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Gene-expression profiling of individuals resilient to Alzheimer's disease reveals higher expression of genes related to metallothionein and mitochondrial processes and no changes in the unfolded protein response

GSE261817 Homo sapiens (human) RNA-seq (gene expression) 35 samples Submitted 2024/04/25 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 35 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
Homo sapiens (human)
Samples
35 samples: 12 × AD vs 12 × Control vs 11 × ResilientGroups 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
Gene-expression profiling of individuals resilient to Alzheimer's disease reveals higher expression of genes related to metallothionein and mitochondrial processes and no changes in the unfolded protein responseActa neuropathologica communications · 2024
Summary (written by the submitters)
Some individuals show a discrepancy between cognition and the amount of neuropathological changes characteristic for Alzheimer’s disease (AD). This phenomenon has been referred to as ‘resilience’. The molecular and cellular underpinnings of resilience remain poorly understood. To obtain an unbiased understanding of the molecular changes underlying resilience, we investigated global changes in gene expression in the superior frontal gyrus of a cohort of cognitively and pathologically well-defined AD patients, resilient individuals and age-matched controls (n=11-12 per group).
Read the full summary ↓
897 genes were significantly altered between AD and control, 1121 between resilient and control and 6 between resilient and AD. Gene set enrichment analysis (GSEA) revealed that the expression of metallothionein (MT) and of genes related to mitochondrial processes was higher in the resilient donors. Weighted gene co-expression network analysis (WGCNA) identified gene modules related to the unfolded protein response, mitochondrial processes and synaptic signaling to be differentially associated with resilience or dementia. As changes in MT, mitochondria, heat shock proteins and the unfolded protein response (UPR) were the most pronounced changes in the GSEA and/or WGCNA, immunohistochemistry was used to further validate these processes. MT was significantly increased in astrocytes in resilient individuals. A higher proportion of the mitochondrial gene MT-CO1 was detected outside the cell body versus inside the cell body in the resilient compared to the control group and there were higher levels of heat shock protein 70 (HSP70) and X-box-binding protein 1 spliced (XBP1s), two proteins related to heat shock proteins and the UPR, in the AD donors. Finally, we show evidence for putative sex-specific alterations in resilience, including gene expression differences related to autophagy in females compared to males. Taken together, these results show possible mechanisms involving MTs, mitochondrial processes and the UPR by which individuals might maintain cognition despite the presence of AD pathology.
Published in
Gene-expression profiling of individuals resilient to Alzheimer's disease reveals higher expression of genes related to metallothionein and mitochondrial processes and no changes in the unfolded protein response
de Vries LE, Jongejan A, Monteiro Fortes J et al. · Acta neuropathologica communications 2024 · PMID 38664739 · doi:10.1186/s40478-024-01760-9
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 GSE261817_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 PRJNA1089147 and SRA study SRP496148. Searching any of these in the dataset finder brings you back here.

Study design
12 × AD vs 12 × Control vs 11 × Resilient

Supports a between-group comparison across 35 samples.

How we read this: 3 replicated groups read from 35 sample titles; they account for 35 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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