Evidence map›Paper›PMID 37292621›Full record

ArticlebioRxiv : the preprint server for biology2023

Transcriptional profiling of mucus production and modification in rhesus macaque endocervical cells under hormonal regulation.

Katrina Rapp, Shuhao Wei, Mackenzie Roberts, Shan Yao, Suzanne S Fei, Lina Gao, Karina Ray, Alexander Wang, Rachelle Godiah, Leo Han

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors at 1 institution in 1 country.

Katrina Rapp
Shuhao Wei
Mackenzie Roberts
Shan Yao
Suzanne S Fei
Lina Gao
Karina Ray
Alexander Wang
Rachelle Godiah
Leo Han
Oregon National Primate Research Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Endocervical mucus production is a key regulator of fertility throughout the menstrual cycle. With cycle-dependent variability in mucus quality and quantity, cervical mucus can either facilitate or block sperm ascension into the upper female reproductive tract. This study seeks to identify genes involved in the hormonal regulation of mucus production, modification, and regulation through profiling the transcriptome of endocervical cells from the non-human primate, the Rhesus Macaque (Macaca mulatta). Design: Experimental. Setting: Translational science laboratory. Intervention: We treated differentiated primary endocervical cultures with estradiol (E2) and progesterone (P4) to mimic peri-ovulatory and luteal-phase hormonal changes. Using RNA-sequencing, we identified differential expression of gene pathways and mucus producing and modifying genes in cells treated with E2 compared to hormone-free conditions and E2 compared to E2-primed cells treated with P4. Main Outcome Measures: We pursued differential gene expression analysis on RNA-sequenced cells. Sequence validation was done using qPCR. Results: Our study identified 158 genes that show significant differential expression in E2-only conditions compared to hormone-free control, and 250 genes that show significant differential expression in P4-treated conditions compared to E2-only conditions. From this list, we found hormone-induced changes in transcriptional profiles for genes across several classes of mucus production, including ion channels and enzymes involved in post-translational mucin modification that have not previously been described as hormonally regulated. Conclusion: Our study is the first to use an

Identifiers

PMID37292621
PMCPMC10245652
OpenAlexW4377098418

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.