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Amino Acids as a Major Carbon Source for Hepatic Lipogenesis

GSE264493 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2024/08/12 Platform GPL24247
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: 6 × SPD vs 6 × LPDGroups 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
Amino acid is a major carbon source for hepatic lipogenesisCell metabolism · 2024
Summary (written by the submitters)
Through a series of epidemiological analyses, flux analyses, and genetic interventions, we identified amino acid as a primary carbon source in hepatic de novo lipogenesis (DNL), and revealed the vital role of dietary protein in non-alcoholic fatty liver disease (NAFLD). To evaluate whether this finding can be applied to treat hepatic steatosis, we implemented protein restriction in obese mice.
Read the full summary ↓
A short-term low-protein diet (5% kcal protein intake) not only reduced food intake and body weight gain of mice but also ameliorated hepatic lipid accumulation and liver damage. Here, we conducted RNA sequencing for liver samples from these mice and demonstrated a global downregulation of lipogenic genes. Together, this study uncovers a significant role of amino acids in hepatic DNL, and suggests a previously unappreciated target for NAFLD.
Published in
Amino acid is a major carbon source for hepatic lipogenesis
Liao Y, Chen Q, Liu L et al. · Cell metabolism 2024 · PMID 39461344 · doi:10.1016/j.cmet.2024.10.001
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 GSE264493_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 PRJNA1102706 and SRA study SRP503028. Searching any of these in the dataset finder brings you back here.

Study design
6 × SPD vs 6 × LPD

Supports a between-group comparison across 12 samples.

How we read this: 2 replicated groups read from 12 sample titles; they account for 12 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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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.