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SLC15A3-Mediated Dipeptide Metabolism Confers Antimetabolite Resistance in Lymphoma via mTORC1 Activation [Ribo-seq]

GSE328313 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2026/05/25 Platform GPL21103
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 × Eμ-Myc/+ RNA biol vs 3 × Eμ-Myc/+;Prps2^null RNA biol vs 3 × Eμ-Myc/+ RPF biol vs 3 × Eμ-Myc/+;Prps2^null RPF biolGroups 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)
Antimetabolites, chemotherapy targeting nucleotide biosynthesis, are among the oldest and most widely used cancer treatments, yet resistance remains a daunting barrier, especially in the fight against B-cell lymphomas. However, the underlying mechanisms of this resistance have long remained elusive. Using an innovative, integrated omics approach, we unexpectedly identified that the accumulation of dipeptides and upregulation of the dipeptide transporter SLC15A3 underlie resistance to nucleotide deficiency in a Myc-driven large B cell lymphoma mouse model.
Read the full summary ↓
A similar mechanism occurs after long treatment of human B cell lymphoma cells with the chemotherapeutic purine synthesis inhibitor 6-mercaptopurine (6MP). Mechanistically, we demonstrate that dipeptides containing essential amino acids activate the growth and survival mTORC1 signaling pathway. Notably, SLC15A3 specifically interacts with mTOR on the lysosome, boosting mTORC1 activity selectively in resistant lymphoma cells but not in the parental cancer cells. Silencing SLC15A3 diminishes mTORC1 activity and restores resistant lymphoma sensitivity to 6MP. Strikingly, resistant lymphomas, but not primary tumors, exhibit heightened sensitivity to the clinical mTOR inhibitor, rapamycin, in culture and in vivo. We extend these findings in human lymphoma biopsies, which reveal increased SLC15A3 expression following antimetabolite therapy. Together, our study uncovers a previously unrecognized metabolic adaptation that fuels cancer resistance to nucleotide deficiency and positions mTORC1 inhibitor, rapamycin, as a potential therapeutic strategy for transforming the management of chemotherapy-resistant lymphomas.
This dataset
Open GSE328313 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 GSE328313_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 PRJNA1454555 and SRA study SRP692558. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
3 × Eμ-Myc/+ RNA biol vs 3 × Eμ-Myc/+;Prps2^null RNA biol vs 3 × Eμ-Myc/+ RPF biol vs 3 × Eμ-Myc/+;Prps2^null RPF biol

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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