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Systems-level computational translation of mouse and human transcriptomics reveals a role for the unfolded protein response in Mycobacterium tuberculosis infection

GSE281697 Mus musculus (mouse) RNA-seq (gene expression) 20 samples Submitted 2024/11/18 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 20 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
Mus musculus (mouse)
Samples
20 samples: 20 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
Cross-species transcriptomics translation reveals a role for the unfolded protein response in Mycobacterium tuberculosis infectionNPJ systems biology and applications · 2025
Summary (written by the submitters)
Numerous studies have identified similarities in blood transcriptomic signatures of tuberculosis (TB) disease phenotypes between mouse and human systems, such as type 1 interferon (IFN) production and innate immune cell activation, yet the pathophysiology observed in murine infection does not recapitulate some of the hallmarks of human TB disease.
Read the full summary ↓
We hypothesized that some of these differences relate to quantitative differences in the importance of different biological pathways in the two systems, rather that completely distinct responses to infection. To test this hypothesis and address the critical animal-to-human gap, we applied a systems biology machine learning framework, Translatable Components Regression, which identifies axes of variation in the preclinical study that correspond with numerous biological pathways and processes and estimates their relative contributions to TB disease state in humans. Our modeling confirmed the presence of biological axes of variation that differed in their relative importance between species. Prominent among the pathways most predictive of human TB phenotype, beyond the previously established common signatures, is the infection-induced Unfolded Protein Response (UPR). To validate that this mechanism can be uncovered even in the murine context, wherein it has not previously been a major avenue of investigation, we show experimentally that this cellular stress pathway controls a variety of immune-related functions in Mycobacterium tuberculosis-infected mouse macrophages. Thus, our work here demonstrates how systems-level computational models enhance the value of animal studies for purpose of elucidating complex human pathophysiology.
Published in
Cross-species transcriptomics translation reveals a role for the unfolded protein response in Mycobacterium tuberculosis infection
Pullen KM, Finethy R, Ko SB et al. · NPJ systems biology and applications 2025 · PMID 39955299 · doi:10.1038/s41540-024-00487-6
This dataset
Open GSE281697 in finder → Find more mouse 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 GSE281697_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 PRJNA1185245 and SRA study SRP544729. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39955299) ↗
Study design
20 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 20 sample titles: 20 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
Datasets on a similar topic

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