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Inhibition of the non-lysosomal glucosylceramidase GBA2 accounts for the efficacy of sinbaglustat in a murine model of Sandhoff disease

GSE289884 Mus musculus (mouse) RNA-seq (gene expression) 56 samples Submitted 2026/02/18 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 56 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
56 samples: 21 conditions, mostly in duplicateGroups 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
Sinbaglustat is efficacious in GM2 gangliosidosis primarily through inhibition of GBA2 rather than GCSMolecular therapy. Advances · 2026
Summary (written by the submitters)
Sinbaglustat is a small molecule under investigation as a treatment for lysosomal storage disorders. It inhibits two enzymes involved in glycosphingolipid (GSL) metabolism, the non-lysosomal glucosylceramidase (GBA2) and, with a lower affinity, glucosylceramide synthase (GCS). High doses of structurally related dual GBA2/GCS inhibitors such as miglustat, and pure GCS inhibitors have shown efficacy in a transgenic mouse model (Hexb-/-) of Sandhoff disease, aGM2 gangliosidosis.
Read the full summary ↓
Whether the inhibition of GBA2 alone confers protection is unknown. We first assessed the functional profile of sinbaglustat relating to GBA2 and GCS inhibition in cellular models and wild-type (WT) mice. Then we investigated sinbaglustat’s potential efficacy in preventing disease manifestation in Hexb-/- mice upon chronic treatment with two dose levels (30 and 300 mg/kg/day), aimed at predominant inhibition of GBA2 or GBA2 and GCS. Non-treated WT mice served as controls. Sinbaglustat inhibited GBA2 more potently than GCS in GM2 gangliosidosis patient-derived fibroblasts and primary mouse neurons. The dual inhibition profile was also reflected in a dose-dependent, bell-shaped modulation of glucosylceramide, the substrate of GBA2 and product of GCS , in wild-type mouse brain. At the two sinbaglustat dose levels that we tested in Hexb-/- mice, glucosylceramide levels in cerebral cortex were elevated, indicating GBA2 target engagement. Only the high 300 mg/kg/day dose reduced in addition the levels of various other GSLs compared to Hexb-/- untreated mice, demonstrating GCS target engagement in the central nervous system. In the liver and plasma, sinbaglustat dose-dependently decreased many GSLs including hexosaminidase A substrates. Sinbaglustat partially reversed the increase in mRNA expression of several neuroinflammation-related genes in the hippocampus of Hexb-/- mice. Sinbaglustat treatment at both dose levels delayed the age at which Hexb-/- mice started to develop motor impairments by about 2-4 weeks. The 30 mg/kg/day dose increased median survival of Hexb-/- mice by approximately 13.5%, the 300 mg/kg/day dose by approximately 18%. The study indicates that sinbaglustat could represent a viable treatment option for GM2 gangliosidosis if data from the mouse model translate to human. Importantly, our data also reveal that GBA2 inhibition in the central nervous system is largely responsible for sinbaglustat’s therapeutic effect in Hexb/- mice.
Published in
Sinbaglustat is efficacious in GM2 gangliosidosis primarily through inhibition of GBA2 rather than GCS
Steiner MA, Vaillant C, Garzotti M et al. · Molecular therapy. Advances 2026 · PMID 42157966 · doi:10.1016/j.omta.2025.201658
This dataset
Open GSE289884 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 GSE289884_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 PRJNA1225244 and SRA study SRP564810. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42157966) ↗
Study design
21 conditions, mostly in duplicate
HexBHD_11E ×4 HexB_9B ×3 HexBLD_10F ×3 WT_10A ×2 WT_11A ×2 WT_11D ×2 WT_12B ×2 WT_12D ×2 +23 more

Supports a between-group comparison across 46 samples.

How we read this: 21 replicated groups read from 56 sample titles; they account for 46 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

+ 16 more — browse all 56 samples with per-sample file links →

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