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Galectin 3-binding protein (Lgals3bp) depletion attenuates hepatic fibrosis by reducing transforming growth factor-β1 (TGF-β1) availability and inhibits hepatocarcinogenesis [PH RNA-seq]

GSE271355 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2024/07/06 Platform GPL30215
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: 3 × Wild type-Veh vs 3 × Wild type-IFNa vs 3 × Lgals3bp KO-Veh vs 3 × Lgals3bp KO-IFNaGroups 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
Galectin 3-binding protein (LGALS3BP) depletion attenuates hepatic fibrosis by reducing transforming growth factor-β1 (TGF-β1) availability and inhibits hepatocarcinogenesisCancer communications (London, England) · 2024
Summary (written by the submitters)
Background: Increased Galectin 3-binding protein (Lgals3bp) serum levels have been used to assess hepatic fibrosis stages and the severity of hepatocellular carcinoma (HCC). Considering the crucial role of transforming growth factor-β1 (TGF-β1) in the emergence of these diseases, the present study tested the hypothesis that Lgals3bp regulates the TGF-β1 signaling pathway.
Read the full summary ↓
Methods: The expression levels of Lgals3bp and TGF-β1 were analyzed in patients with non-alcoholic steatohepatitis (NASH) and HCC. Multiple omics techniques, such as RNA-sequencing, transposase-accessible chromatin-sequencing assay, and liquid chromatography-tandem mass spectrometry proteomics, were used to identify the regulatory mechanisms for the Lgals3bp-TGF-β1 axis. Moreover, the effects of altered TGF-β1 signaling in chronic inflammatory conditions were investigated in conditional Lgals3bp-knockin and Lgals3bp-knockout mice. Results: In patients with NASH and HCC, the levels of Lgals3bp and TGF-β1 exhibited positive correlations. Stimulation of Lgals3bp by the inflammatory cytokine interferon α in HCC cells or ectopic overexpression of Lgals3bp in hepatocytes promoted the expression levels of TGFB1. Aggravated fibrosis was observed in the livers of hepatocyte-specific Lgals3bp knock-in mice, with increased TGF-β1 levels. Lgals3bp directly bound to and assembled integrin αV, an integral mediator required for releasing active TGF-β1 from extracellular latent complex with the rearranged F-actin cytoskeleton. The released TGF-β1 activated JunB transcription factor, which in turn promoted the TGF-β1 positive feedback loop. Lgals3bp deletion in the hepatocytes downregulated TGF-β1 signaling and CCl4 induced fibrosis. Finally, Lgals3bp depletion hindered hepatocarcinogenesis by limiting the availability of fibrogenic TGF-β1. Conclusion: Lgals3bp plays a crucial role in hepatic fibrosis and carcinogenesis by controlling the TGF-β1 signaling pathway, making it a promising therapeutic target in TGF-β1-related diseases.
Published in
Galectin 3-binding protein (LGALS3BP) depletion attenuates hepatic fibrosis by reducing transforming growth factor-β1 (TGF-β1) availability and inhibits hepatocarcinogenesis
Kim DH, Sung M, Park MS et al. · Cancer communications (London, England) 2024 · PMID 39073023 · doi:10.1002/cac2.12600
This dataset
Open GSE271355 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 GSE271355_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 PRJNA1130983 and SRA study SRP517619. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39073023) ↗
Study design
3 × Wild type-Veh vs 3 × Wild type-IFNa vs 3 × Lgals3bp KO-Veh vs 3 × Lgals3bp KO-IFNa

Supports a between-group comparison across 12 samples.

How we read this: 4 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
Datasets on a similar topic

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