Milano Marianna, Cinaglia Pietro, Guzzi Pietro Hiram, Cannataro Mario
Department of Experimental and Clinical Medicine, University Magna Græcia, 88100 Catanzaro, Italy.
Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy.
Life (Basel). 2023 Jul 6;13(7):1520. doi: 10.3390/life13071520.
Neurodegenerative diseases (NDs) are a group of complex disorders characterized by the progressive degeneration and dysfunction of neurons in the central nervous system. NDs encompass many conditions, including Alzheimer's disease and Parkinson's disease. Alzheimer's disease (AD) is a complex disease affecting almost forty million people worldwide. AD is characterized by a progressive decline of cognitive functions related to the loss of connections between nerve cells caused by the prevalence of extracellular Aβ plaques and intracellular neurofibrillary tangles plaques. Parkinson's disease (PD) is a neurodegenerative disorder that primarily affects the movement of an individual. The exact cause of Parkinson's disease is not fully understood, but it is believed to involve a combination of genetic and environmental factors. Some cases of PD are linked to mutations in the LRRK2, PARKIN and other genes, which are associated with familial forms of the disease. Different research studies have applied the Protein Protein Interaction (PPI) networks to understand different aspects of disease progression. For instance, Caenorhabditis elegans is widely used as a model organism for the study of AD due to roughly 38% of its genes having a ortholog. This study's goal consists of comparing PPI network of and by applying computational techniques, widely used for the analysis of PPI networks between species, such as Local Network Alignment (LNA). For this aim, we used L-HetNetAligner algorithm to build a local alignment among two PPI networks, i.e., and PPI networks associated with AD and PD built-in silicon. The results show that L-HetNetAligner can find local alignments representing functionally related subregions. In conclusion, since local alignment enables the extraction of functionally related modules, the method can be used to study complex disease progression.
神经退行性疾病（NDs）是一组复杂的疾病，其特征是中枢神经系统中神经元的渐进性退化和功能障碍。神经退行性疾病包括许多病症，如阿尔茨海默病和帕金森病。阿尔茨海默病（AD）是一种复杂的疾病，全球约有4000万人受其影响。阿尔茨海默病的特征是认知功能逐渐下降，这与细胞外Aβ斑块和细胞内神经原纤维缠结斑块的普遍存在导致神经细胞之间连接丧失有关。帕金森病（PD）是一种神经退行性疾病，主要影响个体的运动。帕金森病的确切病因尚未完全明确，但据信涉及遗传和环境因素的综合作用。一些帕金森病病例与LRRK2、PARKIN等基因突变有关，这些基因与该疾病的家族形式相关。不同的研究已经应用蛋白质-蛋白质相互作用（PPI）网络来理解疾病进展的不同方面。例如，秀丽隐杆线虫因其约38%的基因具有直系同源物而被广泛用作研究阿尔茨海默病的模式生物。本研究的目标是通过应用计算技术比较[未提及具体物种1]和[未提及具体物种2]的PPI网络，这些技术广泛用于分析物种间的PPI网络，如局部网络比对（LNA）。为了实现这一目标，我们使用L-HetNetAligner算法在两个PPI网络之间建立局部比对，即与阿尔茨海默病和帕金森病相关的内置在硅中的[未提及具体物种1]和[未提及具体物种2]PPI网络。结果表明，L-HetNetAligner可以找到代表功能相关子区域的局部比对。总之，由于局部比对能够提取功能相关模块，该方法可用于研究复杂疾病的进展。
