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振动光谱结合机器学习揭示了合理设计的 TLR4 激动剂引起的细胞效应。

Vibrational spectroscopy coupled with machine learning sheds light on the cellular effects induced by rationally designed TLR4 agonists.

机构信息

Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza, 2, 20126, Milano, Italy.

Center for Cooperative Research in Biosciences (CIC bioGUNE), Basque Research and Technology Alliance (BRTA), 48160, Derio, Bizkaia, Spain; Ikerbasque, Basque Foundation for Science, Plaza Euskadi 5, 48009, Bilbao, Bizkaia, Spain.

出版信息

Talanta. 2024 Aug 1;275:126104. doi: 10.1016/j.talanta.2024.126104. Epub 2024 Apr 17.

Abstract

In this work, we present the potential of Fourier transform infrared (FTIR) microspectroscopy to compare on whole cells, in an unbiased and untargeted way, the capacity of bacterial lipopolysaccharide (LPS) and two rationally designed molecules (FP20 and FP20Rha) to activate molecular circuits of innate immunity. These compounds are important drug hits in the development of vaccine adjuvants and tumor immunotherapeutics. The biological assays indicated that FP20Rha was more potent than FP20 in inducing cytokine production in cells and in stimulating IgG antibody production post-vaccination in mice. Accordingly, the overall significant IR spectral changes induced by the treatment with LPS and FP20Rha were similar, lipids and glycans signals being the most diagnostic, while the effect of the less potent molecule FP20 on cells resulted to be closer to control untreated cells. We propose here the use of FTIR spectroscopy supported by artificial intelligence (AI) to achieve a more holistic understanding of the cell response to new drug candidates while screening them in cells.

摘要

在这项工作中,我们展示了傅里叶变换红外(FTIR)显微光谱学的潜力,能够以无偏和非靶向的方式比较细菌脂多糖(LPS)和两种合理设计的分子(FP20 和 FP20Rha)在激活先天免疫分子回路方面的能力。这些化合物是疫苗佐剂和肿瘤免疫治疗开发的重要药物靶点。生物测定表明,FP20Rha 在诱导细胞产生细胞因子和在接种疫苗后刺激小鼠 IgG 抗体产生方面比 FP20 更有效。因此,用 LPS 和 FP20Rha 处理引起的整体显著的 IR 光谱变化相似,脂质和聚糖信号最具诊断性,而较弱分子 FP20 对细胞的影响更接近对照未处理的细胞。在这里,我们提出使用 FTIR 光谱学,并辅以人工智能(AI),以在细胞中筛选新的药物候选物时,实现对细胞对新药物候选物反应的更全面理解。

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