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The CRAC channel inhibitor Auxora interrupts inflammatory circuits between alveolar macrophages and T cells in patients with viral pneumonia

GSE330843 Homo sapiens (human) RNA-seq (gene expression) 88 samples Submitted 2026/06/30 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 88 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
88 samples: 9 conditions, mostly with about 10 replicates eachGroups 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)
Viral pneumonia is perpetuated by inflammatory circuits between activated T cells and monocyte-derived alveolar macrophages (MoAM). T cells and macrophages express ORAI1 and STIM1, which form calcium release-activated calcium (CRAC) channels that allow extracellular calcium entry in response to endoplasmic reticulum calcium store depletion.
Read the full summary ↓
In a randomized, placebo-controlled, multicenter phase 2 trial (CARDEA), Auxora, a CRAC channel inhibitor, reduced all-cause 30-day mortality by 56% in patients with severe SARS-CoV-2 pneumonia1. Here, we report a multi-omics analysis of serially collected alveolar samples from unvaccinated patients with severe SARS-CoV-2 pneumonia treated with Auxora versus placebo. We found reductions in plasma levels of the monocyte- and T cell-chemokines, CCL8 and CXCL10. Using PBMCs from healthy volunteers, we show Auxora directly targets T cells to inhibit the transcription of CCL8 and CXCL10 in monocyte-derived macrophages, supporting a mechanism for its effects and a potential intermediate biomarker of efficacy.
This dataset
Open GSE330843 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 GSE330843_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 PRJNA1465773. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
9 conditions, mostly with about 10 replicates each
DMSO sample ×10 Auxora 50nM sample ×10 Auxora 500nM sample ×10 Anti CD3 28 sample ×10 IFNg sample ×10 IFNg Ax 50 sample ×10 IFNg Ax 500 sample ×10 Anti CD3 28 Ax 50nM sample ×9 +1 more

Supports a between-group comparison across 88 samples.

How we read this: 9 replicated groups read from 88 sample titles; they account for 88 of them. 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

+ 48 more — browse all 88 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.