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A reversible broad-spectrum antiviral targets the human V-ATPase VO domain

GSE313871 Homo sapiens (human) RNA-seq (gene expression) 48 samples Submitted 2026/02/11 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 48 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
48 samples: 48 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
TMPRSS2 inhibitors with broad-spectrum efficacy against SARS-CoV-2 (JN.1) and influenza A (H1N1) viruses protect mice from influenza A infectionEmerging microbes & infections · 2026
Summary (written by the submitters)
Increasing threats of viral disease underscore the urgent need for broad-spectrum antiviral drugs (BSADs). Host proteins utilized by human pathogenic viruses are key BSAD targets. The vacuolar-type H⁺-ATPase (V-ATPase) has been identified as a proviral factor for most pH-dependent enveloped viruses classified as pandemic threats.
Read the full summary ↓
We report here the discovery of cladoniamide A (CA)—a V-ATPase inhibitor with single-digit nanomolar antiviral activity and a high selectivity index (SI: 103-104) against human enveloped viruses [e.g., SARS-CoV-2 variants, influenza A viruses (H1N1, H5N1), respiratory syncytial virus, dengue serotypes 1–4, and Zika virus]. Transcriptome profiling, pH estimation assays, and V-ATPase bioassays indicate that CA interferes with V-ATPase-dependent acidification of the host endolysosomal network thus preventing viral entry. Using pseudoviruses derived from five pathogenic virus families, we confirmed that CA is a BSAD acting as an entry inhibitor. CryoEM revealed that CA inhibits the V-ATPase rotary motor by occupying unique binding sites in the membrane-embedded Vo motor. Importantly, intranasal CA treatment in mice infected with influenza A H1N1 significantly reduced viral load in the lung by four log orders. Together, these findings pave the way for developing next-generation BSADs targeted at unique druggable pockets that enable the reversible pharmacological modulation of the human V-ATPase.
Published in
TMPRSS2 inhibitors with broad-spectrum efficacy against SARS-CoV-2 (JN.1) and influenza A (H1N1) viruses protect mice from influenza A infection
Presley L, Pérez-Vargas J, Saintigny A et al. · Emerging microbes & infections 2026 · PMID 41612887 · doi:10.1080/22221751.2026.2616944
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 GSE313871_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 PRJNA1381702 and SRA study SRP655348. Searching any of these in the dataset finder brings you back here.

Study design
48 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 48 sample titles: 48 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

+ 8 more — browse all 48 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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