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The Myc-Like Mlx Network Impacts Aging and Metabolism

GSE248073 Mus musculus (mouse) RNA-seq (gene expression) 27 samples Submitted 2024/10/10 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 27 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
27 samples: 27 samples, each with a distinct title — no replicated groupsGroups 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
Body-Wide Inactivation of the Myc-Like Mlx Transcription Factor Network Accelerates Aging and Increases the Lifetime Cancer IncidenceAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
Summary (written by the submitters)
The “Mlx” and “Myc” Networks share many common gene targets. Just as Myc’s activity depends upon its heterodimerization with Max, the Mlx Network requires that the Max-like factor Mlx associate with the Myc-like factors MondoA or ChREBP. We show here that body-wide Mlx inactivation, like that of Myc, accelerates numerous aging-related phenotypes pertaining to body habitus and metabolism.
Read the full summary ↓
The deregulation of numerous aging-related Myc target gene sets is also accelerated. Among other functions, these gene sets often regulate ribosomal and mitochondrial structure and function, genomic stability and aging. Whereas “MycKO” mice have an extended lifespan because of a lower cancer incidence, “MlxKO” mice have normal lifespans and a somewhat higher cancer incidence. Like Myc, Mlx, MondoA and ChREBP expression and that of their target genes, deteriorate with age in both mice and humans, underscoring the importance of life-long and balanced cross-talk between the two Networks to maintain normal aging.
Published in
Body-Wide Inactivation of the Myc-Like Mlx Transcription Factor Network Accelerates Aging and Increases the Lifetime Cancer Incidence
Wang H, Stevens T, Lu J et al. · Advanced science (Weinheim, Baden-Wurttemberg, Germany) 2024 · PMID 38976573 · doi:10.1002/advs.202401593
This dataset
Open GSE248073 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 GSE248073_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 PRJNA1041729 and SRA study SRP472514. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38976573) ↗
Study design
27 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 27 sample titles: 27 distinct titles with little repetition. 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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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.