Network analysis of quantitative proteomics on asthmatic bronchi: effects of inhaled glucocorticoid treatment

<p>Abstract</p> <p>Background</p> <p>Proteomic studies of respiratory disorders have the potential to identify protein biomarkers for diagnosis and disease monitoring. Utilisation of sensitive quantitative proteomic methods creates opportunities to determine individual patient proteomes. The aim of the current study was to determine if quantitative proteomics of bronchial biopsies from asthmatics can distinguish relevant biological functions and whether inhaled glucocorticoid treatment affects these functions.</p> <p>Methods</p> <p>Endobronchial biopsies were taken from untreated asthmatic patients (<it>n </it>= 12) and healthy controls (<it>n </it>= 3). Asthmatic patients were randomised to double blind treatment with either placebo or budesonide (800 μg daily for 3 months) and new biopsies were obtained. Proteins extracted from the biopsies were digested and analysed using isobaric tags for relative and absolute quantitation combined with a nanoLC-LTQ Orbitrap mass spectrometer. Spectra obtained were used to identify and quantify proteins. Pathways analysis was performed using Ingenuity Pathway Analysis to identify significant biological pathways in asthma and determine how the expression of these pathways was changed by treatment.</p> <p>Results</p> <p>More than 1800 proteins were identified and quantified in the bronchial biopsies of subjects. The pathway analysis revealed acute phase response signalling, cell-to-cell signalling and tissue development associations with proteins expressed in asthmatics compared to controls. The functions and pathways associated with placebo and budesonide treatment showed distinct differences, including the decreased association with acute phase proteins as a result of budesonide treatment compared to placebo.</p> <p>Conclusions</p> <p>Proteomic analysis of bronchial biopsy material can be used to identify and quantify proteins using highly sensitive technologies, without the need for pooling of samples from several patients. Distinct pathophysiological features of asthma can be identified using this approach and the expression of these features is changed by inhaled glucocorticoid treatment. Quantitative proteomics may be applied to identify mechanisms of disease that may assist in the accurate and timely diagnosis of asthma.</p> <p>Trial registration</p> <p>ClinicalTrials.gov registration <a href="http://www.clinicaltrials.gov/ct2/show/NCT01378039">NCT01378039</a></p

Quantitative expression of osteopontin in nasal mucosa of patients with allergic rhinitis: effects of pollen exposure and nasal glucocorticoid treatment

<p>Abstract</p> <p>Background</p> <p>Osteopontin (OPN) is a multifunctional cytokine that has been primarily investigated in Th1 diseases. Recently, it has also been implicated in Th2-mediated allergic diseases, such as asthma. The expression of OPN in allergic rhinitis (AR) is currently unknown, as is the effect of intranasal glucocorticosteroids (GCs) on that expression.</p> <p>Methods</p> <p>Subjects with AR were randomised to receive treatment with fluticasone propionate (FP) (n = 12) or a placebo (n = 16) over the grass pollen season and nasal biopsies were taken prior to, and during the season. OPN expression in the nasal mucosa was examined with immunohistochemistry. Healthy non-AR controls (n = 5) were used as a comparator.</p> <p>Results</p> <p>OPN expression was detected in epithelial cells, subepithelial infiltrating/inflammatory cells and cells lining the vessels and glands of all subjects. Comparison of the pre- and peak-pollen season biopsy sections in placebo treated patients revealed no increase in OPN expression during the grass pollen season (5.7% vs 6.4%). Treatment with a local glucocorticosteroid did not alter the expression of OPN during pollen exposure (6.2% vs 6.7%).</p> <p>Conclusion</p> <p>OPN has been increasingly associated with the pathogenesis of various Th2-mediated diseases. However, our finding that the OPN expression in the nasal mucosa of AR patients is not significantly affected by allergen exposure and is comparable to that of the healthy controls, suggests that intracellular OPN is not directly involved in the pathogenesis of allergic rhinitis.</p

Expansion of CD4+CD25+ and CD25- T-Bet, GATA-3, Foxp3 and RORγt Cells in Allergic Inflammation, Local Lung Distribution and Chemokine Gene Expression

Allergic asthma is associated with airway eosinophilia, which is regulated by different T-effector cells. T cells express transcription factors T-bet, GATA-3, RORγt and Foxp3, representing Th1, Th2, Th17 and Treg cells respectively. No study has directly determined the relative presence of each of these T cell subsets concomitantly in a model of allergic airway inflammation. In this study we determined the degree of expansion of these T cell subsets, in the lungs of allergen challenged mice. Cell proliferation was determined by incorporation of 5-bromo-2′-deoxyuridine (BrdU) together with 7-aminoactnomycin (7-AAD). The immunohistochemical localisation of T cells in the lung microenvironments was also quantified. Local expression of cytokines, chemokines and receptor genes was measured using real-time RT-PCR array analysis in tissue sections isolated by laser microdissection and pressure catapulting technology. Allergen exposure increased the numbers of T-bet+, GATA-3+, RORγt+ and Foxp3+ cells in CD4+CD25+ and CD4+CD25- T cells, with the greatest expansion of GATA-3+ cells. The majority of CD4+CD25+ T-bet+, GATA-3+, RORγt+ and Foxp3+ cells had incorporated BrdU and underwent proliferation during allergen exposure. Allergen exposure led to the accumulation of T-bet+, GATA-3+ and Foxp3+ cells in peribronchial and alveolar tissue, GATA-3+ and Foxp3+ cells in perivascular tissue, and RORγt+ cells in alveolar tissue. A total of 28 cytokines, chemokines and receptor genes were altered more than 3 fold upon allergen exposure, with expression of half of the genes claimed in all three microenvironments. Our study shows that allergen exposure affects all T effector cells in lung, with a dominant of Th2 cells, but with different local cell distribution, probably due to a distinguished local inflammatory milieu

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

The distribution of T cell transcription factors differs in the lung microenvironments during allergic inflammation.

<p>A) Photographs of immunohistochemistry of MBP (red) in OVA or PBS exposed sensitised mice, T-bet, GATA-3, Foxp3 (brown) and RORγt (red) in peribronchial, perivascular and alveolar tissue after exposure to OVA in sensitised mice. B) Quantification (cells/mm²) of T-bet, GATA-3, Foxp3 and RORγt expressing cells in different compartments of the lungs, including peribronchial tissue, perivascular tissue and alveolar tissue, after exposure to PBS or OVA. Data shown as mean+SEM, *<0.05. C) Pie chart showing the relative distribution of T-bet, GATA-3, Foxp3 and RORγt expressing cells in the different lung compartments after exposure of mice to OVA.</p