bioRxivImmunology
Transcriptional regulatory networks that promote and restrict identities and functions of intestinal innate lymphoid cells
M. Pokrovskii, J. A. Hall, D. E. Ochayon, R. Yi, N. S. Chaimowitz, H. Seelamneni, N. Carriero, A. Watters, S. N. Waggoner, D. R. Littman, R. Bonneau, E. R. Miraldi
Abstract
Innate lymphoid cells (ILCs) can be subdivided into several distinct cytokine-secreting lineages that promote tissue homeostasis and immune defense but also contribute to inflammatory diseases. Accumulating evidence suggests that ILCs, similarly to other immune populations, are capable of phenotypic and functional plasticity in response to infectious or environmental stimuli. Yet the transcriptional circuits that control ILC identity and function are largely unknown. Here we integrate gene expression and chromatin accessibility data to infer transcriptional regulatory networks within intestinal type 1, 2, and 3 ILCs. We predict the \"core\" sets of transcription-factor (TF) regulators driving each ILC subset identity, among which only a few TFs were previously known. To assist in the interpretation of these networks, TFs were organized into cooperative clusters, or modules that control gene programs with distinct functions. The ILC network reveals extensive alternative-lineage-gene repression, whose regulation may explain reported plasticity between ILC subsets. We validate new roles for c-MAF and BCL6 as regulators affecting the type 1 and type 3 ILC lineages. Manipulation of TF pathways identified here might provide a novel means to selectively regulate ILC effector functions to alleviate inflammatory disease or enhance host tolerance to pathogenic microbes or noxious stimuli. Our results will enable further exploration of ILC biology, while our network approach will be broadly applicable to identifying key cell state regulators in other in vivo cell populations.
- organism: mouse
- assay: scRNA-seq
- assay: ATAC-seq
- research focus: gene regulatory network inference
- research focus: cell type annotation
- approach: statistical model
- approach: transformer / foundation model
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