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Maternal obesity influences transcriptomic and metagenomic profiles in term neonates

GSE303891 Homo sapiens (human) RNA-seq (gene expression) 38 samples Submitted 2026/07/29 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 38 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
38 samples: 38 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)
Maternal obesity impacts neonatal outcomes; however, its influence on fetal gene expression and early gut microbiota composition remains insufficiently characterized. This prospective study was conducted in 62 full-term neonates between 2022 and 2024, analyzing transcriptomic profiles from umbilical-cord blood and metagenomic sequencing of fecal samples at 4 months.
Read the full summary ↓
The cohort included 31 neonates born to mothers with normal BMI (NBMI) and 31 neonates born to mothers with overweight or obesity (OBMI). Major congenital anomalies, maternal exposure to drugs or antibiotics were exclusion criteria. OBMI neonates demonstrated significant upregulation of genes related to inflammation (MPO, OLR1) and cell-cycle dysregulation (GADD45G, CCNA1). Evidence of altered lysosomal function was observed, with increased expression of CTSG and CLTCL1 and reduced expression of HYAL1. Microbiome analysis revealed a decrease in Bacteroidota and an increase in Proteobacteria in cesarean-delivered neonates. Holdemanella was enriched in vaginally delivered OBMI neonates, suggesting a potential adaptive microbial response. Enrichment of opportunistic pathogens, including Klebsiella, Morganella, and Enterococcus, was observed in cesarean-delivered neonates, indicating a combined effect of maternal obesity and delivery mode on early microbial colonization. These findings suggest that maternal obesity modifies neonatal immune and metabolic gene expression while reshaping gut microbiome composition, with potential implications for long-term health.
This dataset
Open GSE303891 in finder → Find more human 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 GSE303891_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 PRJNA1298181 and SRA study SRP604223. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
38 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 38 sample titles: 38 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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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.