GEO series

Ovarian leptin signalling regulates NLRP3 inflammasome activation and macrophage infiltration in a cell specific manner

GSE236196 Mus musculus (mouse) RNA-seq (gene expression) 14 samples Submitted 2024/04/10 Platform GPL19057

At a glance

RNA-seq (gene expression) · mouse · 14 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: 14 samples: 4 × GCs,db,control, vs 4 × GCs,db,mutant, vs 3 × GCs,ob,control, vs 3 × GCs,ob,mutant,Groups 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: Leptin signalling regulates transcriptional differences in granulosa cells from genetically obese mice but not the activation of NLRP3 inflammasomeScientific reports · 2024

Summary (written by the submitters)

Background: Obesity is associated with increased ovarian inflammation and the establishment of local leptin resistance. We presently investigated the role of leptin signalling on Nod-Like Receptor Protein 3 (NLPR3) inflammasome and macrophage prevalence in the pathophysiology of ovarian failure of obese mice.

Read the full summary ↓

Methods: We collected ovaries from: (i) diet induced obese (DIO) mice fed chow diet (CD) or high-fat diet (HFD) for 4 or 16 weeks (wk); (ii) mice lacking the long-isoform of leptin receptor (ObRb; db/db); (iii) mice lacking leptin (ob/ob); and (iv) pharmacologically hyperleptinemic (LEPT) mice for protein and mRNA expression analysis. Next, granulosa cells (GCs) from antral follicles isolated from db/db and ob/ob mice were subjected to transcriptome analysis. Results: We observed no changes in the mRNA and protein levels of NLRP3 inflammasome components in the ovaries of db/db mice, as well as in markers of M1 and M2 macrophage infiltration. This contrasted with the downregulation of NLRP3 inflammasome components and M1 markers in ob/ob -/- and 16 wk HFD mice. Transcriptional analysis revealed opposing profiles between genetic models, with genes associated with steroid metabolism and prostaglandin action in db/db mice and genes controlling extracellular matrix in ob/ob mice being downregulated, despite both processes being crucial for follicular development and ovulation. Conclusions: Leptin signalling regulated NLRP3 inflammasome activation and expression of M1 markers in ovaries of obese mice, in an ObRb-dependent and -independent manner. Absence of changes in the expression of leptin signalling and proinflammatory mediators in GCs from db/db and ob/ob mice was associated with impaired folliculogenesis.

Published in

Leptin signalling regulates transcriptional differences in granulosa cells from genetically obese mice but not the activation of NLRP3 inflammasome

Adamowski M, Sharma Y, Molcan T et al. · Scientific reports 2024 · PMID 38580672 · doi:10.1038/s41598-024-58181-w

This dataset

Open GSE236196 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 GSE236196_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 PRJNA989233 and SRA study SRP446606. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38580672) ↗

Study design

4 × GCs,db,control, vs 4 × GCs,db,mutant, vs 3 × GCs,ob,control, vs 3 × GCs,ob,mutant,

Supports a case/control comparison: 7 samples read as cases, 7 as controls.

How we read this: 4 replicated groups read from 14 sample titles; they account for 14 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

GSM7519584 GCs,db,control, rep1 GCs,db,control,
GSM7519585 GCs,db,control, rep2 GCs,db,control,
GSM7519586 GCs,db,control, rep3 GCs,db,control,
GSM7519587 GCs,db,control, rep4 GCs,db,control,
GSM7519588 GCs,db,mutant, rep1 GCs,db,mutant,
GSM7519589 GCs,db,mutant, rep2 GCs,db,mutant,
GSM7519590 GCs,db,mutant, rep3 GCs,db,mutant,
GSM7519591 GCs,db,mutant, rep4 GCs,db,mutant,
GSM7519592 GCs,ob,control, rep1 GCs,ob,control,
GSM7519593 GCs,ob,control, rep2 GCs,ob,control,
GSM7519594 GCs,ob,control, rep3 GCs,ob,control,
GSM7519595 GCs,ob,mutant, rep1 GCs,ob,mutant,
GSM7519596 GCs,ob,mutant, rep2 GCs,ob,mutant,
GSM7519597 GCs,ob,mutant, rep3 GCs,ob,mutant,

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