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Upregulation of inflammatory genes and pathways links obesity to severe COVID-19

GSE267021 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2024/05/21 Platform GPL30172Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 24 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
24 samples: 24 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)
Obesity is a risk factor for developing severe COVID-19. However, the mechanism underlying obesity-accelerated COVID-19 remains unclear. Here, we report results from a study in which K18-hACE2 mice were fed an obesity-inducing western diet (WD) for over 3 months before intranasal infection with SARS-CoV2. After infection, the WD-fed K18-hACE2 mice lost more body weight and had more severe lung inflammation than normal chow (NC)-fed mice.
Read the full summary ↓
Bulk RNAseq analysis of lungs and adipose tissue revealed that a diverse landscape of various immune cells, inflammatory markers, and pathways are upregulated in obese COVID-19 patients or the WD-fed K18-hACE2 mice when compared with their respective control groups. When compared with infected NC-fed mice in the lung, the infected WD-fed mice had upregulation of IL-6, a well-established marker for severe COVID-19. These results indicate that obesity-accelerated severe COVID-19 caused by SARS-CoV-2 infection in the K18-hACE2 mouse model can be used for dissecting the cellular and molecular mechanisms underlying pathogenesis. Furthermore, in the transcriptome analysis of the lung and adipose tissue obtained from deceased COVID-19 patients, we found upregulation of an array of genes and pathways associated with Inflammation. Both the K18-hACE2 mouse model and human COVID-19 patient data support a link between inflammation and an obesity-accelerated COVID-19 disease phenotype.
This dataset
Open GSE267021 in finder → Find more 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 GSE267021_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 PRJNA1109355 and SRA study SRP506555. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
24 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 24 sample titles: 24 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.