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Inhibiting Glycan Degradation Prevents HIV-Induced Inflammaging and Cognitive Impairment

GSE308269 Mus musculus (mouse) RNA-seq (gene expression) 16 samples Submitted 2026/04/08 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 16 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
16 samples
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)
Cognitive impairment is a frequent outcome of chronic viral infections linked to premature aging, including HIV. The mechanisms underlying this decline remain poorly understood. Here, we identify pro-inflammatory glycan degradation, characterized by loss of sialic acid and galactose, alterations that are hallmarks of premature aging, as key contributors to HIV-associated cognitive impairment (HIV-CI).
Read the full summary ↓
In two independent cohorts of people living with HIV, these degradative changes were enriched in individuals with cognitive impairment, particularly women, and correlated with worse neurocognitive performance. In both a humanized mouse model of HIV and Eco-HIV, a complementary model that allows behavioral testing, pharmacological inhibition of glycan degradation with sialidase inhibitors prevented virally induced inflammation, immune activation, accelerated aging, and memory deficits. These findings implicate glycan degradation as a contributor to inflammation and neurocognitive impairment in HIV and highlight glycan-preserving therapies as a promising strategy to mitigate inflammation, premature aging, and cognitive decline during viral infections.
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 GSE308269_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 PRJNA1329335 and SRA study SRP620873. Searching any of these in the dataset finder brings you back here.

Study design
A cohort of 16 individual samples

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

How we read this: Sample titles run STL6520 … STL6558 and carry no group information.

Samples in this study
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