Forkhead box N1 (FOXN1) is the master transcriptional regulator of thymic epithelial cells (TECs), quintessential for T cell development. Well-defined biallelic FOXN1 loss-of-function variants lead to congenital athymia. However, most patients who present with low T cell numbers due to FOXN1 mutations have single allelic variants, the majority of which are of unknown significance. For some individuals, their T cell numbers can improve over a prolonged period, often months to years. The mechanistic basis by which single-allele FOXN1 variants can disrupt thymopoiesis remains poorly understood. To address this knowledge gap, we developed a multi-platform strategy to define how specific human FOXN1 mutations cause T cell lymphopenia due to TEC dysfunctions.
Human FOXN1 variants were screened for transcriptional activity with reporter assays, cytoplasmic versus nuclear distributions, and the potential for aggregation or co-association. Allelic expression patterns are currently being assessed with mouse models developed to genocopy selected human FOXN1 mutations.
Transcriptional reporter assays effectively categorize human FOXN1 variants into complete, partial, and gain-of-function mutations. Both partial loss- and gain-of-function can reduce thymic T cell output. A subset of the human FOXN1 variants function as dominant negatives, antagonizing the function of the wild-type allele. However, this accounts for the effects of a subset of variants, indicating that other mechanisms are involved. Current experiments are addressing whether particular human FOXN1 variants express more stable mRNA transcripts that compete with the normal transcript and/or whether this gene is governed by monoallelic expression.
Human FOXN1 mutations can be effectively categorized into loss- and gain-of-function, which explains the clinical phenotype of low T cell numbers due to thymic hypoplasia. However, other allelic variants can function as dominant negative or may undergo a monoallelic expression, also leading to a T cell lymphopenia. Such a combinatorial approach will lead to better classifications, enabling more informed clinical interventions.
