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TBK1 and IKKε protect target cells from IFNgamma-mediated inflammatory apoptosis

GSE278131 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2024/10/28 Platform GPL23479
At a glance

RNA-seq (gene expression) · mouse · 18 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
18 samples: 6 conditions, mostly in triplicateGroups 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)
T cells produce interferon gamma (IFNγ), but it is unclear whether they can use IFNγ to directly kill target cells. Here, we report that tumor targets lacking TBK1 and IKKε are killed by IFNγ, which induces expression of TNFR1 and the Z-nucleic acid sensor, ZBP1 to trigger RIPK1-dependent apoptosis. Concurrent with apoptosis, IFNγ unexpectedly also induces hyperactivation of NFκB in TBK1 and IKKε double-deficient cells.
Read the full summary ↓
The two kinases normally function to inhibit IKKa/b activity and in their absence, IFNγ induces elevated expression of inflammatory genes. Thus, IFNγ can induce an inflammatory form of apoptosis that is normally suppressed by TBK1 and IKKε. These kinases regulate three responses: (1) induction of type I IFN; (2) inhibition of RIPK1-dependent death; and (3) inhibition of NFκB-dependent inflammation. We propose that these kinases evolved these intertwined functions so that cells infected by pathogens encoding TBK1/IKKε antagonists to block type I IFN expression will be killed by IFNγ-secreting T cells to generate an alternate NFκB-dependent inflammatory response.
This dataset
Open GSE278131 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 GSE278131_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 PRJNA1165745 and SRA study SRP535004. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly in triplicate
B16-F1 cells, NTNT, 0h, biol ×3 B16-F1 cells, NTNT, 16h, biol ×3 B16-F1 cells, NTNT, 24h, biol ×3 B16-F1 cells, DKO, 0h, biol ×3 B16-F1 cells, DKO, 16h, biol ×3 B16-F1 cells, DKO, 24h, biol ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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
Datasets on a similar topic

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