肠道内纤维和粘蛋白降解剂之间竞争的数学模型为粘液变稀提供了可能的解释

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Accounts of Chemical Research Pub Date : 2024-04-10 DOI:10.1016/j.jtbi.2024.111824
Thulasi Jegatheesan , Arun S. Moorthy , Hermann J. Eberl
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引用次数: 0

摘要

人类肠道微生物群依赖复杂碳水化合物(聚糖)获取能量和生长,主要是膳食纤维和宿主衍生的粘蛋白。我们在人类结肠的两室恒温箱模型中引入了一个糖类通才和粘蛋白专才的数学模型。我们的目标是描述膳食纤维和粘蛋白供应对肠道生态系统中粘蛋白降解物种丰度的影响。目前包括聚糖酶降解的数学肠道反应器模型没有区分聚糖类型及其降解物。我们提出的模型区分了既能降解膳食纤维又能降解粘蛋白的通用型物种和只能降解粘蛋白的专业型物种。结肠粘液屏障的完整性对人类的整体健康和福祉至关重要,而粘蛋白专家 Akkermanisa muciniphila 与健康的粘液层息息相关。竞争,尤其是专科菌和普通菌(如 Bacteroides thetaiotaomicron)之间的竞争,可能会导致粘液层被侵蚀,尤其是在缺乏膳食纤维的时期。我们的模型将结肠视为一个肠道反应器系统,将其分为代表肠道内腔和粘液的两个区段,从而产生了一个复杂的常微分方程系统,其参数空间很大且不确定。为了了解模型参数对长期行为的影响,我们采用了随机森林分类器这种有监督的机器学习方法。此外,我们还利用基于方差的敏感性分析来确定稳态值对模型参数输入变化的敏感性。通过构建这个模型,我们可以研究控制肠道微生物群组成和功能的潜在机制,而不受干扰因素的影响。
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A mathematical model of competition between fiber and mucin degraders in the gut provides a possible explanation for mucus thinning

The human gut microbiota relies on complex carbohydrates (glycans) for energy and growth, primarily dietary fiber and host-derived mucins. We introduce a mathematical model of a glycan generalist and a mucin specialist in a two-compartment chemostat model of the human colon. Our objective is to characterize the influence of dietary fiber and mucin supply on the abundance of mucin-degrading species within the gut ecosystem. Current mathematical gut reactor models that include the enzymatic degradation of glycans do not differentiate between glycan types and their degraders. The model we present distinguishes between a generalist that can degrade both dietary fiber and mucin, and a specialist species that can only degrade mucin. The integrity of the colonic mucus barrier is essential for overall human health and well-being, with the mucin specialist Akkermanisa muciniphila being associated with a healthy mucus layer. Competition, particularly between the specialist and generalists like Bacteroides thetaiotaomicron, may lead to mucus layer erosion, especially during periods of dietary fiber deprivation. Our model treats the colon as a gut reactor system, dividing it into two compartments that represent the lumen and the mucus of the gut, resulting in a complex system of ordinary differential equations with a large and uncertain parameter space. To understand the influence of model parameters on long-term behavior, we employ a random forest classifier, a supervised machine learning method. Additionally, a variance-based sensitivity analysis is utilized to determine the sensitivity of steady-state values to changes in model parameter inputs. By constructing this model, we can investigate the underlying mechanisms that control gut microbiota composition and function, free from confounding factors.

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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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