Samper, N. | bioRxiv Science

bioRxiv2024

Kir6.1, a component of an ATP-sensitive potassium channel, regulates natural killer cell development

AO_SCPLOWBSTRACTC_SCPLOWInvolved in immunity and reproduction, natural killer (NK) cells offer opportunities to develop new immunotherapies to treat infections and cancer or to alleviate pregnancy complications. Most current strategies use cytokines or antibodies to enhance NK-cell function, but none use ion channel modulators, which are widely used in clinical practice to treat hypertension, diabetes, epilepsy, and other conditions. Little is known about ion channels in NK cells. We show that Kcnj8, which codes for the Kir6.1 subunit of a certain type of ATP-sensitive potassium (KATP) channel, is highly expressed in murine splenic and uterine NK cells compared to other K+ channels previously identified in NK cells. Kcnj8 expression is highest in the most mature subset of splenic NK cells (CD27-CD11b+) and in NKG2A+ or Ly49C/I+ educated uterine NK cells. Using patch clamping, we show that a subset of NK cells expresses a current sensitive to the Kir6.1 blocker PNU-37883A. Kcnj8 does not participate in NK cell degranulation in response to tumor cells in vitro or rejection of tumor cells in vivo. Transcriptomics show that genes previously implicated in NK cell development are amongst those differentially expressed in CD27-CD11b+ NK cells deficient of Kcnj8. Indeed, we found that mice with NK-cell specific Kcnj8 gene ablation have fewer CD11b+CD27- and KLRG-1+ NK cells in the bone barrow and spleen. These results show that the KATP subunit Kir6.1 has a key role in NK-cell development.

immunology↗

bioRxiv2020

Model-based analysis of polymorphisms in an enhancer reveals cis-regulatory mechanisms

It is challenging to predict the impact of small genetic changes such as single nucleotide polymorphisms on gene expression, since mechanisms involved in gene regulation and their cis-regulatory encoding are not well-understood. Recent studies have attempted to predict the functional impact of non-coding variants based on available knowledge of cis-regulatory encoding, e.g., transcription factor (TF) motifs. In this work, we explore the relationship between regulatory variants and cis-regulatory encoding from the opposite angle, using the former to inform the latter. We employ sequence-to-expression modeling to resolve ambiguities regarding gene regulatory mechanisms using information about effects of single nucleotide variations in an enhancer. We demonstrate our methodology using a well-studied enhancer of the developmental gene intermediate neuroblasts defective (ind) in D. melanogaster. We first trained the thermodynamics-based model GEMSTAT to relate the neuroectodermal expression pattern of ind to its enhancers sequence, and constructed an ensemble of models that represent different parameter settings consistent with available data for this gene. We then predicted the effects of every possible single nucleotide variation within this enhancer, and compared these to SNP data recorded in the Drosophila Genome Reference Panel. We chose specific SNPs for which different models in the ensemble made conflicting predictions, and tested their effect in vivo. These experiments narrowed in on one mechanistic model as capable of explaining the observed effects. We further confirmed the generalizability of this model to orthologous enhancers and other related developmental enhancers. In conclusion, mechanistic models of cis-regulatory function not only help make specific predictions of variant impact, they may also be learned more accurately using data on variants. STATEMENT OF SIGNIFICANCEA central issue in analyzing variations in the non-coding genome is to interpret their functional impact, and their connections to phenotype differences and disease etiology. Machine learning methods based on statistical modeling have been developed to associate genetic variants to expression changes. However, associations predicted by these models may not be functionally relevant, despite being statisticaly significant. We describe how mathematical modeling of gene expression can be employed to systematically study the non-coding sequence and its relationship to gene expression. We demonstrate our method in a well studied developmental enhancer of the fruitfly. We establish the efficacy of mathematical models in combination with the polymorphism data to reveal new mechanistic insights.

bioinformatics↗