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

The Janus kinase (JAK)-signal transducer and activator of transcription (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 framework to make global cytokine-induced gene predictions from STAT phosphorylation dynamics, modeling macrophage responses to interleukin (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 cytokine-specific genes associated with late pSTAT3 time frames and a preferential pSTAT1 reduction upon JAK2 inhibition. We predicted and validated the impact of JAK2 inhibition on gene expression, identifying genes that were sensitive or insensitive to JAK2 variation. Thus, we successfully linked STAT signaling dynamics to gene expression to support future efforts targeting pathology-associated STAT-driven gene sets. This serves as a first step in developing multi-level prediction models to understand and perturb gene expression outputs from signaling systems. A record of this paper's transparent peer review process is included in the supplemental information.

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

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 or insensitive to JAK2 variation. Thus, we successfully linked STAT signaling dynamics to gene expression to support future efforts targeting pathology-associated STAT-driven gene sets. This serves as a first step in developing multi-level prediction models to understand and perturb gene expression outputs from signaling systems.

CISH attenuates homeostatic cytokine signaling to promote lung-specific macrophage programming and function.

Tissue-specific cytokine stimuli orchestrate specialized homeostatic functions of resident macrophages. In the lung, steady-state signaling by the cytokine GM-CSF is critical for alveolar macrophage (AM) development and function. Here, we showed that CISH, a suppressor of cytokine signaling (SOCS) family member that is acutely induced by diverse cytokine stimuli in many tissues, was expressed constitutively in AMs in response to steady-state GM-CSF signaling. Cish deficiency led to the generation of foamy AMs and the accumulation of pulmonary surfactant. These phenotypic changes were associated with enhanced activation of STAT5, AKT, and ERK and increased expression of the gene encoding the transcription factor GATA2. RNA-seq analysis of Cish−/− AMs revealed a set of dysregulated immune and lipid-process modules, including the increased expression of genes enriched for GATA2-binding motifs. Last, Cish-deficient, bone marrow­derived macrophages showed increased Gata2 expression and accumulated more lipid upon incubation with bronchoalveolar lavage fluid compared with Cish-sufficient cells. Thus, CISH is part of a feedback loop that constrains homeostatic cytokine signaling and Gata2 expression to maintain AM identity and function.