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Background: There is increasing evidence indicating that immune system dysregulation plays a pivotal role in the pathogenesis of retinopathy of prematurity (ROP) and sepsis. This study aims to identify key diagnostic candidate genes in ROP with sepsis. Methods: We obtained publicly available data on ROP and sepsis from the gene expression omnibus database. Differential analysis and weighted gene correlation network analysis (WGCNA) were performed to identify differentially expressed genes (DEGs) and key module genes. Subsequently, we conducted functional enrichment analysis to gain insights into the biological functions and pathways. To identify immune-related pathogenic genes and potential mechanisms, we employed several machine learning algorithms, including Support Vector Machine Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF). We evaluated the diagnostic performance using nomogram and Receiver Operating Characteristic (ROC) curves. Furthermore, we used CIBERSORT to investigate immune cell dysregulation in sepsis and performed cMAP analysis to identify potential therapeutic drugs. Results: The sepsis dataset comprised 352 DEGs, while the ROP dataset had 307 DEGs and 420 module genes. The intersection between DEGs for sepsis and module genes for ROP consisted of 34 genes, primarily enriched in immune-related pathways. After conducting PPI network analysis and employing machine learning algorithms, we pinpointed five candidate hub genes. Subsequent evaluation using nomograms and ROC curves underscored their robust diagnostic potential. Immune cell infiltration analysis revealed immune cell dysregulation. Finally, through cMAP analysis, we identified some small molecule compounds that have the potential for sepsis treatment. Conclusion: Five immune-associated candidate hub genes (CLEC5A, KLRB1, LCN2, MCEMP1, and MMP9) were recognized, and the nomogram for the diagnosis of ROP with sepsis was developed. [ABSTRACT FROM AUTHOR]

Background: This study aims to propose a breast cancer prediction model for early diagnosis and prognosis management of breast cancer. Objective: In order to explore the pathogenesis of breast cancer and develop accurate breast cancer screening and treatment methods, we have used machine‐learning technologies to conduct an in‐depth study of breast cancer genetic data to obtain new breast cancer signature and prognostic prediction models. Methods: We explored an optimal cluster by unsupervised clustering methods with different expression genes (DEGs) between normal (n = 113) and tumour (n = 1,102) samples. Using least absolute shrinkage and selection operator (LASSO) regression, we selected four biomarkers to develop a predictive model by Cox regression method in the training set (n = 1,083) and validated its predictive accuracy and independence in the testing sets (n = 2,480). Then Gene Set Enrichment Analysis (GSEA) revealed enriched biological pathways in clusters. Finally, we constructed a nomogram including this signature and other significant risk factors to predict survival rates in patients. Results: Four mRNAs (CD163L1, QPRT, NKAIN1 and TP53AIP1) between two clusters from 4,938 DEGs were identified, and then a four‐gene model (risk scores = 0.454*CD163L1–0.360*NKAIN1 + 0.581*QPRT + 0.788*TP53AIP1) was established to divide patients into high‐ and low‐risk group with significantly different prognosis (p < 0.0001) in the training set. Integrated analysis revealed dysregulated molecular processes including predominantly oncogenic signalling pathway, cell cycle and DNA repair in high‐risk group but enriched metabolism pathway in low‐risk group. In addition, this model had similar predictive value (HR >1.60; p < 0.05) in three independent validation sets, which could predict survival independently with more power compared with single clinical factor. In addition, the nomogram could predict the prognosis of breast cancer patients precisely in the training set and another three testing sets. Conclusion: This model could predict prognosis of breast cancer patients precisely and independently, and provide evidence to make treatment decisions and design clinical trials. [ABSTRACT FROM AUTHOR]

C-Type lectin receptor 5A (CLEC5A) is a spleen tyrosine kinase- (Syk-) coupled pattern recognition receptor expressed on myeloid cells and involved in the innate immune response to viral and bacterial infections. Activation of the CLEC5A receptor with pathogen-derived antigens leads to a secretion of proinflammatory mediators such as TNF-α and IL-6 that may provoke a systemic cytokine storm, and CLEC5A gene polymorphisms are associated with the severity of DV infection. In addition, the CLEC5A receptor was mentioned in the context of noninfectious disorders like chronic obstructive pulmonary disease (COPD) or arthritis. Altogether, CLEC5A may be considered as an innate immune checkpoint capable to amplify proinflammatory signals, and this way contributes to infection or to aseptic inflammation. In this study, we determined CLEC5A receptor expression on different macrophage subsets (in vitro and ex vivo) and the functional consequences of its activation in aseptic conditions. The CLEC5A surface expression appeared the highest on proinflammatory M1 macrophages while intermediate on tumor-associated phenotypes (M2c or TAM). In contrast, the CLEC5A expression on ex vivo-derived alveolar macrophages from healthy donors or macrophages from ovarian cancer patients was hardly detectable. Targeting CLEC5A on noninflammatory macrophages with an agonistic α-CLEC5A antibody triggered a release of proinflammatory cytokines, resembling a response to dengue virus, and led to phenotypic changes in myeloid cells that may suggest their reprogramming towards a proinflammatory phenotype, e.g., upregulation of CD80 and downregulation of CD163. Interestingly, the CLEC5A agonist upregulated immune-regulatory molecules like CD206, PD-L1, and cytokines like IL-10, macrophage-derived chemokine (MDC/CCL22), and thymus and activation chemokine (TARC/CCL17) which are associated with an anti-inflammatory or a protumorigenic macrophage phenotype. In the absence of concomitant pathogenic or endogenous danger signals, the CLEC5A receptor activation did not amplify an autologous T cell response, which may represent a protective innate mechanism to avoid an undesirable autoimmune adaptive response. [ABSTRACT FROM AUTHOR]

Recently it was discovered that tissue-resident macrophages derive from embryonic precursors, not only from peripheral blood monocytes, and maintain themselves by self-renewal. Most in-vitro studies on macrophage biology make use of in-vitro cultured human monocyte-derived macrophages. Phagocytosis of IgG-opsonized particles by tissue-resident macrophages takes place via interaction with IgG receptors, the Fc-gamma receptors (FcγRs). We investigated the FcγR expression on macrophages both in-vivo and ex-vivo from different human tissues. Upon isolation of primary human macrophages from bone marrow, spleen, liver and lung, we observed that macrophages from all studied tissues expressed high levels of FcγRIII, which was in direct contrast with the low expression on blood monocyte-derived macrophages. Expression levels of FcγRI were highly variable, with bone marrow macrophages showing the lowest and alveolar macrophages the highest expression. Kupffer cells in the liver were the only tissue-resident macrophages that expressed the inhibitory IgG receptor, FcγRIIB. This inhibitory receptor was also found to be expressed by sinusoidal endothelial cells in the liver. In sum, our immunofluorescence data combined with ex-vivo stainings of isolated macrophages indicated that tissue-resident macrophages are remarkably unique and different from monocyte-derived macrophages in their phenotypic expression of IgG receptors. Tissue macrophages show distinct tissue-specific FcγR expression patterns. [ABSTRACT FROM AUTHOR]

Activin A belongs to the transforming growth factor superfamily and has a variety of biological functions. Studies have revealed that activin A can regulate the body's immune and inflammatory responses and participate in the regulation of cell death. In addition, activin A also has neurotrophic function and plays an important role in the repair of brain damage. This article summarizes recent advances in understanding the role and mechanism of action of activin A in brain injury and provides new hints into the application of activin A in the treatment of brain injury. [ABSTRACT FROM AUTHOR]

IL‐37 is produced and secreted by antigen‐presenting cells, including monocytes and DCs, where IL‐37 may act as an alarmin. The interleukin (IL)‐1 family member IL‐37 is one of few anti‐inflammatory cytokines, and it is capable of countering a broad spectrum of proinflammatory assaults. Although it is known that leukocytes are a major source of IL‐37, knowledge on IL‐37 production and secretion in specific immune cell types remains limited. Thus, we investigated IL‐37 mRNA expression as well as protein production and secretion in human PBMCs. In PBMCs stimulated with agonists of Toll‐like receptors (TLRs) 1–6 and 9, IL1F7 (the IL‐37‐encoding gene) was induced up to 9‐fold, peaked at 6–8 h and returned to steady‐state at 72 h. LPS‐induced IL1F7 expression comprised isoforms b and c but not a and e. Flow cytometry revealed that among IL‐37+ PBMCs, monocytes predominated (81–91%), but T cells (6–8%) and myeloid dendritic cells (mDCs, 1–2%) also contributed to the IL‐37+ leukocyte pool. Monocytes and mDCs, but not T cells, were capable of secreting IL‐37. Whereas monocytes and mDCs secreted IL‐37 upon LPS stimulation, only mDCs also released IL‐37 at steady‐state. Among monocyte subsets, IL‐37 was LPS inducible and secreted only in classical and, although less pronounced, in intermediate monocytes; secretion was observed as early as 3 h after stimulation. Overall, our data suggest that constitutive IL‐37 secretion by mDCs may serve to maintain an anti‐inflammatory milieu at steady state, whereas IL‐37 is stored in monocytes to be available for rapid release upon inflammatory encounters, thus acting as a novel anti‐inflammatory alarmin. These insights may prove important to advancing towards clinical use the protective functions of one of the most powerful anti‐inflammatory mediators so far discovered. [ABSTRACT FROM AUTHOR]

ANCA-associated vasculitis (AAV) comprises granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA), and eosinophilic granulomatosis with polyangiitis (EGPA). While systemic vasculitis is a hallmark of all AAV, GPA is characterized by extravascular granulomatous inflammation, preferentially affecting the respiratory tract. The mechanisms underlying the emergence of neutrophilic microabscesses; the appearance of multinucleated giant cells; and subsequent granuloma formation, finally leading to scarred or destroyed tissue in GPA, are still incompletely understood. This review summarizes findings describing the presence and function of molecules and cells contributing to granulomatous inflammation in the respiratory tract and to renal inflammation observed in GPA. In addition, factors affecting or promoting the development of granulomatous inflammation such as microbial infections, the nasal microbiome, and the release of damage-associated molecular patterns (DAMP) are discussed. Further, on the basis of numerous results, we argue that, in situ, various ways of exposure linked with a high number of infiltrating proteinase 3 (PR3)- and myeloperoxidase (MPO)-expressing leukocytes lower the threshold for the presentation of an altered PR3 and possibly also of MPO, provoking the local development of ANCA autoimmune responses, aided by the formation of ectopic lymphoid structures. Although extravascular granulomatous inflammation is unique to GPA, similar molecular and cellular patterns can be found in both the respiratory tract and kidney tissue of GPA and MPA patients; for example, the antimicrobial peptide LL37, CD163+ macrophages, or regulatory T cells. Therefore, we postulate that granulomatous inflammation in GPA or PR3-AAV is intertwined with autoimmune and destructive mechanisms also seen at other sites. [ABSTRACT FROM AUTHOR]