Tonsillar transcriptional profiles in atopic and non-atopic subjects.

BACKGROUND: Emerging research suggests that local lymphatic tissue such as tonsils have important role in regulating the immune responses. However, allergen sensitization-induced alterations in transcriptome of tonsils are not known. OBJECTIVES: To examine the key differences in tonsillar gene expression between atopic and non-atopic subjects and further by type of sensitization. METHODS: RNA-sequencing was performed on 52 tonsillar samples from atopic and non-atopic tonsillectomy patients. Sensitization to common food- and aero-allergen was defined by allergen specific IgE. Following groups were studied: (1) aero- and food-allergen sensitized (AS+FS) versus non-sensitized (NS), (2) aeroallergen-sensitized (AS) versus food-allergen sensitized (FS), (3) AS versus NS, (4) FS versus NS. Bioinformatics analysis was done using DESeq2(v3.10.2), WGCNA and GATK pipeline in R software (v3.3.1). Protein-protein interaction network was made from String database. RESULTS: We studied 13 aeroallergen-sensitized, 6 food-allergen sensitized, 4 both food-and aero-allergen-sensitized and 29 non-sensitized tonsillectomy patients. Overall, 697 unique differentially expressed genes (DEGs) were detected in all sensitized subgroups including chemokines (CXCL2, CXCL8, CXCL10, CXCL11), IL-20RA, MUC1 and MUC20. When comparing different groups, the gene expression profiles overlapped except the AS versus FS group comparison, suggesting significantly different gene expression between the two sensitization subgroups. Furthermore, aeroallergen-sensitized subjects had more prominent immune responses compared with non-sensitized and food-allergen sensitized subjects including gene expression for IL-17 pathway and Toll-like receptor signalling pathway. CONCLUSION: Allergic sensitization is associated with extensive tonsillar transcriptomic alterations and changes in immune related genes and pathways. Distinct differences were found between aero-allergen and food-allergen sensitization.

Hierarchical clustering in evaluating inflammatory upper airway phenotypes; increased symptoms in adults with allergic multimorbidity.

BACKGROUND: Inflammatory upper airway diseases cause significant morbidity. They include phenotypes with different treatment; allergic or non-allergic rhinitis (AR, nAR), and chronic rhinosinusitis with or without nasal polyps (CRSwNP, CRSsNP). In clinical practice, these phenotypes are often difficult to distinguish and may overlap. OBJECTIVE: To evaluate if hierarchical clustering can be used to distinguish these phenotypes based on the presence of nasal polyps, off-seasonal allergic symptoms, and self-reported background characteristics - e.g. atopic dermatitis (AD); and to further analyse the obtained clusters. METHODS: We studied a random sample of 74 CRS (chronic rhinosinusitis) patients, and a control group of 80 subjects without CRS with/without AR (tertiary hospitals, 2006-2012). All underwent interview and nasal examination, and filled a questionnaire. Variables regarding demographics, off-seasonal symptoms, and clinical findings were collected. Hierarchical clustering was performed, the obtained clusters were cross-tabulated and analysed. RESULTS: Four clusters were identified; 1: "Severe symptoms and CRSwNP" (n = 29), 2: "Asymptomatic AR and controls" (n = 39), 3: "Moderate symptoms and CRSsNP" (n = 36), and 4: "Symptomatic and AD" (n = 50). Cluster 1 had most sinonasal symptoms, cluster 3 had a high prevalence of facial pain. The presence of AR did not distinguish CRS groups. Of the AR subjects, 51 % belonged to cluster 4, where AR with off-seasonal airway symptoms and AD predominated. CONCLUSIONS: Hierarchical clustering can be used to distinguish inflammatory upper airway disease phenotypes. The AR phenotype was subdivided by the presence of AD. Adult AR+ AD patients could benefit from active clinical care of the upper airways also off-season.

Genomics of asthma, allergy and chronic rhinosinusitis: novel concepts and relevance in airway mucosa.

Genome wide association studies (GWASs) have revealed several airway disease-associated risk loci. Their role in the onset of asthma, allergic rhinitis (AR) or chronic rhinosinusitis (CRS), however, is not yet fully understood. The aim of this review is to evaluate the airway relevance of loci and genes identified in GWAS studies. GWASs were searched from databases, and a list of loci associating significantly (p < 10-8) with asthma, AR and CRS was created. This yielded a total of 267 significantly asthma/AR-associated loci from 31 GWASs. No significant CRS -associated loci were found in this search. A total of 170 protein coding genes were connected to these loci. Of these, 76/170 (44%) showed bronchial epithelial protein expression in stained microscopic figures of Human Protein Atlas (HPA), and 61/170 (36%) had a literature report of having airway epithelial function. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation analyses were performed, and 19 functional protein categories were found as significantly (p < 0.05) enriched among these genes. These were related to cytokine production, cell activation and adaptive immune response, and all were strongly connected in network analysis. We also identified 15 protein pathways that were significantly (p < 0.05) enriched in these genes, related to T-helper cell differentiation, virus infection, JAK-STAT signaling pathway, and asthma. A third of GWAS-level risk loci genes of asthma or AR seemed to have airway epithelial functions according to our database and literature searches. In addition, many of the risk loci genes were immunity related. Some risk loci genes also related to metabolism, neuro-musculoskeletal or other functions. Functions overlapped and formed a strong network in our pathway analyses and are worth future studies of biomarker and therapeutics.

RNA-SSPT: RNA Secondary Structure Prediction Tools.

The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes.