Computational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, Calle Doctor Beguiristain S/N, 20014, San Sebastián, Spain.
Computational Biomedicine Data Analysis Platform, Biodonostia Health Research Institute, Calle Doctor Beguiristain S/N, 20014, San Sebastián, Spain.
Sci Rep. 2020 Nov 3;10(1):18923. doi: 10.1038/s41598-020-75014-8.
While the central common feature of the neurodegenerative diseases (NDs) is the accumulation of misfolded proteins, they share other pathogenic mechanisms. However, we miss an explanation for the onset of the NDs. The mechanisms through which genetic mutations, present from conception are expressed only after several decades of life are unknown. We aim to find clues on the complexity of the disease onset trigger of the different NDs expressed in the number of steps of factors related to a disease. We collected brain autopsies on diseased patients with NDs, and found a dynamic increase of the ND multimorbidity with the advance of age. Together with the observation that the NDs accumulate multiple misfolded proteins, and the same misfolded proteins are involved in more than one ND, motivated us to propose a model for a genealogical tree of the NDs. To collect the dynamic data needed to build the tree, we used a Big-data approach that searched automatically epidemiological datasets for age-stratified incidence of NDs. Based on meta-analysis of over 400 datasets, we developed an algorithm that checks whether a ND follows a multistep model, finds the number of steps necessary for the onset of each ND, finds the number of common steps with other NDs and the number of specific steps of each ND, and builds with these findings a parsimony tree of the genealogy of the NDs. The tree discloses three types of NDs: the stem NDs with less than 3 steps; the trunk NDs with 5 to 6 steps; and the crown NDs with more than 7 steps. The tree provides a comprehensive understanding of the relationship across the different NDs, as well as a mathematical framework for dynamic adjustment of the genealogical tree of the NDs with the appearance of new epidemiological studies and the addition of new NDs to the model, thus setting the basis for the search for the identity and order of these steps. Understanding the complexity, or number of steps, of factors related to disease onset trigger is important prior deciding to study single factors for a multiple steps disease.
虽然神经退行性疾病(NDs)的中心共同特征是错误折叠蛋白的积累,但它们也存在其他致病机制。然而,我们仍未找到 NDs 发病的解释。遗传突变的机制,从出生就存在,仅在生命的几十年后才表现出来,目前仍不清楚。我们的目标是在不同 NDs 的发病诱因的复杂性中找到线索,这些复杂性表现在与疾病相关的因素的步骤数量上。我们收集了患有 NDs 的患者的大脑尸检样本,发现随着年龄的增长,ND 多种疾病的发病率呈动态增加。此外,我们还观察到 NDs 会积累多种错误折叠的蛋白质,并且同一种错误折叠的蛋白质会涉及多种 NDs,这促使我们提出了一种 NDs 的系统发生树模型。为了收集构建树所需的动态数据,我们使用了大数据方法,自动搜索流行病学数据集以确定 NDs 的年龄分层发病率。基于对超过 400 个数据集的元分析,我们开发了一种算法,该算法检查 ND 是否遵循多步模型,找到每种 ND 发病所需的步骤数,找到与其他 NDs 共有和特有步骤的数量,并根据这些发现构建 NDs 系统发生树。该树揭示了三种类型的 NDs:少于 3 步的主干 NDs;有 5 到 6 步的干 NDs;以及超过 7 步的树冠 NDs。该树提供了对不同 NDs 之间关系的全面理解,以及对 NDs 系统发生树进行动态调整的数学框架,随着新的流行病学研究的出现和新的 NDs 被添加到模型中,可以对其进行调整,从而为寻找这些步骤的身份和顺序奠定了基础。在决定研究多步疾病的单个因素之前,了解与疾病发病诱因相关的因素的复杂性或步骤数量是很重要的。