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SGLT2 inhibition decreases complement C1q, maintaining renal antibacterial response

GSE307417 Mus musculus (mouse) RNA-seq (gene expression) 14 samples Submitted 2026/01/22 Platform GPL24247
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: 8 × Ctr_UTI vs 5 × SGLT2i_UTIGroups 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
Sodium glucose transporter 2 inhibition maintains kidney antibacterial response by decreasing complement C1qKidney international · 2026
Summary (written by the submitters)
SGLT2 inhibitors, which inhibit glucose reabsorption in the proximal tubule, are well established in the treatment of diabetes mellitus, chronic kidney disease, and heart failure. Although these agents induce glucosuria, clinical trials have consistently shown no increase in renal bacterial infection rates. To investigate the mechanisms that compensate for the increased risk of bacterial growth associated with glucosuria, we employed a pyelonephritis mouse model.
Read the full summary ↓
In this model, approximately 40-day-old C57BL/6J mice were treated with or without empagliflozin and subsequently infected with the uropathogenic Escherichia coli strain 536. RNA-seq data were obtained from kidneys 16 hours post-infection.
Published in
Sodium glucose transporter 2 inhibition maintains kidney antibacterial response by decreasing complement C1q
Sendtner GW, Miranda J, Naumann P et al. · Kidney international 2026 · PMID 41565025 · doi:10.1016/j.kint.2026.01.003
This dataset
Open GSE307417 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 GSE307417_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 PRJNA1321587 and SRA study SRP618232. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41565025) ↗
Study design
8 × Ctr_UTI vs 5 × SGLT2i_UTI

Supports a between-group comparison across 13 samples.

How we read this: 2 replicated groups read from 14 sample titles; they account for 13 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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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.