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Systemic infections alter cortical transcriptional signatures in Alzheimer′s disease

GSE313307 Homo sapiens (human) RNA-seq (gene expression) 202 samples Submitted 2025/12/16 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 202 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
202 samples: 202 samples, each with a distinct title — no replicated groupsGroups 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.
Summary (written by the submitters)
Alzheimer′s disease (AD) is characterized by neuroinflammation, yet the impact of concurrent systemic infections on the AD brain remains poorly understood. We investigated the molecular mechanisms underlying the central nervous system response to systemic infections in AD by analyzing RNA sequencing data generated in the prefrontal cortex from 202 post-mortem donors (113 AD, 89 controls), where we stratified by the presence of a respiratory infection at the time of death.
Read the full summary ↓
We identified 763 significantly differentially expressed genes (DEGs) between AD and controls without infection, which were enriched for oxidative phosphorylation and neurodegenerative pathways. In contrast, 122 DEGs distinguished AD from controls during infection, with 57 genes uniquely altered in AD in the presence of infection, including MAPK4 , VAV3 , and POU3F4 , implicating infection-dependent mechanisms of vascular and immune regulation. Pathway activity analysis revealed that infection in AD suppresses some immune and vascular pathways, while enhancing transcriptional and developmental programs. Weighted gene co-expression network analysis uncovered three key modules: one module strongly associated with AD, enriched for aging and signal transduction; one module linked to both AD and infection, highlighting cytoskeletal remodelling and host–pathogen interactions; and one module specific to infection, enriched in astrocytes, pericytes, and endothelial cells, implicating blood–brain–barrier dysfunction. These findings suggest that systemic respiratory infections reshape transcriptional programs in the AD brain, dampening immune effector pathways and engaging vascular and host–pathogen processes in blood–brain–barrier–associated cell types. Our results highlight the complex interplay between systemic infection, neuroinflammation, and vascular responses in AD.
This dataset
Open GSE313307 in finder → Find more human RNA-seq datasets →
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.

Series matrix ↓
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 GSE313307_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
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.

Raw reads on ENA ↗

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 PRJNA1378909 and SRA study SRP654157. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
202 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 202 sample titles: 202 distinct titles with little repetition. 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

+ 162 more — browse all 202 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.