Gut microbiota dysbiosis rewires host mRNA m6A epitranscriptomic landscape via bile acid metabolism
RNA-seq (gene expression) · mouse · 102 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
- 102 samples: 34 conditions, mostly in triplicateGroups 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
- Antibiotic-Induced Gut Microbiota Dysbiosis Modulates Host Transcriptome and m(6)A Epitranscriptome via Bile Acid MetabolismAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
Read the full summary ↓
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE216620_RAW.tar (it can run to tens of gigabytes).
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 PRJNA894523 and SRA study SRP404680. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 102 samples.
How we read this: 34 replicated groups read from 102 sample titles; they account for 102 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.
- GSM6684543 Abx_brain-Input1 Abx_brain-Input
- GSM6684544 Abx_brain-Input2 Abx_brain-Input
- GSM6684545 Abx_brain-Input3 Abx_brain-Input
- GSM6684546 Abx_heart-1 Abx_heart
- GSM6684547 Abx_heart-2 Abx_heart
- GSM6684548 Abx_heart-3 Abx_heart
- GSM6684549 Abx_intestine-Input1 Abx_intestine-Input
- GSM6684550 Abx_intestine-Input2 Abx_intestine-Input
- GSM6684551 Abx_intestine-Input3 Abx_intestine-Input
- GSM6684552 Abx_kidney-1 Abx_kidney
- GSM6684553 Abx_kidney-2 Abx_kidney
- GSM6684554 Abx_kidney-3 Abx_kidney
- GSM6684555 Abx_liver-Input1 Abx_liver-Input
- GSM6684556 Abx_liver-Input2 Abx_liver-Input
- GSM6684557 Abx_liver-Input3 Abx_liver-Input
- GSM6684558 Abx_lung-1 Abx_lung
- GSM6684559 Abx_lung-2 Abx_lung
- GSM6684560 Abx_lung-3 Abx_lung
- GSM6684561 Abx_spleen-1 Abx_spleen
- GSM6684562 Abx_spleen-2 Abx_spleen
- GSM6684563 Abx_spleen-3 Abx_spleen
- GSM6684564 Abx_testis-1 Abx_testis
- GSM6684565 Abx_testis-2 Abx_testis
- GSM6684566 Abx_testis-3 Abx_testis
- GSM6684567 Amp_brain-Input1 Amp_brain-Input
- GSM6684568 Amp_brain-Input2 Amp_brain-Input
- GSM6684569 Amp_brain-Input3 Amp_brain-Input
- GSM6684570 Amp_heart-1 Amp_heart
- GSM6684571 Amp_heart-2 Amp_heart
- GSM6684572 Amp_heart-3 Amp_heart
- GSM6684573 Amp_intestine-Input1 Amp_intestine-Input
- GSM6684574 Amp_intestine-Input2 Amp_intestine-Input
- GSM6684575 Amp_intestine-Input3 Amp_intestine-Input
- GSM6684576 Amp_kidney-1 Amp_kidney
- GSM6684577 Amp_kidney-2 Amp_kidney
- GSM6684578 Amp_kidney-3 Amp_kidney
- GSM6684579 Amp_liver-Input1 Amp_liver-Input
- GSM6684580 Amp_liver-Input2 Amp_liver-Input
- GSM6684581 Amp_liver-Input3 Amp_liver-Input
- GSM6684582 Amp_lung-1 Amp_lung
+ 62 more — browse all 102 samples with per-sample file links →
- GSE313697 High-fat diet rewires gut host-microbiota relationship to derive worse outcomes following Clostridioides difficile infection 72 samples shares microbiota, rewires, host
- GSE322803 Gut Microbiota Dysbiosis Remodels the Multi-Tissue Transcriptional Landscape of the Host 54 samples shares microbiota, dysbiosis, host
- GSE265876 A microbiota-derived bile acid controls hepatic cholesterol levels in cachectic mice 22 samples shares microbiota, bile, acid
- GSE307734 Microbiota-derived bile acid ameliorates lung inflammation through metabolic rewiring of alveolar macrophages 12 samples shares microbiota, bile, acid
- GSE293180 Analysis of Host and Microbiome Responses to Antibiotics Reveals Microbiota-Independent Effects on Gut Immune Response [multiabx] 97 samples shares microbiota, host
- GSE293029 Analysis of Host and Microbiome Responses to Antibiotics Reveals Microbiota-Independent Effects on Gut Immune Response 64 samples shares microbiota, host
- GSE273242 Gut microbiota drives vitiligo-related oxidative stress in skin via the metabolite Hippuric Acid 38 samples shares microbiota, acid
- GSE299495 Maternal Helminths Rewire the Microbiota to Promote Offspring Antiviral Immunity via Indole-3-Propionic Acid 28 samples shares microbiota, acid
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.