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Persistent organic pollutants dysregulate energy homeostasis in human ovaries in vitro

GSE252776 Homo sapiens (human) RNA-seq (gene expression) 242 samples Submitted 2024/05/03 Platform GPL21697
At a glance

RNA-seq (gene expression) · human · 242 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
242 samples: 200 distinct titles, 21 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
Persistent organic pollutants dysregulate energy homeostasis in human ovaries in vitroEnvironment international · 2024
Summary (written by the submitters)
Exposure to persistent organic pollutants (POPs), such as dichlorodiphenyltrichloroethane (DDT) and polychlorinated biphenyls (PCBs), has historically been linked to population collapses in wildlife. Despite international regulations, these legacy chemicals are still currently detected in women of reproductive age, and their levels correlate with reduced ovarian reserve, longer time-to-pregnancy, and higher risk of infertility.
Read the full summary ↓
However, the specific modes of action underlying these associations remain unclear. Here, we examined the effects of five commonly occurring POPs − hexachlorobenzene (HCB), p,p'-dichlorodiphenyldichloroethylene (DDE), 2,3,3′,4,4′,5-hexachlorobiphenyl (PCB156), 2,2′,3,4,4′,5,5′-heptachlorobiphenyl (PCB180), perfluorooctane sulfonate (PFOS) − and their mixture on human ovaries in vitro. We exposed human ovarian cancer cell lines COV434, KGN, and PA1 as well as primary ovarian cells for 24 h, and ovarian tissue containing unilaminar follicles for 6 days. RNA-sequencing of samples exposed to concentrations covering epidemiologically relevant levels revealed significant gene expression changes related to central energy metabolism in the exposed cells, indicating glycolysis, oxidative phosphorylation, fatty acid metabolism, and reactive oxygen species as potential shared targets of POP exposures in ovarian cells. Alpha-enolase (ENO1), lactate dehydrogenase A (LDHA), cytochrome C oxidase subunit 4I1 (COX4I1), ATP synthase F1 subunit alpha (ATP5A), and glutathione peroxidase 4 (GPX4) were validated as targets through qPCR in additional cell culture experiments in KGN. In ovarian tissue cultures, we observed significant effects of exposure on follicle growth and atresia as well as protein expression. All POP exposures, except PCB180, decreased unilaminar follicle proportion and increased follicle atresia. Immunostaining confirmed altered expression of LDHA, ATP5A, and GPX4 in the exposed tissues. Moreover, POP exposures modified ATP production in KGN and tissue culture. In conclusion, our results demonstrate the disruption of cellular energy metabolism as a novel mode of action underlying POP-mediated interference of follicle growth in human ovaries.
Published in
Persistent organic pollutants dysregulate energy homeostasis in human ovaries in vitro
Li T, Björvang RD, Hao J et al. · Environment international 2024 · PMID 38701644 · doi:10.1016/j.envint.2024.108710
This dataset
Open GSE252776 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 GSE252776_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 PRJNA1062431 and SRA study SRP482664. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38701644) ↗
Study design
200 distinct titles, 21 of them repeated
COV434, MIX_100X, P27A_P ×3 Primary, HCB_100X, patient ×3 Primary, HCB_10X, patient ×3 Primary, HCB_1X, patient ×3 Primary, DDE_100X, patient ×3 Primary, DDE_10X, patient ×3 Primary, DDE_1X, patient ×3 Primary, PCB156_100X, patient ×3 +13 more

Supports a case/control comparison: 54 samples read as cases, 9 as controls.

How we read this: Read from 242 sample titles: 200 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

+ 202 more — browse all 242 samples with per-sample file links →

Datasets on a similar topic

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