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

IF 9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Neha Cheemalavagu, Karsen E. Shoger, Yuqi M. Cao, Brandon A. Michalides, Samuel A. Botta, James R. Faeder, Rachel A. Gottschalk
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引用次数: 0

Abstract

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.

Abstract Image

利用从机制到机器的学习框架预测基因水平对JAK-STAT信号扰动的敏感性
Janus 激酶(JAK)-信号转导和激活转录因子(STAT)通路通过数量有限的分子成分整合了复杂的细胞因子信号,这激发了人们为阐明 STAT 转录因子功能的多样性和特异性所做的大量努力。我们开发了一个计算框架,根据 STAT 磷酸化动态预测细胞因子诱导的全局基因,模拟巨噬细胞对白细胞介素(IL)-6 和 IL-10 的反应。我们的机械学习模型确定了与晚期 pSTAT3 时间框架相关的细胞因子特异性基因,以及 JAK2 抑制后 pSTAT1 的优先减少。我们预测并验证了 JAK2 抑制对基因表达的影响,确定了对 JAK2 变化敏感或不敏感的基因。因此,我们成功地将 STAT 信号动态与基因表达联系起来,为今后针对病理相关 STAT 驱动基因组的研究提供了支持。这是开发多层次预测模型以了解和扰乱信号系统基因表达输出的第一步。这篇论文的同行评审过程非常透明,相关记录见补充信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cell Systems
Cell Systems Medicine-Pathology and Forensic Medicine
CiteScore
16.50
自引率
1.10%
发文量
84
审稿时长
42 days
期刊介绍: In 2015, Cell Systems was founded as a platform within Cell Press to showcase innovative research in systems biology. Our primary goal is to investigate complex biological phenomena that cannot be simply explained by basic mathematical principles. While the physical sciences have long successfully tackled such challenges, we have discovered that our most impactful publications often employ quantitative, inference-based methodologies borrowed from the fields of physics, engineering, mathematics, and computer science. We are committed to providing a home for elegant research that addresses fundamental questions in systems biology.
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