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The role of LDLR in intestinal inflammation

GSE277062 Mus musculus (mouse) RNA-seq (gene expression) 10 samples Submitted 2024/11/01 Platform GPL18480
At a glance

RNA-seq (gene expression) · mouse · 10 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
10 samples: 5 × WT vs 5 × Ldlr KOGroups 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)
Low-density lipoprotein (LDL) is a risk factor for atherosclerosis, obesity, and other metabolic diseases, but its role in colitis is unknown. Here, we investigated the role of LDL in colitis by using LDL receptor deficient mice (Ldlr-/-). Wild-type and Ldlr-/- mice were fed with 3% DSS for 5 days and colons tissues were collected at day 8 post DSS treatment.
Read the full summary ↓
We observed that Ldlr-/- mice are less susceptible to colitis. To understand the role of LDLR in colitis pathogenesis, we isolated RNA from colons of WT and Ldlr-/- mice and perfoemed RNA-seq. Our data show that the expression of inflamamtory genes are reduced in Ldlr-/- colons compared to wild-type. These data suggest that LDL is a potent endogenous trigger for intestinal inflammation.
This dataset
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.

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 GSE277062_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1160308 and SRA study SRP532363. Searching any of these in the dataset finder brings you back here.

Study design
5 × WT vs 5 × Ldlr KO

Supports a between-group comparison across 10 samples.

How we read this: 2 replicated groups read from 10 sample titles; they account for 10 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
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