling hubs during T helper cell activation, revealing a clear role for Lck in Th1 development and for PKCθ in both Th1 and Th2 development. Medial signaling via MAPkinases appeared to be less important in these pathways, since specific inhibitors of these kinases displayed a minor effect on gene expression. Translation towards a primary, whole blood setting and purified human CD4+ T cells revealed that PMA/CD3 stimulation induced a more pronounced Th1 specific, Lck and PKCθ dependent IFNγ production, whereas PMA/CD28 induced Th2 specific IL-5 and IL-13 production, independent of Lck activation. PMA/CD3-mediated skewing towards a Th1 phenotype was also reflected in mRNA expression of the master transcription factor Tbet, whereas PMA/CD28-mediated stimulation enhanced GATA3 mRNA expression in primary human CD4+ Tcells.CONCLUSIONS: This study identifies stimulatory pathways and gene expression profiles for in vitro skewing of T helper cell activation. PMA/CD3 stimulation enhances a Th1-like response in an Lck and PKCθ dependent fashion, whereas PMA/CD28 stimulation results in a Th2-like phenotype independent of the proximal TCR-tyrosine kinase Lck. This approach offers a robust and fast translational in vitro system for skewed T helper cell responses in Jurkat T cells, primary human CD4+ Tcells and in a more complex matrix such as human whole blood.
Multiple sclerosis (MS) is a severe inflammatory condition of the central nervous system (CNS) affecting about 2.5 million people globally. It is more common in females, usually diagnosed in their 30s and 40s, and can shorten life expectancy by 5 to 10 years. While MS is rarely fatal; its effects on a person's life can be profound, which signifies comprehensive management and support. Most studies regarding MS focus on how lymphocytes and other immune cells are involved in the disease. However, little attention has been given to red blood cells (erythrocytes), which might also be important in developing MS. Artificial intelligence (AI) has shown significant potential in medical imaging for analyzing blood cells, enabling accurate and efficient diagnosis of various conditions through automated image analysis. The project aims to implement an AI pipeline based on Deep Learning (DL) algorithms (e.g., Transfer Learning approach) to classify MS and Healthy Blood cells.
