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Ligature-induced periodontitis in mice potentially accelerates CD4+ T cells senescence and exacerbates rheumatoid arthritis

GSE318778 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2026/05/16 Platform GPL34871
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: 12 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.
Summary (written by the submitters)
Objective: Chronic periodontitis typically affects individuals aged 40–50. Aging impairs immunity, sustains chronic inflammation, and enhances autoimmunity, a process called "immunosenescence", which contributes to the development of type 2 diabetes and rheumatoid arthritis (RA). Thymic involution after puberty reduces naive T cells, promoting senescent CD4+ T cells.
Read the full summary ↓
The reason is that senescent T cells attempt to complement this reduction by proliferating to their limits, maintaining their absolute numbers through “homeostatic proliferation”. These cells, characterized by PD-1/CD153 expression, show reduced proliferation and increased senescence-associated secretory phenotype (SASP) cytokines. This study investigates how ligature-induced periodontitis (LIP) in mice affects CD4+ T cell senescence. Materials and Methods: Balb/c mice aged 5, 10, 18, 26, 34, and 42 weeks received LIP by silk ligation around maxillary second molars. Splenic CD4+ T cells were extracted and cultured with IL-2, anti-TCRβ, and anti-CD28 for 1 to 3 days to mimic homeostatic proliferation. Senescence markers were compared between LIP and control groups before and after in vitro stimulation. RNA sequencing (RNA-seq) analysis was performed to identify differentially expressed genes. Results: No significant difference was observed in PD-1/CD153 double-positive cells between the LIP group and the control group at any age of mice. However, after in vitro stimulation, the LIP group showed a higher proportion of PD-1/CD153 double-positive cells, peaking at 18 weeks. SASP cytokines (osteopontin, IL-6) in the LIP group were higher than that in the control graoup. Senescence-associated β-galactosidase (SAβ-gal)-positive CD4+ T cells were more prevalent, indicating accelerated senescence in LIP group. Additionally, fewer cells in LIP group were in the S phase, suggesting cell cycle arrest. RNA-seq suggested "inflammatory response," "T cell differentiation," “STAT protein phosphorylation," and “JAK-STAT signalling pathway" in the LIP group. Among the upregulated genes in the LIP group, Cx3cr1, a marker selectively expressed by senescent CD4⁺ T cells; Il1rn, which reflects the inflammation-regulating capacity of senescent CD4⁺ T cells by antagonizing IL-1β signalling; Il4, indicative of Th2 differentiation and B cell activation via the JAK/STAT6 pathway; and Endog, a mitochondrial nuclease implicated in age-related cellular stress and telomere dysfunction, were ranked high. Conclusions: These results suggest that LIP accelerates CD4+ T cell senescence, potentially exacerbating systemic inflammation. Future experiments will involve transferring splenic CD4+ T cells from LIP and control groups into T cell-deficient mice to evaluate in vivo homeostatic proliferation and its role in experimental RA.
This dataset
Open GSE318778 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 GSE318778_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 PRJNA1418973. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
12 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 12 sample titles: 12 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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