Keywords: Allergic asthma, Atopic dermatitis, Immune disorders, Transcriptomic profiling, Type I hypersensitivity
Introduction
Allergy of type I hypersensitivity is a severe condition experienced by about 150 million people in Europe1. Type I hypersensitivity can be manifested as atopic dermatitis, rhinitis, conjunctivitis, rhinoconjuctivitis or allergic asthma. These manifestations very often appear together2. In addition, they usually occur gradually from atopic dermatitis to allergic asthma, collectively known as the atopic march3, 4. All of these clinical signs, even if they are distinct, come from a single intrinsic process: IgE mediated (type I) hypersensitivity. In type I hypersensitivity, T helper type 2 (Th2) cells play a key role in the patient response to antigens by activating immunoglobulin (Ig) E sensitization3, 5.
The type I hypersensitivity reaction occurs when a mucosal surface (i. e. respiratory or gastrointestinal tract) or the skin is exposed to an antigen, which causes an allergy (allergen)6, 7. The immune response to the antigen begins with the sensitization phase7. During this phase, the antigen is captured by antigen-presenting cells (APCs), and subsequently presented to naive T cells7, 8. This interaction leads to the activation of naive T cells, which then secrete interleukin-4 (IL-4), which together with IL-4 produced by basophils and mast cells, initiate their differentiation into Th2 cells7, 9..Additionally, differentiated Th2 cells further secrete IL-4 and IL-13 which stimulate B cell activation, leading to class-switching and production of IgE antibodies by activated B cells7, 8, 10. Consequently, the binding of IgE antibodies to high-affinity IgE receptors (FcεRI) on mast cells and basophils cause their sensitization, and this leads to the subsequent effector phase. The effector phase of type I hypersensitivity starts with cross-linking of the antigen and IgE antibodies bound to FcεRI on mast cells and basophils10, 11. The cross-linking triggers the activation of mast cells and basophils leading to their degranulation. During degranulation, inflammatory mediators in granules, such as histamine, leukotrienes, and cytokines, are rapidly released, which induce characteristic hypersensitivity symptoms, such as itching, sneezing, nasal congestion, wheezing, dermatitis, or gastrointestinal symptoms7, 8, 11, 12. When re-exposure to the allergen occurs, the type I hypersensitivity response is immediate8.
Although the general progression of type I hypersensitivity reaction is well known, the underlying mechanisms at the transcript level remains understudied. To understand the complex transcriptomic mechanisms underscoring patients with type I hypersensitivity symptoms, we investigated the blood transcriptome of adult participants from Central European Longitudinal Study of Parents and Children: Young Adults (CELSPAC: YA) cohort study. We performed differential gene expression analysis (DGEA) and compared transcriptomic profiles of participants with allergic reaction to population without any of allergic triggers or symptoms. By analysing differences in gene expression and identifying key biomarkers, we aim to better understand how these conditions develop and potentially predict their future progression. The analysis of the blood transcriptome not only serves as a non-invasive and accessible source of information, but also offers a comprehensive view of the systemic changes occurring in response to antigens. Such knowledge can pave the way for more targeted and effective therapeutic interventions, leading to improved patient outcomes and ultimately, better management of type I hypersensitivity.
Materials and methods
Study population and sample collection
The data analysed in this study is derived from the CELSPAC: YA cohort study (ethical approval no. ELSPAC/EK/2/2019), a follow-up study of European Longitudinal Study of Pregnancy and Childhood (ELSPAC-CZ) performed in Czech Republic (for more detailed description of ELSPAC-CZ, see Piler et al. (2017)13). Participants underwent blood sampling and completed comprehensive questionnaires. The questionnaire data contain information about participants such as their age, BMI, education, smoking status, alcohol consumption, and health outcome. The health outcome information includes information on immune-related diseases, such as atopic dermatitis, asthma, allergies to food, drugs, insects and pollen, dust, or mite, contact dermatitis, celiac disease, psoriasis, or lupus. The detailed information of CELSPAC: YA cohort is provided in our recent study by Rudzanova et al. (2023)14.
All participants with non-allergic triggers or symptoms (e.g. contact dermatitis) were excluded from the analysis to avoid coincidence with inflammation other than type I hypersensitivity. To analyse the underlying mechanisms of type I hypersensitivity, we formed 3 test groups: 1) subjects with allergy: All participants who answered “yes” to the question “Have you ever been diagnosed with an allergy (pollen, dust mite, food, insects), asthma or atopic dermatitis by a doctor? ” in a questionnaire; 2) Subjects with allergy without asthma and atopic dermatitis: All whose answered “yes” to the questionnaire question “Have you ever been diagnosed by a doctor with an allergy (pollen, dust mite, food, insect)” and simultaneously answered “no” to the question “Have you ever been diagnosed by a doctor with asthma or atopic dermatitis”. 3) Subjects with allergic asthma and atopic dermatitis: All those who answered “yes” to the question “Have you ever been diagnosed by a doctor with asthma and atopic dermatitis at the same time?”. The questionnaires were completed under the supervision of health professionals.
The collected whole blood samples (9 mL) were immediately centrifuged, and the buffy coat fraction (i.e., white blood cell fraction) was separated by Ficoll-Paque to isolate peripheral blood mononuclear cells (PBMC). The PBMC fraction was suspended in RNAprotect Cell Reagent and frozen (-80 °C) in 300 μL aliquots containing ~13 million cells until use for analysis. RNA was then extracted from PBMC in RNAprotect Cell Reagent with a Zymo Research: Quick-RNA Whole Blood (R1201) extraction kit according to manufacturer´s instructions. Quality parameters like concentration, purity (Nanodrop, Thermo Fisher Scientific) and integrity (Agilent 5200 Fragment Analyzer system) of extracted RNA were determined. For the library preparation and sequencing, 1 μg of high-quality RNA per sample was used. The mean RNA Integrity Number (RIN) for samples was 9.0 (min − max: 7.3 – 10.0).
Library preparation and Sequencing
Genome-wide analysis of gene expression was conducted using the Next Generation Sequencing (NGS) platform with the QuantSeq library preparation step. cDNA libraries for each sample (RNA) were generated from 1 μg of total RNA using the Quantseq 3′ mRNA-Seq Library Prep kit for Illumina (Lexogen) following the manufacturer’s instructions. QuantSeq generates strand-specific NGS libraries close to the 3′ end of poly-A RNA15. Standard external barcodes were ligated to allow for multiplex sequencing. After PCR amplification, the libraries were size-selected with Agencourt AMPure XP magnetic beads (Beckman Coulter). Libraries were quantified by Qubit (Life Technologies), and their size (∼250 bp) was determined using an Agilent 2100 Bioanalyzer. Libraries were sequenced (Illumina NovaSeq platform) and quality checked (110 bp single read) to obtain min. 20–25 million reads per sample. Following this, NGS data were demultiplexed.
Data preprocessing
After sample demultiplexing, UMI tags (6 bp) were removed from all reads using umi_tools (1.0.0). Afterwards, TruSeq adapters and low quality 3’-ends of reads (treshold: 13) were trimmed using bbmap (38.42). Short reads (length < 30 bp) and reads with low quality (treshold: 24) were filtered out. Samples were mapped against reference human genome (Genome Reference Consortium Human Build 38) using STAR (2.7.7a) and deduplicated using umi_tools (1.0.0). Transcript features were summarized using htseq-count (0.11.1). During data pre-processing, quality of samples was continuously controlled using fastqc (0.11.5), qualimap (11_12-16) and multiqc (1.8, 1.12).
Data processing and statistical analysis were performed using R programming software (version 4.2.2)16. Only samples with allergies to food, insect, pollen, dust, or mite were included in the analysis; and participants without any other immune-related diseases (i.e., celiac disease, psoriasis, or lupus) were assigned as a control group. Samples with any missing data were filtered out. Genes with at least 5 CPM (counts per million) in at least 20% of samples were kept in the data set. The data was normalized using TMM (trimmed mean of M values) normalization and transformed to continuous log2 scale using limma-voom17, 18. Batch effects were accounted for using principal component analysis (PCA) plots and by correlation of principal components with potential confounders. Surrogate variable analysis was performed to the data and the first 10 surrogate variables were used to adjust the unknown cell blood composition19, 20, 21.
Statistical analysis
Differentially expressed genes were identified using limma lmFit model and p-values were corrected for multiple testing using Benjamini-Hochberg false discovery rate (FDR)22. The model was adjusted for biological, socio-economic, and technical covariates (sex, age, BMI, education, smoking status, alcohol consumption and library preparation batch). Genes were annotated using GeneCard22, DAVID and GO databases23, 24, 25. Functional analysis, namely Gene Set Enrichment Analysis (GSEA) and Sub-Network Enrichment Analysis (SNEA) was performed using Pathway Studio (version 12.0)26.
Results
Study population
The final dataset included 218 participants from CELSPAC: YA cohort study between 20–37 years old with an average BMI of 23.41. Overall, 114 participants had at least one allergic manifestation and 104 participants did not suffer from any of allergic triggers or symptoms. The detailed description of the population is presented in Table 1.
Table 1 : Description of study population participants (subset derived from CELSPAC: YA cohort).