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A disease similarity approach identifies short-lived Niemann-Pick type C disease mice with accelerated brain aging as a novel mouse model for Alzheimer’s disease and aging research

GSE266485 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2024/05/07 Platform GPL21103
At a glance

RNA-seq (gene expression) · mouse · 12 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
12 samples: 3 × wildtype male mice FC vs 3 × wildtype female mice FC vs 3 × NPC1KO male mice FC vs 3 × NPC1KO female mice FCGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
A disease similarity approach identifies short-lived Niemann-Pick type C disease mice with accelerated brain aging as a novel mouse model for Alzheimer's disease and aging researchbioRxiv : the preprint server for biology · 2024
Summary (written by the submitters)
Since its first description in 1906 by Dr. Alois Alzheimer, Alzheimer’s disease (AD) has been the most common type of dementia. Initially thought to be caused by age-associated accumulation of plaques, in recent years, research has increasingly associated AD with lysosomal storage and metabolic disorders, and the explanation of its pathogenesis has shifted from amyloid and tau accumulation to oxidative stress and impaired lipid and glucose metabolism aggravated by hypoxic conditions.
Read the full summary ↓
However, the underlying mechanisms linking those cellular processes and conditions to disease progression have yet to be defined. Here, we applied a disease similarity approach to identify unknown molecular targets of AD by using transcriptomic data from congenital diseases known to increase AD risk, namely Down Syndrome, Niemann Pick Disease Type C (NPC), and Mucopolysaccharidoses I. We uncovered common pathways, hub genes, and miRNAs across in vitro and in vivo models of these diseases as potential molecular targets for neuroprotection and amelioration of AD pathology, many of which have never been associated with AD. We then investigated common molecular alterations in brain samples from an NPC disease mouse model by juxtaposing them with brain samples of both human and mouse models of AD. Detailed phenotypic and molecular analyses revealed the NPCmut mouse as a novel, short-lived in vivo model of AD characterized by accelerated brain aging, concluding that the NPCmut mouse model can serve as a potential short-lived in vivo model for AD research and for understanding molecular factors affecting brain aging. This research represents the first comprehensive approach to congenital disease association with neurodegeneration and a new perspective on AD research while highlighting shortcomings and lack of correlation in diverse in vitro models. Considering the lack of an AD mouse model that recapitulates the physiological hallmarks of brain aging, the characterization of a short-lived NPC mouse model will further accelerate the research in these fields and offer a unique model for understanding the molecular mechanisms of AD from a perspective of accelerated brain aging.
Published in
A disease similarity approach identifies short-lived Niemann-Pick type C disease mice with accelerated brain aging as a novel mouse model for Alzheimer's disease and aging research
Gujjala VA, Klimek I, Abyadeh M et al. · bioRxiv : the preprint server for biology 2024 · PMID 38712089 · doi:10.1101/2024.04.19.590328
This dataset
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE266485_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1107268 and SRA study SRP505439. Searching any of these in the dataset finder brings you back here.

Study design
3 × wildtype male mice FC vs 3 × wildtype female mice FC vs 3 × NPC1KO male mice FC vs 3 × NPC1KO female mice FC

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.