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Open AccessArticle

Exploring Alzheimer’s Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures

1
The Rachel and Selim Benin School of Computer Science and Engineering, Hebrew University, Jerusalem 9190416, Israel
2
Department for Bio-Medical Research, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel
3
Department of Biochemistry and Molecular Biology, The Institute for Medical Research Israel—Canada, The Hebrew University School of Medicine, Jerusalem 9112102, Israel
*
Author to whom correspondence should be addressed.
These authors contributed equally.
Biomolecules 2020, 10(4), 503; https://doi.org/10.3390/biom10040503
Received: 30 January 2020 / Revised: 23 March 2020 / Accepted: 24 March 2020 / Published: 26 March 2020
Despite huge investments and major efforts to develop remedies for Alzheimer’s disease (AD) in the past decades, AD remains incurable. While evidence for molecular and phenotypic variability in AD have been accumulating, AD research still heavily relies on the search for AD-specific genetic/protein biomarkers that are expected to exhibit repetitive patterns throughout all patients. Thus, the classification of AD patients to different categories is expected to set the basis for the development of therapies that will be beneficial for subpopulations of patients. Here we explore the molecular heterogeneity among a large cohort of AD and non-demented brain samples, aiming to address the question whether AD-specific molecular biomarkers can progress our understanding of the disease and advance the development of anti-AD therapeutics. We studied 951 brain samples, obtained from up to 17 brain regions of 85 AD patients and 22 non-demented subjects. Utilizing an information-theoretic approach, we deciphered the brain sample-specific structures of altered transcriptional networks. Our in-depth analysis revealed that 7 subnetworks were repetitive in the 737 diseased and 214 non-demented brain samples. Each sample was characterized by a subset consisting of ~1–3 subnetworks out of 7, generating 52 distinct altered transcriptional signatures that characterized the 951 samples. We show that 30 different altered transcriptional signatures characterized solely AD samples and were not found in any of the non-demented samples. In contrast, the rest of the signatures characterized different subsets of sample types, demonstrating the high molecular variability and complexity of gene expression in AD. Importantly, different AD patients exhibiting similar expression levels of AD biomarkers harbored distinct altered transcriptional networks. Our results emphasize the need to expand the biomarker-based stratification to patient-specific transcriptional signature identification for improved AD diagnosis and for the development of subclass-specific future treatment. View Full-Text
Keywords: Alzheimer’s disease; information theory; altered transcriptional network structure; surprisal analysis; patient-specific transcriptional signatures Alzheimer’s disease; information theory; altered transcriptional network structure; surprisal analysis; patient-specific transcriptional signatures
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MDPI and ACS Style

Dagan, H.; Flashner-Abramson, E.; Vasudevan, S.; Jubran, M.R.; Cohen, E.; Kravchenko-Balasha, N. Exploring Alzheimer’s Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures. Biomolecules 2020, 10, 503.

AMA Style

Dagan H, Flashner-Abramson E, Vasudevan S, Jubran MR, Cohen E, Kravchenko-Balasha N. Exploring Alzheimer’s Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures. Biomolecules. 2020; 10(4):503.

Chicago/Turabian Style

Dagan, Hila; Flashner-Abramson, Efrat; Vasudevan, Swetha; Jubran, Maria R.; Cohen, Ehud; Kravchenko-Balasha, Nataly. 2020. "Exploring Alzheimer’s Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures" Biomolecules 10, no. 4: 503.

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