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Record W4403237413 · doi:10.3389/fendo.2024.1454006

Estradiol-mediated enhancement of the human ectocervical epithelial barrier correlates with desmoglein-1 expression in the follicular menstrual phase

2024· article· en· W4403237413 on OpenAlexaff
Frideborg Bradley, Alexandra Minna Stern, Mathias Franzén Boger, Zaynab Mousavian, Olga Dethlefsen, Vilde Kaldhusdal, Julie Lajoie, Kenneth Omollo, Sofia Bergström, Anna Månberg, Peter Nilsson, Joshua Kimani, Adam Burgener, Annelie Tjernlund, Christopher Sundling, Keith R. Fowke, Kristina Broliden

Bibliographic record

VenueFrontiers in Endocrinology · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicReproductive tract infections research
Canadian institutionsUniversity of Manitoba
FundersKarolinska Institutet
KeywordsFollicular phaseLuteal phaseMenstrual cycleGene expressionBiologyEstrogenProgesterone receptorGene expression profilingInternal medicineAndrologyEndocrinologyHormoneGeneMedicineEstrogen receptorGenetics

Abstract

fetched live from OpenAlex

Background The cervicovaginal epithelial barrier is crucial for defending the female reproductive tract against sexually transmitted infections. Hormones, specifically estradiol and progesterone, along with their respective receptor expressions, play an important role in modulating this barrier. However, the influence of estradiol and progesterone on gene and protein expression in the ectocervical mucosa of naturally cycling women is not well understood. Methods Mucosal and blood samples were collected from Kenyan female sex workers at high risk of sexually transmitted infections. All samples were obtained at two time points, separated by two weeks, aiming for the follicular and luteal phases of the menstrual cycle. Ectocervical tissue biopsies were analyzed by RNA-sequencing and in situ immunofluorescence staining, cervicovaginal lavage samples (CVL) were evaluated using protein profiling, and plasma samples were analyzed for hormone levels. Results Unsupervised clustering of RNA-sequencing data was performed using Weighted gene co-expression network analysis (WGCNA). In the follicular phase, estradiol levels positively correlated with a gene module representing epithelial structure and function, and negatively correlated with a gene module representing cell cycle regulation. These correlations were confirmed using regression analysis including adjustment for bacterial vaginosis status. Using WGCNA, no gene module correlated with progesterone levels in the follicular phase. In the luteal phase, no gene module correlated with either estradiol or progesterone levels. Protein profiling on CVL revealed that higher levels of estradiol during the follicular phase correlated with increased expression of epithelial barrier integrity markers, including DSG1. This contrasted to the limited correlations of protein expression with estradiol levels in the luteal phase. In situ imaging analysis confirmed that higher estradiol levels during the follicular phase correlated with increased DSG1 expression. Conclusion We demonstrate that estradiol levels positively correlate with specific markers of ectocervical epithelial structure and function, particularly DSG1, during the follicular phase of the menstrual cycle. Neither progesterone levels during the follicular phase nor estradiol and progesterone levels during the luteal phase correlated with any specific sets of gene markers. These findings align with the expression of estradiol and progesterone receptors in the ectocervical epithelium during these menstrual phases.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.284
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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