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Filaggrin status modulates gene transcription, DNA damage and epidermal barrier repair during clinical response to methotrexate in atopic eczema

GSE283265 Homo sapiens (human) RNA-seq (gene expression) 44 samples Submitted 2025/06/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 44 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
44 samples: 44 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)
Background: Methotrexate (MTX) is an effective treatment for atopic eczema (AE); however, the mechanism of action remains incompletely understood. MTX is known to induce keratinocyte differentiation, which can occur following DNA damage triggered by external stimuli. Objectives: We sought to investigate the mechanism of action of MTX in AE using a combination of in vitro models and study of AE skin during the early phases of therapy with MTX.
Read the full summary ↓
Specifically, we investigated the relationship between MTX-induced DNA damage, keratinocyte differentiation, barrier function, filaggrin status and disease activity. Methods: Lesional and non-lesional skin from two AE cohorts and in vitro human 3-D epidermal organoids (living epidermal equivalents – [LEEs]) were used to assess response to MTX. Markers of late keratinocyte differentiation and response to purine/pyrimidine nucleosides were assessed. Keratinocyte DNA damage following MTX was evaluated through measurement of DNA damage markers and transcriptomic analysis. Results: MTX restored barrier function and induced filaggrin and loricrin expression in AE skin and LEE, through a mechanism inhibited by thymidine. MTX-induced DNA damage and DNA damage response signalling pathways in LEE and AE skin which were upstream of keratinocyte differentiation. There was an interaction between gene expression, disease severity (EASI) and FLG status (heterozygotes versus wildtype, e.g. LGALS7, SPINK7). Conclusions: MTX induces key epidermal barrier proteins through thymidine-dependent DNA damage in the absence of inflammatory cells. Findings were associated with improved disease outcomes in AE skin.
This dataset
Open GSE283265 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 GSE283265_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 PRJNA1192991 and SRA study SRP548651. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
44 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 44 sample titles: 44 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

+ 4 more — browse all 44 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.