Revisiting differences between atopic and non-atopic asthmatics: When age is shaping airway inflammatory profile

BACKGROUND: Atopic asthma is one of the most common asthma phenotypes and is generally opposed to the non-atopic counterpart. There have been very few large-scale studies comparing atopic and non-atopic asthmatics in terms of systemic and airway inflammation across the age spectrum. METHODS: Here, we have undertaken a retrospective study investigating 1626 patients (924 atopic and 702 non-atopic asthmatics) recruited from our university asthma clinic who underwent extensive clinical investigations including induced sputum. Atopy was defined by any positive specific IgE to common aeroallergens (>0,35 kU/L). We performed direct comparisons between the groups and sought to appreciate the influence of age on the airway and systemic inflammatory components. The study was approved by the ethics committee of the University Hospital of Liege (Ref. 2016/276). Informed consents were obtained from healthy subjects. RESULTS: Atopic asthmatics were younger (P < .001), had a higher male/female ratio (P < .001), an earlier disease onset (P < .001) and a greater proportion of treated rhinitis (P < .001) while non-atopic asthmatics had greater smoke exposure (P < .001), lower FEV(1)/FVC ratio (P = .01) and diffusing capacity (P < .001). There was no difference between the 2 groups regarding FEV(1) (% predicted), asthma control, asthma quality of life and exacerbations in the previous 12 months. Regarding inflammation, atopic patients had higher FeNO levels (median = 28 ppb, P < .001), were more eosinophilic both in blood (median = 2.8%, P < .001) and in sputum (median = 2.2%, P < .001) while non-atopic patients displayed greater blood (median = 57%, P = .01) and sputum (median = 58.8%, P = .01) neutrophilic inflammation. However, stratifying patients by age showed that non-atopic asthmatics above 50 years old became equally eosinophilic in the sputum (P = .07), but not in the blood, as compared to atopic patients. Likewise, FeNO rose in non-atopic patients after 50 years old but remained, however, lower than in atopic patients. CONCLUSIONS: We conclude that, while sharing many features, atopic group still differentiates from non-atopic asthmatics by demographics, functional and inflammatory profiles. When atopic asthmatics showed a constant eosinophilic pattern across the age spectrum, non-atopic asthmatics were found to be neutrophilic before the age of 50 but eosinophilic above 50 years old

Microglia in Neurological Diseases: A Road Map to Brain-Disease Dependent-Inflammatory Response

Microglia represent a specialized population of macrophages-like cells in the central nervous system (CNS) considered immune sentinels that are capable of orchestrating a potent inflammatory response. Microglia are also involved in synaptic organization, trophic neuronal support during development, phagocytosis of apoptotic cells in the developing brain, myelin turnover, control of neuronal excitability, phagocytic debris removal as well as brain protection and repair. Microglial response is pathology dependent and affects to immune, metabolic. In this review, we will shed light on microglial activation depending on the disease context and the influence of factors such as aging, environment or cell-to-cell interaction

A clustering analysis of eosinophilic asthmatics: Two clusters with sharp differences in atopic status and disease severity.

peer reviewe

GBR 12909 administration as a mouse model of bipolar disorder mania: mimicking quantitative assessment of manic behavior

Mania is a core feature of bipolar disorder (BD) that traditionally is assessed using rating scales. Studies using a new human behavioral pattern monitor (BPM) recently demonstrated that manic BD patients exhibit a specific profile of behavior that differs from schizophrenia and is characterized by increased motor activity, increased specific exploration, and perseverative locomotor patterns as assessed by spatial d. It was hypothesized that disrupting dopaminergic homeostasis by inhibiting dopamine transporter (DAT) function would produce a BD mania-like phenotype in mice as assessed by the mouse BPM. We compared the spontaneous locomotor and exploratory behavior of C57BL/6J mice treated with the catecholamine transporter inhibitor amphetamine or the selective DAT inhibitor GBR 12909 in the mouse BPM. We also assessed the duration of the effect of GBR 12909 by testing mice in the BPM for 3 h and its potential strain dependency by testing 129/SvJ mice. Amphetamine produced hyperactivity and increased perseverative patterns of locomotion as reflected in reduced spatial d values but reduced exploratory activity in contrast to the increased exploration observed in BD patients. GBR 12909 increased activity and reduced spatial d in combination with increased exploratory behavior, irrespective of inbred strain. These effects persisted for at least 3 h. Thus, selectively inhibiting the DAT produced a long-lasting cross-strain behavioral profile in mice that was consistent with that observed in manic BD patients. These findings support the use of selective DAT inhibition in animal models of the impaired dopaminergic homeostasis putatively involved in the pathophysiology of BD mania

Evidence for two clusters among non-eosinophilic asthmatics.

peer reviewed[en] BACKGROUND: Although asthma is often seen as an eosinophilic disease associated with atopy non eosinophilic asthmatics represent a substantial part of asthmatic population. OBJECTIVE: Here we have applied an unsupervised clustering method on a cohort of 588 non eosinophilic asthmatics (sputum eosinophils <3%) recruited from an asthma clinic of a secondary care center. METHODS: Our cluster analysis of the whole cohort identified two subgroups as cluster 1 (n=417) and cluster 2 (n=171). RESULTS: Cluster 1 consisted of a dominant female group with a late disease onset, a low proportion of atopy (24%) and a substantial smoking history (53%). In this cluster, treatment burden was low (<50% of ICS users), asthma control and quality of life was poor with median ACT, ACQ and AQLQ of 16 and 1,7 and 4,5 respectively whereas lung function was preserved with a median post bronchodilation FEV1 of 93% predicted. Cluster 2 was a dominant male group, almost exclusively composed of atopic patients (99%) with early disease onset and a moderate treatment burden (median ICS dose 800 µg/d equivalent beclomethasone). In cluster 2 asthma was partially controlled with median ACT and ACQ reaching 18 and 1.3 respectively and lung function well preserved with a median post bronchodilation 95% predicted. While systemic and airway neutrophilic inflammation was the dominant pattern in cluster 1, cluster 2 essentially comprised paucigranulocytic asthma with moderately elevated FeNO. CONCLUSION: Non eosinophilic asthma splits in two clusters distinguishing by disease onset, atopic status, smoking history, systemic and airway inflammation and disease control and quality of life

DNA methylation-based classification of sinonasal tumors

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs

DNA methylation-based classification of sinonasal tumors

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs