Table 1 . Definitions of clinical outcomes following oral immunotherapy9 for the purposes of this review.
Traditional approaches to investigate immune mechanisms have studied a few candidate genes or pathways at a time. Recent mechanistic studies in the last decade, however, have harnessed the power of RNA sequencing (RNAseq) to investigate mechanisms of food allergy and outcomes following OIT at a genome-wide scale. The majority of RNAseq studies have focused on differential gene expression (DGE), however this approach is limited, because it does not account for changes in the structure of the underlying gene networks10.
Genes do not exist nor act in isolation but must work together in a coordinated fashion to achieve complex immune functions. Approaches to study genes within their full molecular context are necessary to understand the global organization and function of the gene expression program and unveil the dynamic molecular states that underpin clinical states. Towards this goal, systems biology methods including gene co-expression network analysis works backwards from gene expression profiles to reconstruct the global connectivity structure and functional organization of the gene expression program. This approach is well suited to uncovering novel patterns of gene expression underlying complex diseases like food allergies, which are mediated by multiple cellular and molecular pathways acting together to mount a response. This systems-level approach clusters co-expressed genes into modules that are enriched for genes which are associated with specific biological functions and pathways. Once modules are defined, researchers can investigate the connectivity of genes within these modules, based on the sum of their pairwise co-expression relationships with all other genes. The degree of connectivity, or the number of edges (co-expression relationships) a gene has within a module, serves as a useful metric in prioritizing genes which are more strongly associated with the module and have potential regulatory functions, and accordingly are hypothesized to play pivotal roles in regulating or driving the biological processes associated with the module. Identification of modules and highly connected genes (nodes) within modules thereby provides insights into the coordinated regulation of genes within the context of larger biological systems and can shed light on the molecular underpinnings of diseases. For example, by comparing networks from individuals with remission following OIT and those who fail to achieve remission, it may be possible to pinpoint key regulators of, and changes to, dysregulated pathways leading to this outcome.
Emerging technologies such as single cell RNA sequencing (scRNAseq) are now available that enable a much deeper understanding of complex disease mechanisms at single cell resolution. For example, single cell data can be leveraged to infer cell-to-cell interactions that are mediated via ligand-receptor pairs11. Moreover, gene regulatory networks can be constructed between transcription factors and target genes in a cell-type specific manner12. Trajectory inference can be employed to study dynamic biological processes and map individual immune cells into a pseudotemporal order based on their progression through the activation process13. While the application of this technology to samples from individuals undergoing OIT is still in its infancy, scRNAseq has been applied to study shifts in allergen-reactive CD4+ T cells in a few studies.