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. 2022 Oct 7;12(1):16846.
doi: 10.1038/s41598-022-20404-3.

Alzheimer's disease: insights from a network medicine perspective

Federica Conte  1 , Paola Paci  2   3
Affiliations

Alzheimer's disease: insights from a network medicine perspective

Federica Conte et al. Sci Rep. .

Abstract

Alzheimer's disease (AD) is the most common neurodegenerative disease that currently lacks available effective therapy. Thus, identifying novel molecular biomarkers for diagnosis and treatment of AD is urgently demanded. In this study, we exploited tools and concepts of the emerging research area of Network Medicine to unveil a novel putative disease gene signature associated with AD. We proposed a new pipeline, which combines the strengths of two consolidated algorithms of the Network Medicine: DIseAse MOdule Detection (DIAMOnD), designed to predict new disease-associated genes within the human interactome network; and SWItch Miner (SWIM), designed to predict important (switch) genes within the co-expression network. Our integrated computational analysis allowed us to enlarge the set of the known disease genes associated to AD with additional 14 genes that may be proposed as new potential diagnostic biomarkers and therapeutic targets for AD phenotype.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
DIAMOnD methodology. The network corresponds to the human interactome where the orange circular nodes are the seed proteins, the green square node is the protein to test with k connections (red and grey thick links) including ks links to seed proteins (red thick links), the grey circular nodes refer to other proteins in the interactome. The ensembles considered in the hypergeometric test for the computation of connectivity significance are reported.
Figure 2
Figure 2
Study design. The figure depicts the schematic of the analysis applied in this study.
Figure 3
Figure 3
Biological evaluation of DIAMOnD predictions. The significance (enrichment p value ≤ 0.01) of the similarity between DIAMOnD predicted disease genes and seed genes suggests a cutoff corresponding to the iteration 238.
Figure 4
Figure 4
SWIMmeR results on AD gene expression data. (a) Differentially expressed genes (DEGs). Differentially expressed profiles are clustered according to genes (rows) and samples (columns) by using Pearson correlation distance as metrics. Heat map colours represent different expression levels increasing from blue to yellow. AD samples (case) are indicated in red, whereas control samples are indicated in green. (b) Connectivity of correlation network. The x-axis represents the Pearson correlation threshold varying in the chosen range, while the y-axis represents the fraction of nodes populating the largest component. The dashed red line corresponds to the selected threshold (i.e., 0.46 or 83th percentile). Note that y = 1 means that all nodes fall in the largest component and thus the network is fully connected; otherwise more components exist. (c) APCC distribution. Probability distribution of APCC for hubs (i.e., node with degree greater than 5) identified in the AD correlation network. Note the presence of the three peaks associated to the date/party/fight-club hub classification. (d) Heat cartography map. Dots correspond to nodes in the AD correlation network. Each node is coloured according to the value of the APCC between its expression profile and that of its nearest neighbours in the network.
Figure 5
Figure 5
AD gene signature. By integrating the DIAMOnD and SWIMmeR results, the set of the known disease genes associated to AD (seed) was enlarged with additional 14 genes that are switch genes and that physically interact with each other and with the seed.
See this image and copyright information in PMC

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