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High-Throughput Transcriptomics Toxicity Assessment of Eleven Data-Poor Bisphenol A Alternatives

GSE271332 Homo sapiens (human) RNA-seq (gene expression) 286 samples Submitted 2024/08/26 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 286 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
286 samples: 278 distinct titles, 4 of them repeatedGroups 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
High-throughput transcriptomics toxicity assessment of eleven data-poor bisphenol A alternativesEnvironmental pollution (Barking, Essex : 1987) · 2024
Summary (written by the submitters)
Bisphenol A (BPA), a widely used chemical in the production of plastics and epoxy resins, has garnered significant attention due to its association with adverse health effects, particularly its endocrine-disrupting properties. Regulatory measures aimed at reducing human exposure to BPA have led to a proliferation of alternative chemicals used in various consumer products.
Read the full summary ↓
While these alternatives serve to reduce BPA exposure, concerns have arisen regarding their safety and potential toxicity as regrettable substitutes. Previous efforts have demonstrated that in vitro high-throughput transcriptomics (HTTr) studies can be used to assess the endocrine-disrupting potential of BPA alternatives, and this strategy produces transcriptomic points-of-departure (tPoDs) that are protective of human health when compared to the PODs from traditional rodent studies. In this study, we leveraged in vitro HTTr advancements to assess the toxicity of eleven data-poor legacy chemicals sharing structural similarities to BPA. Human breast cancer MCF-7 cells were exposed to BPA and 11 alternatives at concentrations ranging from (0.1 - 25 µM) to assess toxicity. Analysis of global transcriptomic changes and a previously characterized estrogen receptor alpha (ERα) biomarker signature revealed that 9/11 chemicals were bioactive. One of the chemicals (2,4’-Bisphenol A) activated the ERα biomarker at the same concentration as BPA (i.e., 4,4’-BPA) but was deemed to be more potent as it induced global transcriptomic changes at lower concentrations. These results are addressing data gaps in ongoing screening assessments to identify BPA alternatives with hazard potential and are helping to identify potential candidates to serve as safer alternatives.
Published in
High-throughput transcriptomics toxicity assessment of eleven data-poor bisphenol A alternatives
Beal MA, Coughlan MC, Nunnikhoven A et al. · Environmental pollution (Barking, Essex : 1987) 2024 · PMID 39197648 · doi:10.1016/j.envpol.2024.124827
This dataset
Open GSE271332 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 GSE271332_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 PRJNA1130946 and SRA study SRP517606. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39197648) ↗
Study design
278 distinct titles, 4 of them repeated
Brain ×3 Universal ×3 Media ×3 1X Lysis Buffe ×3

How we read this: Read from 286 sample titles: 278 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

+ 246 more — browse all 286 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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