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Evidence for a role of human blood-borne factors in mediating age-associated changes in molecular circadian rhythms (preliminary experiment)

GSE270289 Homo sapiens (human) RNA-seq (gene expression) 96 samples Submitted 2024/06/26 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 96 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
Homo sapiens (human)
Samples
96 samples: 24 × Young subject serum treatment, replicate A, CT vs 24 × Young subject serum treatment, replicate B, CT vs 24 × Old subject serum treatment, replicate A, CT vs 24 × Old subject serum treatment, replicate B, CTGroups 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
Evidence for a role of human blood-borne factors in mediating age-associated changes in molecular circadian rhythmseLife · 2024
Summary (written by the submitters)
Aging is associated with a number of physiologic changes including perturbed circadian rhythms; however, mechanisms by which rhythms are altered remain unknown. To test the idea that circulating factors mediate age-dependent changes in peripheral rhythms, we compared the ability of human serum from young and old individuals to synchronize circadian rhythms in culture.We collected blood from apparently healthy young (age 25-30) and old (age 70-76) individuals and used the serum to synchronize cultured fibroblasts.
Read the full summary ↓
We found that young and old sera are equally competent at initiating robust ~24h oscillations of a luciferase reporter driven by clock gene promoter. However, cyclic gene expression is affected, such that young and old sera promote cycling of sets of different genes. Genes that lose rhythmicity with old serum entrainment are associated with oxidative phosphorylation and Alzheimer’s Disease as identified by STRING and IPA analyses. Also, the expression of cycling genes associated with cholesterol biosynthesis increased in the cells entrained with old serum. Genes involved in the cell cycle and transcription/translation remain rhythmic in both conditions, We did not observe a global difference in the distribution of phase between groups, but found that peak expression of several clock-controlled genes (PER3, NR1D1, NR1D2, CRY1, CRY2, and TEF) lagged in the cells synchronized ex vivo with old serum. Taken together, these findings demonstrate that age-dependent blood-borne factors affect peripheral circadian rhythms in cells and have the potential to impact health and disease via maintaining or disrupting rhythms respectively.
Published in
Evidence for a role of human blood-borne factors in mediating age-associated changes in molecular circadian rhythms
Schwarz JE, Mrčela A, Lahens NF et al. · eLife 2024 · PMID 39485282 · doi:10.7554/eLife.88322
This dataset
Open GSE270289 in finder → Find more human 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 GSE270289_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 PRJNA1126134 and SRA study SRP514997. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39485282) ↗
Study design
24 × Young subject serum treatment, replicate A, CT vs 24 × Young subject serum treatment, replicate B, CT vs 24 × Old subject serum treatment, replicate A, CT vs 24 × Old subject serum treatment, replicate B, CT

Supports a between-group comparison across 96 samples.

How we read this: 4 replicated groups read from 96 sample titles; they account for 96 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

+ 56 more — browse all 96 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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