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The intestinal microbiota regulates body composition through NFIL3 and the circadian clock

GSE100339 Mus musculus (mouse) RNA-seq (gene expression) 32 samples Submitted 2017/09/26 Platform GPL21051Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 32 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
32 samples
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)
The intestinal microbiota has been identified as an environmental factor that markedly impacts energy storage and body fat accumulation, yet the underlying mechanisms remain unclear. Here we show that the microbiota regulates body composition through the circadian transcription factor NFIL3. Nfil3 transcription oscillates diurnally in intestinal epithelial cells and the amplitude of the circadian oscillation is controlled by the microbiota through type 3 innate lymphoid cells (ILC3), STAT3, and the epithelial cell circadian clock.
Read the full summary ↓
NFIL3 controls expression of a circadian lipid metabolic program and regulates lipid absorption and export in intestinal epithelial cells. These findings provide mechanistic insight into how the intestinal microbiota regulates body composition and establish NFIL3 as an essential molecular link among the microbiota, the circadian clock, and host metabolism.
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 GSE100339_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 PRJNA391374 and SRA study SRP110082. Searching any of these in the dataset finder brings you back here.

Samples in this study

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