Complex-mediated evasion: modeling defense against antimicrobial peptides with application to human-pathogenic fungus Candida albicans.

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Yann Bachelot, Anastasia Solomatina, Marc Thilo Figge
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引用次数: 0

Abstract

Understanding the complex interplay between host and pathogen during infection is critical for developing diagnostics and improving therapeutic interventions. Among the diverse arsenal employed by the host, antimicrobial peptides (AMP) play a key role in the defense against pathogens. We propose an immune evasion mechanism termed "Complex-mediated evasion" (CME), that allows pathogens to protect themselves against AMP and investigate it through mathematical modeling and computer simulations. To achieve CME, we hypothesize that the pathogen secretes defense molecules that bind AMP. When bound within the complex, AMP are unable to harm the pathogen. Due to molecular gradients, complexes may diffuse away from the pathogen, enhancing the protective effect of the mechanism by decreasing the concentration of AMP in the vicinity of the pathogen. We establish a mathematical model to (i) explore the sensitivity of the mechanism to various parameters and (ii) simulate the immune evasion of the human-pathogenic fungus Candida albicans.

复合物介导的逃避:模拟抗微生物肽的防御与应用于人类致病真菌白色念珠菌。
了解感染期间宿主和病原体之间复杂的相互作用对于发展诊断和改进治疗干预措施至关重要。在宿主使用的多种武器库中,抗菌肽(AMP)在防御病原体中起着关键作用。我们提出了一种称为“复合物介导的逃避”(Complex-mediated evasion, CME)的免疫逃避机制,该机制允许病原体保护自己免受AMP的侵害,并通过数学建模和计算机模拟对其进行研究。为了实现CME,我们假设病原体分泌结合AMP的防御分子。当AMP结合在复合物内时,AMP无法伤害病原体。由于分子梯度,复合物可能向远离病原体的方向扩散,从而通过降低病原体附近AMP的浓度来增强该机制的保护作用。我们建立了一个数学模型,以(i)探索该机制对各种参数的敏感性,(ii)模拟人类病原真菌白色念珠菌的免疫逃避。
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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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