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脂多糖诱导表观遗传重编程后中性粒细胞迁移模式的双稳态数学模型

Bistable Mathematical Model of Neutrophil Migratory Patterns After LPS-Induced Epigenetic Reprogramming.

作者信息

Ciupe Stanca M, Boribong Brittany P, Kadelka Sarah, Jones Caroline N

机构信息

Department of Mathematics, Virginia Tech, Blacksburg, VA, United States.

Division of Pediatric Pulmonology, Massachusetts General Hospital, Boston, MA, United States.

出版信息

Front Genet. 2021 Feb 23;12:633963. doi: 10.3389/fgene.2021.633963. eCollection 2021.

DOI:10.3389/fgene.2021.633963
PMID:33708241
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7940759/
Abstract

The highly controlled migration of neutrophils toward the site of an infection can be altered when they are trained with lipopolysaccharides (LPS), with high dose LPS enhancing neutrophil migratory pattern toward the bacterial derived source signal and super-low dose LPS inducing either migration toward an intermediary signal or dysregulation and oscillatory movement. Empirical studies that use microfluidic chemotaxis-chip devices with two opposing chemoattractants showed differential neutrophil migration after challenge with different LPS doses. The epigenetic alterations responsible for changes in neutrophil migratory behavior are unknown. We developed two mathematical models that evaluate the mechanistic interactions responsible for neutrophil migratory decision-making when exposed to competing chemoattractants and challenged with LPS. The first model, which considers the interactions between the receptor densities of two competing chemoattractants, their kinases, and LPS, displayed bistability between high and low ratios of primary to intermediary chemoattractant receptor densities. In particular, at equilibrium, we observe equal receptor densities for low LPS (< 15ng/mL); and dominance of receptors for the primary chemoattractant for high LPS (> 15ng/mL). The second model, which included additional interactions with an extracellular signal-regulated kinase in both phosphorylated and non-phosphorylated forms, has an additional dynamic outcome, oscillatory dynamics for both receptors, as seen in the data. In particular, it found equal receptor densities in the absence of oscillation for super-low and high LPS challenge (< 0.4 and 1.1 <LPS< 375 ng/mL); equal receptor densities with oscillatory receptor dynamics for super-low LPS (0.5 < LPS< 1.1ng/mL); and dominance of receptors for the primary chemoattractant for super-high LPS (>376 ng/mL). Predicting the mechanisms and the type of external LPS challenge responsible for neutrophils migration toward pro-inflammatory chemoattractants, migration toward pro-tolerant chemoattractants, or oscillatory movement is necessary knowledge in designing interventions against immune diseases, such as sepsis.

摘要

当中性粒细胞用脂多糖(LPS)进行训练时,其向感染部位的高度受控迁移会发生改变,高剂量LPS增强中性粒细胞向细菌衍生源信号的迁移模式,而超低剂量LPS则诱导其向中间信号迁移或导致失调和振荡运动。使用具有两种相反趋化因子的微流控趋化芯片装置的实证研究表明,用不同剂量LPS刺激后,中性粒细胞的迁移存在差异。导致中性粒细胞迁移行为改变的表观遗传变化尚不清楚。我们开发了两个数学模型,以评估中性粒细胞在暴露于竞争性趋化因子并受到LPS刺激时,其迁移决策背后的机制性相互作用。第一个模型考虑了两种竞争性趋化因子的受体密度、它们的激酶以及LPS之间的相互作用,在初级趋化因子与中间趋化因子受体密度的高低比率之间表现出双稳态。特别是在平衡状态下,我们观察到低LPS(<15ng/mL)时受体密度相等;高LPS(>15ng/mL)时初级趋化因子受体占主导。第二个模型包括与磷酸化和非磷酸化形式的细胞外信号调节激酶的额外相互作用,具有另一种动态结果,即两种受体都出现振荡动力学,正如数据中所见。特别是,它发现在超低和高LPS刺激(<0.4和1.1<LPS<375 ng/mL)且无振荡时受体密度相等;超低LPS(0.5<LPS<1.1ng/mL)时受体密度相等且受体动力学呈振荡;超高LPS(>376 ng/mL)时初级趋化因子受体占主导。预测导致中性粒细胞向促炎趋化因子迁移、向促耐受趋化因子迁移或振荡运动的LPS外部刺激的机制和类型,是设计针对免疫疾病(如败血症)干预措施的必要知识。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bca/7940759/27f1bf844944/fgene-12-633963-g0009.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bca/7940759/e0a3291fd66d/fgene-12-633963-g0006.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bca/7940759/27f1bf844944/fgene-12-633963-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bca/7940759/1fcd57c0d565/fgene-12-633963-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bca/7940759/4fe7fffe1d90/fgene-12-633963-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bca/7940759/d4e18d419d43/fgene-12-633963-g0003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bca/7940759/e6fb5ea7b3a1/fgene-12-633963-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bca/7940759/e0a3291fd66d/fgene-12-633963-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bca/7940759/4a0844857f83/fgene-12-633963-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bca/7940759/e6c08e01060e/fgene-12-633963-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bca/7940759/27f1bf844944/fgene-12-633963-g0009.jpg

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