Omics technologies in allergy and asthma research: An EAACI position paper

Abstract

Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical These different cellular components are tightly interconnected with TA B L E 1 Summary of methods, protocols, and data analysis pipelines most frequently used in omics approaches for allergic diseases research Methods and protocols Data analysis pipeline Mechanisms of allergic diseases Genomics and transcriptomics Microarray 181,182GWAS 183 and RNA microarray bioinformatics 184,185

Fungal exposome, human health, and unmet needs: A 2022 update with special focus on allergy

“…These methods provide a broad quantification of fungi identified from the environment 90,91 . The metagenomic approach also allows the study of external fungal exposome 5,11,33,83 …”

“…Studies on human mycobiota have taken advantage of the culturomics approach, which can be combined with molecular methods such as metagenomic deep sequencing, allowing the identification of more fungal taxa in patients and healthy controls 5,33,93 …”

“…90,91 The metagenomic approach also allows the study of external fungal exposome. 5,11,33,83 A third approach gaining momentum addresses fungal exposome using statistical modeling of airborne particles based on the study of air-mass movements categorized in spatio-temporal patterns of connectivity. This approach might alleviate the labor-intensive classical identification of airborne fungal spores, and eliminate the potential bias linked to the choice of the air sampling site.…”

“…Fungi are also a major cause of human diseases, including type I (IgE), type III (IgG), and type IV (T cells) hypersensitivity 4 . Long‐known unmet needs in the field of fungal hypersensitivity are increasingly addressed by new technologies, as highlighted in EAACI position papers 5–7 . The aim of this position paper is to summarize our current understanding of fungi, their place in the human exposome, their involvement in hypersensitivity diseases, and to provide an overview of the tools available for their investigation and of the currently unmet needs.…”

The Role of Systems Biology in Deciphering Asthma Heterogeneity

“…Machine learning approaches explore asthma heterogeneity to determine endotypes that correlate with the sub-phenotypes of asthma and allergy using mathematical models on genomic, transcriptomic, and proteomic data [ 33 ]. There is a great potential to maximize a single omics approach utilization by integrating them with other omics [ 131 ]. For example, recently, merged affinity network association clustering (MANAclust), a coding-free automated pipeline enabling the integration of categorical and numeric data spanning clinical and multi-omic profiles for unsupervised clustering to identify disease subsets was applied to a clinically and multi-omically phenotyped asthma cohort and was able to identify clinically and molecularly distinct clusters, including heterogeneous groups of “healthy controls” and viral and allergy-driven subsets of asthmatic subjects [ 43 ].…”