Predicting gene level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning framework.

ABSTRACT: The JAK-STAT pathway integrates complex cytokine signals via a limited number of molecular components, inspiring numerous efforts to clarify the diversity and specificity of STAT transcription factor function. We developed a computational workflow to make global cytokine-induced gene predictions from STAT phosphorylation dynamics, modeling macrophage responses to IL-6 and IL-10, which signal through common STATs, but with distinct temporal dynamics and contrasting functions. Our mechanistic-to-machine learning model identified select cytokine-induced gene sets associated with late pSTAT3 timeframes and a preferential pSTAT1 reduction upon JAK2 inhibition. We predicted and validated the impact of JAK2 inhibition on gene expression, identifying dynamically regulated genes that were sensitive

SUBMITTER: Cheemalavagu N 

PROVIDER: S-EPMC10245690 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature