Next Article in Journal
Comparative Transcriptome Analysis of Two Aegilops tauschii with Contrasting Drought Tolerance by RNA-Seq
Previous Article in Journal
A Simple and Effective Flow Cytometry-Based Method for Identification and Quantification of Tissue Infiltrated Leukocyte Subpopulations in a Mouse Model of Peripheral Arterial Disease
Open AccessArticle

Robust Sampling of Defective Pathways in Alzheimer’s Disease. Implications in Drug Repositioning

1
Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C/Federico García Lorca, 18, 33007 Oviedo, Spain
2
DeepBioInsights, C/Federico García Lorca, 18, 33007 Oviedo, Spain
3
Department of Informatics and Computer Science, University of Oviedo, C/Federico García Lorca, 18, 33007 Oviedo, Spain
4
Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
5
Department of Pediatrics, The Ohio State University, Columbus, OH 43205, USA
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(10), 3594; https://doi.org/10.3390/ijms21103594
Received: 27 April 2020 / Revised: 9 May 2020 / Accepted: 13 May 2020 / Published: 19 May 2020
(This article belongs to the Section Molecular Biophysics)
We present the analysis of the defective genetic pathways of the Late-Onset Alzheimer’s Disease (LOAD) compared to the Mild Cognitive Impairment (MCI) and Healthy Controls (HC) using different sampling methodologies. These algorithms sample the uncertainty space that is intrinsic to any kind of highly underdetermined phenotype prediction problem, by looking for the minimum-scale signatures (header genes) corresponding to different random holdouts. The biological pathways can be identified performing posterior analysis of these signatures established via cross-validation holdouts and plugging the set of most frequently sampled genes into different ontological platforms. That way, the effect of helper genes, whose presence might be due to the high degree of under determinacy of these experiments and data noise, is reduced. Our results suggest that common pathways for Alzheimer’s disease and MCI are mainly related to viral mRNA translation, influenza viral RNA transcription and replication, gene expression, mitochondrial translation, and metabolism, with these results being highly consistent regardless of the comparative methods. The cross-validated predictive accuracies achieved for the LOAD and MCI discriminations were 84% and 81.5%, respectively. The difference between LOAD and MCI could not be clearly established (74% accuracy). The most discriminatory genes of the LOAD-MCI discrimination are associated with proteasome mediated degradation and G-protein signaling. Based on these findings we have also performed drug repositioning using Dr. Insight package, proposing the following different typologies of drugs: isoquinoline alkaloids, antitumor antibiotics, phosphoinositide 3-kinase PI3K, autophagy inhibitors, antagonists of the muscarinic acetylcholine receptor and histone deacetylase inhibitors. We believe that the potential clinical relevance of these findings should be further investigated and confirmed with other independent studies. View Full-Text
Keywords: Alzheimer’s disease; mild cognitive impairment; pathway analysis; deep pathways sampling; drug repositioning Alzheimer’s disease; mild cognitive impairment; pathway analysis; deep pathways sampling; drug repositioning
Show Figures

Figure 1

MDPI and ACS Style

Fernández-Martínez, J.L.; Álvarez-Machancoses, Ó.; deAndrés-Galiana, E.J.; Bea, G.; Kloczkowski, A. Robust Sampling of Defective Pathways in Alzheimer’s Disease. Implications in Drug Repositioning. Int. J. Mol. Sci. 2020, 21, 3594.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop