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Minimal impact of ivermectin on immune response and transcriptional profiles in naïve adults with mild COVID-19

GSE290188 Homo sapiens (human) RNA-seq (gene expression) 72 samples Submitted 2025/08/04 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 72 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
72 samples: 72 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)
Ivermectin (IVM), an antiparasitic, was tested in clinical trials as a potential treatment for COVID-19 due to its in vitro antiviral properties and hypothetical immunomodulatory effects. In this study, we explored the immunomodulatory effects of IVM in a clinical trial involving 24 mild COVID-19 patients who received IVM within 48 hours of symptom onset.
Read the full summary ↓
The IVM group showed a trend towards lower viral loads post-treatment, and a significantly shorter duration of hyposmia/anosmia. We comprehensively analyzed immune responses by measuring antibody levels, cytokine profiles, immune cell subsets over 28 days and whole blood gene expression. The IVM group had increased levels of IgG against the SARS-CoV-2 nucleocapsid and IL-1RA, an anti-inflammatory cytokine. IVM did not affect the kinetics of SARS-CoV-2 T cells, despite a slight decrease in naive CD4+ T cells. Additionally, gene expression analysis showed a decrease in innate and antiviral responses and an increase of adaptive responses over time that were stronger in the placebo compared to the IVM group. In conclusion, despite some differences in IVM treated participants, our detailed analyses do not support significant immunomodulatory effects, reinforcing evidence that IVM is unlikely to be an effective treatment for COVID-19.
This dataset
Open GSE290188 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 GSE290188_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 PRJNA1226538 and SRA study SRP565481. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
72 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 72 sample titles: 72 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

+ 32 more — browse all 72 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.