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蛋白质组学驱动因素的奇异流形模型用于模拟炎症性肠病状态的演变。

Singular manifolds of proteomic drivers to model the evolution of inflammatory bowel disease status.

机构信息

Université Sorbonne Paris Nord, LAGA, CNRS, UMR 7539, Laboratoire d'excellence Inflamex, F-93430, Villetaneuse, France.

INSERM, Research Centre of Inflammation, Laboratoire d'excellence Inflamex, BP 416, Paris, France.

出版信息

Sci Rep. 2020 Nov 4;10(1):19066. doi: 10.1038/s41598-020-76011-7.

Abstract

The conditions used to describe the presence of an immune disease are often represented by interaction graphs. These informative, but intricate structures are susceptible to perturbations at different levels. The mode in which that perturbation occurs is still of utmost importance in areas such as cell reprogramming and therapeutics models. In this sense, module identification can be useful to well characterise the global graph architecture. To help us with this identification, we perform topological overlap-related measures. Thanks to these measures, the location of highly disease-specific module regulators is possible. Such regulators can perturb other nodes, potentially causing the entire system to change behaviour or collapse. We provide a geometric framework explaining such situations in the context of inflammatory bowel diseases (IBD). IBD are severe chronic disorders of the gastrointestinal tract whose incidence is dramatically increasing worldwide. Our approach models different IBD status as Riemannian manifolds defined by the graph Laplacian of two high throughput proteome screenings. It also identifies module regulators as singularities within the manifolds (the so-called singular manifolds). Furthermore, it reinterprets the characteristic nonlinear dynamics of IBD as compensatory responses to perturbations on those singularities. Then, particular reconfigurations of the immune system could make the disease status move towards an innocuous target state.

摘要

用于描述免疫疾病存在的条件通常由相互作用图表示。这些信息丰富但复杂的结构容易受到不同层次的干扰。在细胞重编程和治疗模型等领域,干扰发生的方式仍然至关重要。在这种意义下,模块识别对于全面描述全局图形结构很有用。为了帮助我们进行这种识别,我们执行拓扑重叠相关的措施。借助这些措施,可以定位高度特定于疾病的模块调节剂。这些调节剂可以干扰其他节点,可能导致整个系统改变行为或崩溃。我们提供了一个几何框架,解释了炎症性肠病(IBD)背景下的这种情况。IBD 是一种严重的慢性胃肠道疾病,其发病率在全球范围内急剧上升。我们的方法将不同的 IBD 状态建模为通过两个高通量蛋白质组筛选的图拉普拉斯定义的黎曼流形。它还将模块调节剂识别为流形内的奇点(所谓的奇异流形)。此外,它将 IBD 的特征非线性动力学重新解释为对这些奇点上的干扰的补偿反应。然后,免疫系统的特定重新配置可能会使疾病状态朝着无害的目标状态发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71dd/7643119/8db3bf612853/41598_2020_76011_Fig1_HTML.jpg

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