ArticleMolecular neurobiology2025
Whole Transcriptome RNA-Seq Reveals Drivers of Pathological Dysfunction in a Transgenic Model of Alzheimer's Disease.
Article in Molecular neurobiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- SPGA: graph representation learning and attention fusion for enhanced disease-associated snoRNA prediction.BMC bioinformatics · 2026Article
- Arhgef7 as a key target for enriched environment rescuing spatial cognitive deficits and anxiety-like behaviors in a mouse model of Alzheimer's disease following early social isolation.Alzheimer's research & therapy · 2025Article
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Authors and funding
7 authors.
Funding
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Abstract
Alzheimer's disease (AD) affects more than 55 million people worldwide, yet current theories cannot fully explain its aetiology. Accordingly, gene expression profiling has been used to provide a holistic view of the biology underpinning AD. Focusing primarily on protein-coding genes, such approaches have highlighted a critical involvement of microglia-related inflammatory processes. Simultaneous investigation of transcriptional regulators and noncoding RNA (ncRNA) can offer further insight into AD biology and inform the development of disease-modifying therapies. We previously described a method for whole transcriptome sampling to simultaneously investigate protein-coding genes and ncRNA. Here, we use this technique to explore transcriptional changes in a murine model of AD (15-month-old APP/PS1 mice). We confirmed the extensive involvement of microglia-associated genes and gene networks, consistent with literature. We also report a wealth of differentially-expressed non-coding RNA - including microRNA, long non-coding RNA, small nuclear and small nucleolar RNA, and pseudogenes - many of which have been overlooked previously. Transcription factor analysis determined that six transcription factors likely regulate gene expression changes in this model (Irf8, Junb, c-Fos, Lmo2, Runx1, and Nfe2l2). We then utilised validated miRNA-target interactions, finding 60 interactions between 15 miRNA and 42 mRNA (messenger RNA) with largely consistent directionality. Furthermore, we found that eight transcription factors (Clock, Lmo2, Runx1, Nfe2l2, Egr2, c-Fos, Junb, and Nr4a1) are likely responsible for the regulation of miRNA expression. Taken together, these data indicate a complex interplay of coding and non-coding RNA, driven by a small number of specific transcription factors, contributing to transcriptional changes in 15-month-old APP/PS1 mice.
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Registered trials
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