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Article type: Research Article
Authors: Lio, Chit Tonga; b | Kacprowski, Timc; d | Klaedtke, Maike | Jensen, Lars R.e | Bouter, Yvonnef | Bayer, Thomas A.f; * | Kuss, Andreas W.e; *
Affiliations: [a] Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany | [b] Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany | [c] Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany | [d] Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany | [e] Department of Functional Genomics, Human Molecular Genetics Group, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany | [f] Department of Psychiatry and Psychotherapy, Division of Molecular Psychiatry, University Medical Center Goettingen (UMG), Georg-August-University, Goettingen, Germany
Correspondence: [*] Correspondence to: Andreas W. Kuss, Human Molecular Genetics Group, Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany. Tel.: +49 0 3834 4205814; E-mail: [email protected] and Thomas A. Bayer, Division of Molecular Psychiatry, Department of Psychiatry and Psychotherapy, University Medical Center Goettingen (UMG), Georg-August-University, Goettingen, Germany. Tel.: +49 (0)551 39 22912; E-mail: [email protected].
Abstract: Background:The Tg4-42 mouse model for sporadic Alzheimer’s disease (AD) has unique features, as the neuronal expression of wild type N-truncated Aβ4–42 induces an AD-typical neurological phenotype in the absence of plaques. It is one of the few models developing neuron death in the CA1 region of the hippocampus. As such, it could serve as a powerful tool for preclinical drug testing and identification of the underlying molecular pathways that drive the pathology of AD. Objective:The aim of this study was to use a differential co-expression analysis approach for analyzing a small RNA sequencing dataset from a well-established murine model in order to identify potentially new players in the etiology of AD. Methods:To investigate small nucleolar RNAs in the hippocampus of Tg4-42 mice, we used RNA-Seq data from this particular tissue and, instead of analyzing the data at single gene level, employed differential co-expression analysis, which takes the comparison to gene pair level and thus affords a new angle to the interpretation of these data. Results:We identified two clusters of differentially correlated small RNAs, including Snord55, Snord57, Snord49a, Snord12, Snord38a, Snord99, Snord87, Mir1981, Mir106b, Mir30d, Mir598, and Mir99b. Interestingly, some of them have been reported to be functionally relevant in AD pathogenesis, as AD biomarkers, regulating tau phosphorylation, TGF-β receptor function or Aβ metabolism. Conclusion:The majority of snoRNAs for which our results suggest a potential role in the etiology of AD were so far not conspicuously implicated in the context of AD pathogenesis and could thus point towards interesting new avenues of research in this field.
Keywords: Alzheimer’s disease, miRNA, NGS, RNA-Seq, small RNA, snoRNA, Tg4-42, transgenic mouse model
DOI: 10.3233/JAD-220110
Journal: Journal of Alzheimer's Disease, vol. 87, no. 4, pp. 1671-1681, 2022
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