Aligning Cross-Species Interactomes for Studying Complex and Chronic Diseases

Abstract

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 human ortholog. This study's goal consists of comparing PPI network of C. elegans and human 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., C. elegans and human 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.

Keywords: Alzheimer’s disease; PPI network; Parkinson’s disease; local network alignment; network alignment.

Figure 1

The figure shows the semantic similarity of six real modules (AD-related) (black marker) and synthetic modules (red, purple, green, blue, golden, dark green, orange, orchid, gray markers) computed with Resnik’s measure.

Figure 2

The figure shows the semantic similarity of real six modules (AD-related) (black marker) and synthetic modules (red, purple, green, blue, golden, dark green, orange, orchid, gray markers) computed with Lin’s measure.

Figure 3

The figure shows the semantic similarity of real six modules (AD-related) (black marker) and synthetic modules (red, purple, green, blue, golden, dark green, orange, orchid, gray markers) computed with Wang’s measure.

Figure 4

The figure shows the semantic similarity of real six modules (PD-related) (black marker) and synthetic modules (red, purple, green, blue, golden, dark green, orange, orchid, gray markers) computed with Resnik’s measure.

Figure 5

The figure shows the semantic similarity of real six modules (PD-related) (black marker) and synthetic modules (red, purple, green, blue, golden, dark green, orange, orchid, gray markers) computed with Lin’s measure.

Figure 6

The figure shows the semantic similarity of real six modules (PD-related) (black marker) and synthetic modules (red, purple, green, blue, golden, dark green, orange, orchid, gray markers) computed with Wang’s measure.