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Gastric Bypass Elicits Persistent Gut Adaptation and Unique Diabetes Remission-Related Metabolic Gene Regulation

GSE281598 Homo sapiens (human) RNA-seq (gene expression) 56 samples Submitted 2025/11/11 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 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
Homo sapiens (human)
Samples
56 samples: 56 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.
Paper
Gastric bypass elicits persistent gut adaptation and unique diabetes remission-related metabolic gene regulationObesity (Silver Spring, Md.) · 2024
Summary (written by the submitters)
Abstract: Objective: We have previously shown that early intestinal adaptation precedes and relates to metabolic improvement in humans after Roux-en-Y gastric bypass surgery (RYGB). We hypothesized that intestinal adaptation would persist at the one-year postoperative timepoint, and that gene expression (GE) signatures would relate to Type 2 diabetes (T2D) remission, providing insight into potential mechanisms for intestinally-mediated metabolic improvement after RYGB.
Read the full summary ↓
Methods: We determined GE by RNA-seq in jejunum (Roux limb, RL) collected from 28 patients before and 12 months after RYGB. Results: Global GE from paired baseline and 1-year jejunal samples did not separate according to clinical phenotype (T2D remission, sustained weight loss). In general, GE was consistent with persistent RL remodeling, and microvilli were elongated by 39%. Remodeling was not attenuated in patients with lack of diabetes remission or with weight regain. Patients with diabetes remission demonstrated greater jejunal activation of lipogenesis-related pathways driven by RXR, LXR and SREBP. Conclusions: RL adaptation is a key feature of RYGB in all patients, likely reflecting the dramatic alterations to gastrointestinal anatomy, but jejunal lipogenesis appears to be more strongly activated in those patients with diabetes remission. Further study is needed to understand whether these pathways may drive metabolic remission after RYGB.
Published in
Gastric bypass elicits persistent gut adaptation and unique diabetes remission-related metabolic gene regulation
Stefater-Richards MA, Panciotti C, Esteva V et al. · Obesity (Silver Spring, Md.) 2024 · PMID 39410706 · doi:10.1002/oby.24135
This dataset
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.

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 GSE281598_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1184774 and SRA study SRP544721. Searching any of these in the dataset finder brings you back here.

Study design
56 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 56 sample titles: 56 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

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

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.