细菌肽聚糖穿过胎盘诱导胎儿神经增殖和出生后行为异常

IF 16.3 1区 数学 Q1 MATHEMATICS Acta Numerica Pub Date : 2016-03-09 DOI:10.1016/j.chom.2016.02.009
Jessica Humann, Beth Mann, Geli Gao, Philip Moresco, Joseph Ramahi, Lip Nam Loh, Arden Farr, Yunming Hu, Kelly Durick-Eder, Sophie A Fillon, Richard J Smeyne, Elaine I Tuomanen
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引用次数: 0

摘要

孕期母体感染与胎儿的不良结局有关,包括产后认知障碍。然而,其潜在机制尚不清楚。我们发现,细菌细胞壁肽聚糖(CW)是TLR2的通用PAMP,可穿过小鼠胎盘进入发育中的胎儿大脑。与成人不同的是,接触过 CW 的胎儿大脑没有显示任何炎症或神经元死亡的迹象。相反,神经元转录因子 FoxG1 被诱导,并观察到神经元增殖导致皮质板中的神经元密度增加了 50%。对怀孕母鼠进行细菌感染,然后进行抗生素治疗,释放 CW,也会产生同样的结果。神经增殖需要 TLR2,并通过胎儿神经元前体细胞和 TLR2/6(而非 TLR2/1)配体在体外重现。胎儿神经增生反应与接触化武的幼崽出生后的异常认知行为相关。因此,细菌化武-TLR2 信号轴会影响胎儿的神经发育,并可能是出生后认知障碍的原因。
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Bacterial Peptidoglycan Traverses the Placenta to Induce Fetal Neuroproliferation and Aberrant Postnatal Behavior

Maternal infection during pregnancy is associated with adverse outcomes for the fetus, including postnatal cognitive disorders. However, the underlying mechanisms are obscure. We find that bacterial cell wall peptidoglycan (CW), a universal PAMP for TLR2, traverses the murine placenta into the developing fetal brain. In contrast to adults, CW-exposed fetal brains did not show any signs of inflammation or neuronal death. Instead, the neuronal transcription factor FoxG1 was induced, and neuroproliferation leading to a 50% greater density of neurons in the cortical plate was observed. Bacterial infection of pregnant dams, followed by antibiotic treatment, which releases CW, yielded the same result. Neuroproliferation required TLR2 and was recapitulated in vitro with fetal neuronal precursor cells and TLR2/6, but not TLR2/1, ligands. The fetal neuroproliferative response correlated with abnormal cognitive behavior in CW-exposed pups following birth. Thus, the bacterial CW-TLR2 signaling axis affects fetal neurodevelopment and may underlie postnatal cognitive disorders.

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来源期刊
Acta Numerica
Acta Numerica MATHEMATICS-
CiteScore
26.00
自引率
0.70%
发文量
7
期刊介绍: Acta Numerica stands as the preeminent mathematics journal, ranking highest in both Impact Factor and MCQ metrics. This annual journal features a collection of review articles that showcase survey papers authored by prominent researchers in numerical analysis, scientific computing, and computational mathematics. These papers deliver comprehensive overviews of recent advances, offering state-of-the-art techniques and analyses. Encompassing the entirety of numerical analysis, the articles are crafted in an accessible style, catering to researchers at all levels and serving as valuable teaching aids for advanced instruction. The broad subject areas covered include computational methods in linear algebra, optimization, ordinary and partial differential equations, approximation theory, stochastic analysis, nonlinear dynamical systems, as well as the application of computational techniques in science and engineering. Acta Numerica also delves into the mathematical theory underpinning numerical methods, making it a versatile and authoritative resource in the field of mathematics.
期刊最新文献
Splitting methods for differential equations Adaptive finite element methods The geometry of monotone operator splitting methods Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning Optimal experimental design: Formulations and computations
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