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.