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Combining Spironolactone to Antiretroviral Therapy Accelerates HIV Decay in Humanized Mice

GSE310371 Homo sapiens (human) RNA-seq (gene expression) 39 samples Submitted 2025/12/01 Platform GPL30202
At a glance

RNA-seq (gene expression) · human · 39 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
39 samples: 39 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.
Paper
Combining spironolactone to antiretroviral therapy accelerates HIV decay in humanized miceEmerging microbes & infections · 2025
Summary (written by the submitters)
Spironolactone (SP), a clinically used aldosterone antagonist, has been explored as an anti-HIV agent in models of HIV-1 latency for inducing transcriptional silencing of the viral reservoir. SP promotes the degradation of xeroderma pigmentosum group B (XPB) protein, a crucial component of transcription factor II H (TFIIH) required for RNA polymerase II transcriptional initiation.
Read the full summary ↓
This study evaluated the impact of a long-acting formulation of SP on HIV replication within the context of antiretroviral therapy (ART) in the humanized mice model of HIV infection. The findings demonstrate that adding SP to ART accelerates viral decline and reduces expression of inflammation-related genes in human immune cells - genes often upregulated in chronic viral infections. Although SP treatment did not alter levels of cell-associated viral DNA, it led to a significant 4.4-fold decrease in systemic cell-associated viral RNA. This supports the role of XPB in HIV transcriptional regulation and advocates for incorporating transcriptional inhibitors like SP into primary HIV therapy. Additionally, SP treatment diminished markers of immune activation and inflammation, critical factors contributing to morbidity and mortality in individuals with chronic HIV infection. These results highlight SP’s potential to enhance HIV treatment by mitigating key aspects of viral persistence and associated immune challenges.
Published in
Combining spironolactone to antiretroviral therapy accelerates HIV decay in humanized mice
Ling L, Mori LP, Soper A et al. · Emerging microbes & infections 2025 · PMID 41319248 · doi:10.1080/22221751.2025.2589549
This dataset
Open GSE310371 in finder → Find more 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 GSE310371_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 PRJNA1365840 and SRA study SRP646625. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41319248) ↗
Study design
39 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 39 sample titles: 39 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
Datasets on a similar topic

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