Differentiation and Glucocorticoid Regulated Apopto-Phagocytic Gene Expression Patterns in Human Macrophages. Role of Mertk in Enhanced Phagocytosis

The daily clearance of physiologically dying cells is performed safely mainly by cells in the mononuclear phagocyte system. They can recognize and engulf dying cells utilizing several cooperative mechanisms. In our study we show that the expression of a broad range of apopto-phagocytic genes is strongly up-regulated during differentiation of human monocytes to macrophages with different donor variability. The glucocorticoid dexamethasone has a profound effect on this process by selectively up-regulating six genes and down-regulating several others. The key role of the up-regulated mer tyrosine kinase (Mertk) in dexamethasone induced enhancement of phagocytosis could be demonstrated in human monocyte derived macrophages by gene silencing as well as blocking antibodies, and also in a monocyte-macrophage like cell line. However, the additional role of other glucocorticoid induced elements must be also considered since the presence of autologous serum during phagocytosis could almost completely compensate for the blocked function of Mertk

Anti-Fas mAb-induced apoptosis and cytolysis of airway tissue eosinophils aggravates rather than resolves established inflammation

BACKGROUND: Fas receptor-mediated eosinophil apoptosis is currently forwarded as a mechanism resolving asthma-like inflammation. This view is based on observations in vitro and in airway lumen with unknown translatability to airway tissues in vivo. In fact, apoptotic eosinophils have not been detected in human diseased airway tissues whereas cytolytic eosinophils abound and constitute a major mode of degranulation of these cells. Also, Fas receptor stimulation may bypass the apoptotic pathway and directly evoke cytolysis of non-apoptotic cells. We thus hypothesized that effects of anti-Fas mAb in vivo may include both apoptosis and cytolysis of eosinophils and, hence, that established eosinophilic inflammation may not resolve by this treatment. METHODS: Weeklong daily allergen challenges of sensitized mice were followed by airway administration of anti-Fas mAb. BAL was performed and airway-pulmonary tissues were examined using light and electron microscopy. Lung tissue analysis for CC-chemokines, apoptosis, mucus production and plasma exudation (fibrinogen) were performed. RESULTS: Anti-Fas mAb evoked apoptosis of 28% and cytolysis of 4% of eosinophils present in allergen-challenged airway tissues. Furthermore, a majority of the apoptotic eosinophils remained unengulfed and eventually exhibited secondary necrosis. A striking histopathology far beyond the allergic inflammation developed and included degranulated eosinophils, neutrophilia, epithelial derangement, plasma exudation, mucus-plasma plugs, and inducement of 6 CC-chemokines. In animals without eosinophilia anti-Fas evoked no inflammatory response. CONCLUSION: An efficient inducer of eosinophil apoptosis in airway tissues in vivo, anti-Fas mAb evoked unprecedented asthma-like inflammation in mouse allergic airways. This outcome may partly reflect the ability of anti-Fas to evoke direct cytolysis of non-apoptotic eosinophils in airway tissues. Additionally, since most apoptotic tissue eosinophils progressed into the pro-inflammatory cellular fate of secondary necrosis this may also explain the aggravated inflammation. Our data indicate that Fas receptor mediated eosinophil apoptosis in airway tissues in vivo may cause severe disease exacerbation due to direct cytolysis and secondary necrosis of eosinophils

Impact of protozoan cell death on parasite-host interactions and pathogenesis

PCD in protozoan parasites has emerged as a fascinating field of parasite biology. This not only relates to the underlying mechanisms and their evolutionary implications but also to the impact on the parasite-host interactions within mammalian hosts and arthropod vectors. During recent years, common functions of apoptosis and autophagy in protozoa and during parasitic infections have emerged. Here, we review how distinct cell death pathways in Trypanosoma, Leishmania, Plasmodium or Toxoplasma may contribute to regulation of parasite cell densities in vectors and mammalian hosts, to differentiation of parasites, to stress responses, and to modulation of the host immunity. The examples provided indicate crucial roles of PCD in parasite biology. The existence of PCD pathways in these organisms and the identification as being critical for parasite biology and parasite-host interactions could serve as a basis for developing new anti-parasitic drugs that take advantage of these pathways

MicroRNA Genes Derived from Repetitive Elements and Expanded by Segmental Duplication Events in Mammalian Genomes

MicroRNAs (miRNAs) are a class of small noncoding RNAs that regulate gene expression by targeting mRNAs for translation repression or mRNA degradation. Many miRNAs are being discovered and studied, but in most cases their origin, evolution and function remain unclear. Here, we characterized miRNAs derived from repetitive elements and miRNA families expanded by segmental duplication events in the human, rhesus and mouse genomes. We applied a comparative genomics approach combined with identifying miRNA paralogs in segmental duplication pair data in a genome-wide study to identify new homologs of human miRNAs in the rhesus and mouse genomes. Interestingly, using segmental duplication pair data, we provided credible computational evidence that two miRNA genes are located in the pseudoautosomal region of the human Y chromosome. We characterized all the miRNAs whether they were derived from repetitive elements or not and identified significant differences between the repeat-related miRNAs (RrmiRs) and non-repeat-derived miRNAs in (1) their location in protein-coding and intergenic regions in genomes, (2) the minimum free energy of their hairpin structures, and (3) their conservation in vertebrate genomes. We found some lineage-specific RrmiR families and three lineage-specific expansion families, and provided evidence indicating that some RrmiR families formed and expanded during evolutionary segmental duplication events. We also provided computational and experimental evidence for the functions of the conservative RrmiR families in the three species. Together, our results indicate that repetitive elements contribute to the origin of miRNAs, and large segmental duplication events could prompt the expansion of some miRNA families, including RrmiR families. Our study is a valuable contribution to the knowledge of evolution and function of non-coding region in genome

Eosinophils in glioblastoma biology

Glioblastoma multiforme (GBM) is the most common primary brain tumor in adults. The development of this malignant glial lesion involves a multi-faceted process that results in a loss of genetic or epigenetic gene control, un-regulated cell growth, and immune tolerance. Of interest, atopic diseases are characterized by a lack of immune tolerance and are inversely associated with glioma risk. One cell type that is an established effector cell in the pathobiology of atopic disease is the eosinophil. In response to various stimuli, the eosinophil is able to produce cytotoxic granules, neuromediators, and pro-inflammatory cytokines as well as pro-fibrotic and angiogenic factors involved in pathogen clearance and tissue remodeling and repair. These various biological properties reveal that the eosinophil is a key immunoregulatory cell capable of influencing the activity of both innate and adaptive immune responses. Of central importance to this report is the observation that eosinophil migration to the brain occurs in response to traumatic brain injury and following certain immunotherapeutic treatments for GBM. Although eosinophils have been identified in various central nervous system pathologies, and are known to operate in wound/repair and tumorstatic models, the potential roles of eosinophils in GBM development and the tumor immunological response are only beginning to be recognized and are therefore the subject of the present review

Discovering Pediatric Asthma Phenotypes on the Basis of Response to Controller Medication Using Machine Learning

RationalePediatric asthma has variable underlying inflammation and symptom control. Approaches to addressing this heterogeneity, such as clustering methods to find phenotypes and predict outcomes, have been investigated. However, clustering based on the relationship between treatment and clinical outcome has not been performed, and machine learning approaches for long-term outcome prediction in pediatric asthma have not been studied in depth.ObjectivesOur objectives were to use our novel machine learning algorithm, predictor pursuit (PP), to discover pediatric asthma phenotypes on the basis of asthma control in response to controller medications, to predict longitudinal asthma control among children with asthma, and to identify features associated with asthma control within each discovered pediatric phenotype.MethodsWe applied PP to the Childhood Asthma Management Program study data (n = 1,019) to discover phenotypes on the basis of asthma control between assigned controller therapy groups (budesonide vs. nedocromil). We confirmed PP's ability to discover phenotypes using the Asthma Clinical Research Network/Childhood Asthma Research and Education network data. We next predicted children's asthma control over time and compared PP's performance with that of traditional prediction methods. Last, we identified clinical features most correlated with asthma control in the discovered phenotypes.ResultsFour phenotypes were discovered in both datasets: allergic not obese (A+/O-), obese not allergic (A-/O+), allergic and obese (A+/O+), and not allergic not obese (A-/O-). Of the children with well-controlled asthma in the Childhood Asthma Management Program dataset, we found more nonobese children treated with budesonide than with nedocromil (P = 0.015) and more obese children treated with nedocromil than with budesonide (P = 0.008). Within the obese group, more A+/O+ children's asthma was well controlled with nedocromil than with budesonide (P = 0.022) or with placebo (P = 0.011). The PP algorithm performed significantly better (P < 0.001) than traditional machine learning algorithms for both short- and long-term asthma control prediction. Asthma control and bronchodilator response were the features most predictive of short-term asthma control, regardless of type of controller medication or phenotype. Bronchodilator response and serum eosinophils were the most predictive features of asthma control, regardless of type of controller medication or phenotype.ConclusionsAdvanced statistical machine learning approaches can be powerful tools for discovery of phenotypes based on treatment response and can aid in asthma control prediction in complex medical conditions such as asthma