Understanding allergic multimorbidity within the non-eosinophilic interactome

Distinction between rhinitis alone and rhinitis with asthma using interactomics

The concept of "one-airway-one-disease", coined over 20 years ago, may be an over-simplification of the links between allergic diseases. Genomic studies suggest that rhinitis alone and rhinitis with asthma are operated by distinct pathways. In this MeDALL (Mechanisms of the Development of Allergy) study, we leveraged the information of the human interactome to distinguish the molecular mechanisms associated with two phenotypes of allergic rhinitis: rhinitis alone and rhinitis in multimorbidity with asthma. We observed significant differences in the topology of the interactomes and in the pathways associated to each phenotype. In rhinitis alone, identified pathways included cell cycle, cytokine signalling, developmental biology, immune system, metabolism of proteins and signal transduction. In rhinitis and asthma multimorbidity, most pathways were related to signal transduction. The remaining few were related to cytokine signalling, immune system or developmental biology. Toll-like receptors and IL-17-mediated signalling were identified in rhinitis alone, while IL-33 was identified in rhinitis in multimorbidity. On the other hand, few pathways were associated with both phenotypes, most being associated with signal transduction pathways including estrogen-stimulated signalling. The only immune system pathway was FceRI-mediated MAPK activation. In conclusion, our findings suggest that rhinitis alone and rhinitis and asthma multimorbidity should be considered as two distinct diseases.

Multimorbidities in Allergic Rhinitis-Current Evidence from Epidemiological Studies, Treatment Trials, and Molecular Data

PURPOSE OF REVIEW

Given that allergic rhinitis (AR) commonly coexists with other diseases, the present narrative review attempts a brief presentation of current theories on multimorbidities in relation to phenotypes, genotypes, age, and treatment responses with the term "multimorbidities" indicating the uncertainty regarding the primary defect, organ, or pathophysiologic mechanism involved.

RECENT FINDINGS

Though age-related manifestations allow for the generation of several hypotheses on AR's specific mechanisms, the various theories regarding the initiation or the aggravation of atopic disorders have yet to be proved. Multimorbid AR seems to have a different genetic basis from "stand-alone" AR as well a more severe phenotype. Most studies on the treatment of AR and its multimorbidities focus on allergen immunotherapy, which improves the atopic symptoms and may play a preventive role in the onset of new allergen sensitizations. The use of biological factors may also have a beneficial effect, even though it has currently been approved only for some comorbidities of AR, such as asthma. Employing the use of phenotypes and genotypes concerning multimorbidity broadens current knowledge, but further research is needed to develop diagnostic, stratificational, and predictive algorithms for single and multimorbid allergic diseases (Fig. 1). The real-time data obtained by mobile apps and the new insights on the pathophysiology of AR and its comorbidities will permit both timed preventive measures and better individualized and effective antiallergic treatment. Fig. 1 Current concepts and future trends in diagnosis and management of multimorbid allergic rhinitis.

Real-world data using mHealth apps in rhinitis, rhinosinusitis and their multimorbidities

Digital health is an umbrella term which encompasses eHealth and benefits from areas such as advanced computer sciences. eHealth includes mHealth apps, which offer the potential to redesign aspects of healthcare delivery. The capacity of apps to collect large amounts of longitudinal, real-time, real-world data enables the progression of biomedical knowledge. Apps for rhinitis and rhinosinusitis were searched for in the Google Play and Apple App stores, via an automatic market research tool recently developed using JavaScript. Over 1500 apps for allergic rhinitis and rhinosinusitis were identified, some dealing with multimorbidity. However, only six apps for rhinitis (AirRater, AllergyMonitor, AllerSearch, Husteblume, MASK-air and Pollen App) and one for rhinosinusitis (Galenus Health) have so far published results in the scientific literature. These apps were reviewed for their validation, discovery of novel allergy phenotypes, optimisation of identifying the pollen season, novel approaches in diagnosis and management (pharmacotherapy and allergen immunotherapy) as well as adherence to treatment. Published evidence demonstrates the potential of mobile health apps to advance in the characterisation, diagnosis and management of rhinitis and rhinosinusitis patients.

Integrating Multimorbidity into a Whole-Body Understanding of Disease Using Spatial Genomics

Multimorbidity is characterized by multidimensional complexity emerging from interactions between multiple diseases across levels of biological (including genetic) and environmental determinants and the complex array of interactions between and within cells, tissues and organ systems. Advances in spatial genomic research have led to an unprecedented expansion in our ability to link alterations in genome folding with changes that are associated with human disease. Studying disease-associated genetic variants in the context of the spatial genome has enabled the discovery of transcriptional regulatory programmes that potentially link dysregulated genes to disease development. However, the approaches that have been used have typically been applied to uncover pathological molecular mechanisms occurring in a specific disease-relevant tissue. These forms of reductionist, targeted investigations are not appropriate for the molecular dissection of multimorbidity that typically involves contributions from multiple tissues. In this perspective, we emphasize the importance of a whole-body understanding of multimorbidity and discuss how spatial genomics, when integrated with additional omic datasets, could provide novel insights into the molecular underpinnings of multimorbidity.

A Computational Framework to Identify Cross Association Between Complex Disorders by Protein-protein Interaction Network Analysis

Objective:

It is a known fact that numerous complex disorders do not happen in isolation indicating the plausible set of shared causes common to several different sicknesses. Hence, analysis of comorbidity can be utilized to explore the association between several disorders. In this study, we have proposed a network-based computational approach, in which genes are organized based on the topological characteristics of the constructed Protein-Protein Interaction Network (PPIN) followed by a network prioritization scheme, to identify distinctive key genes and biological pathways shared among diseases.

Methods:

The proposed approach is initiated from constructed PPIN of any randomly chosen disease genes in order to infer its associations with other diseases in terms of shared pathways, coexpression, co-occurrence etc. For this, initially, proteins associated to any disease based on random choice were identified. Secondly, PPIN is organized through topological analysis to define hub genes. Finally, using a prioritization algorithm a ranked list of newly predicted multimorbidity-associated proteins is generated. Using Gene Ontology (GO), cellular pathways involved in multimorbidity-associated proteins are mined.

Result and Conclusion:

: The proposed methodology is tested using three disorders, namely Diabetes, Obesity and blood pressure at an atomic level and the results suggest the comorbidity of other complex diseases that have associations with the proteins included in the disease of present study through shared proteins and pathways. For diabetes, we have obtained key genes like GAPDH, TNF, IL6, AKT1, ALB, TP53, IL10, MAPK3, TLR4 and EGF with key pathways like P53 pathway, VEGF signaling pathway, Ras Pathway, Interleukin signaling pathway, Endothelin signaling pathway, Huntington disease etc. Studies on other disorders such as obesity and blood pressure also revealed promising results.

New concepts in pediatric rhinitis

Rhinitis-and especially allergic rhinitis (AR)-remains the most frequent hypersensitivity condition, affecting up to a quarter of the population and impacting the quality of life of individual patients and the health economy. Data, especially with respect to underlying pathophysiologic mechanisms, mainly derive from studies on adults and are subsequently extrapolated to the pediatric population. Therapeutic algorithms for children with rhinitis are long based on the same principles as in adults. We explore and describe novel aspects of rhinitis, ranging from mechanisms to disease classification, phenotypes, diagnostic and monitoring tools, and the use of treatments, with a focus on the traits of pediatric age groups.