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Gut Microbiota Orchestrates Energy Homeostasis during Cold [RNA-Seq]

GSE74157 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2015/12/03 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 12 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
12 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)
Microbial functions in the host physiology are a result of co-evolution between microbial communities and their hosts. Here we show that cold exposure leads to marked shift of the microbiota composition, referred to as cold microbiota. Transplantation of the cold microbiota to germ-free mice is sufficient to increase the insulin sensitivity of the host, and enable complete tolerance to cold partly by promoting the white fat browning, leading to increased energy expenditure and fat loss.
Read the full summary ↓
During prolonged cold however, the body weight loss is attenuated, caused by adaptive mechanisms maximising caloric uptake and increasing intestinal, villi and microvilli lengths. This increased absorptive surface is promoted by the cold microbiota - effect that can be diminished by co-transplanting the most downregulated bacterial strain from the Verrucomicrobia phylum, Akkermansia muciniphila, during the cold microbiota transfer. Our results demonstrate the microbiota as a key factor orchestrating the overall energy homeostasis during increased demand.
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 GSE74157_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 PRJNA299190 and SRA study SRP064981. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 12 more — browse all 12 samples with per-sample file links →

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