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

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

PROVIDER: PRJNA964443 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR24360600_1.fastq.gz	Fastqsanger.gz
	SRR24360600_2.fastq.gz	Fastqsanger.gz
	SRR24360601_1.fastq.gz	Fastqsanger.gz
	SRR24360601_2.fastq.gz	Fastqsanger.gz
	SRR24360602_1.fastq.gz	Fastqsanger.gz

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

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

| PRJNA964441 | ENA

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

Project description:To link upstream IL-6 and IL-10-induced JAK-STAT signaling to downstream gene expression in bone marrow derived macrophages (BMDMs). Bulk RNAseq was done on cytokine-stimulated BMDM in order to profile cytokine-induced response.

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

Project description:Predicting gene level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning framework II

| PRJNA964442 | ENA

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

Project description:To inhibit JAK2 upon IL-6 and IL-10-induced JAK-STAT signaling and quantify gene expression in bone marrow derived macrophages (BMDMs). Bulk RNAseq was done on cytokine-stimulated BMDM with and without Fedratinib treatment in order to profile the JAK2-inhibited cytokine-induced response.

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

Project description:This SuperSeries is composed of the SubSeries listed below.

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

Project description: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.

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

Project description: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.

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Project description:Immune cells need to sustain a state of constant alertness over a lifetime. Yet, little is known about the regulatory processes that control the fluent and fragile balance that is called homeostasis. Here we demonstrate that JAK-STAT signaling, beyond its role in immune responses, is a major regulator of immune cell homeostasis. We investigated JAK-STAT-mediated transcription and chromatin accessibility across 12 mouse models, including knockouts of all STAT transcription factors and of the TYK2 kinase. Baseline JAK-STAT signaling was detected in CD8+ T cells and macrophages of unperturbed mice – but abrogated in the knockouts and in unstimulated immune cells deprived of their normal tissue context. We observed diverse transcription-regulatory programs, including gene regulation by STAT2 and IRF9 independent of STAT1. In summary, our large-scale dataset and integrative analysis of JAK-STAT mutant and wildtype mice uncovered a crucial role of JAK-STAT signaling in unstimulated immune cells, where it contributes to a poised epigenetic and transcription-regulatory state and helps prepare these cells for rapid response to immune stimuli.

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