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Genomic crosstalk between carbachol, a muscarinic receptor agonist, and indacaterol, a long-acting, β2-adrenoceptor agonist, in human airway epithelial cells

GSE282554 Homo sapiens (human) RNA-seq (gene expression) 160 samples Submitted 2025/03/20 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 160 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
160 samples: 160 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)
Many patients with chronic obstructive pulmonary disease (COPD) are susceptible to recurrent exacerbations. Long-acting, muscarinic receptor antagonists protect against exacerbations suggesting that endogenous acetylcholine (ACh) could be a pro-inflammatory mediator. This idea was explored by determining if carbachol (CCh), a stable ACh analog, was a genomic stimulus in BEAS-2B bronchial epithelial cells.
Read the full summary ↓
The ability of CCh to interact with indacaterol (Ind), a long-acting, β2-adrenoceptor agonists (LABA), was also assessed because sympathomimetic bronchodilators can promote the expression of adverse-effect genes in airway structural cells that are often regulated by the same transcription factors. Checkerboard assays using BEAS-2B cells expressing a cAMP response element luciferase reporter determined that CCh was a weak stimulus but interacted with Ind in a supra-additive manner. Likewise, mRNA-seq revealed that CCh regulated only 20 genes in BEAS-2B cells whereas Ind and Ind+CCh were powerful genomic stimuli affecting 869 and 1027 unique mRNAs, respectively. Of those, 39 genes were induced by Ind+CCh in a supra-additive manner; for the remainder, the interaction was either additive or infra-additive. Functional annotation of the Ind-regulated transcriptome identified transcription and signaling as dominant themes with gene ontology (GO) terms associated with inflammation and immune processes being highly represented. A comparable GO signature was obtained in the presence of CCh; however, the number, magnitude and duration of gene expression changes were markedly enhanced. If genomic interactions occur between a LABA and ACh in vivo, then this may lead to the expression of adverse-effect genes that, paradoxically, could maintain features of lung pathology in COPD.
This dataset
Open GSE282554 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 GSE282554_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 PRJNA1189129 and SRA study SRP546828. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
160 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 160 sample titles: 160 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

+ 120 more — browse all 160 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.