17 research outputs found
Establishment of a novel human fetal adrenal culture model that supports de novo and manipulated steroidogenesis
Dexamethasone affects human fetal adrenal steroidogenesis and subsequent ACTH response in an ex vivo culture model
Identification of a window of androgen sensitivity for somatic cell function in human fetal testis cultured ex vivo
BACKGROUND: Reduced androgen action during early fetal development has been suggested as the origin of reproductive disorders comprised within the testicular dysgenesis syndrome (TDS). This hypothesis has been supported by studies in rats demonstrating that normal male development and adult reproductive function depend on sufficient androgen exposure during a sensitive fetal period, called the masculinization programming window (MPW). The main aim of this study was therefore to examine the effects of manipulating androgen production during different timepoints during early human fetal testis development to identify the existence and timing of a possible window of androgen sensitivity resembling the MPW in rats. METHODS: The effects of experimentally reduced androgen exposure during different periods of human fetal testis development and function were examined using an established and validated human ex vivo tissue culture model. The androgen production was reduced by treatment with ketoconazole and validated by treatment with flutamide which blocks the androgen receptor. Testicular hormone production ex vivo was measured by liquid chromatography-tandem mass spectrometry or ELISA assays, and selected protein markers were assessed by immunohistochemistry. RESULTS: Ketoconazole reduced androgen production in testes from gestational weeks (GW) 7–21, which were subsequently divided into four age groups: GW 7–10, 10–12, 12–16 and 16–21. Additionally, reduced secretion of testicular hormones INSL3, AMH and Inhibin B was observed, but only in the age groups GW 7–10 and 10–12, while a decrease in the total density of germ cells and OCT4(+) gonocytes was found in the GW 7–10 age group. Flutamide treatment in specimens aged GW 7–12 did not alter androgen production, but the secretion of INSL3, AMH and Inhibin B was reduced, and a reduced number of pre-spermatogonia was observed. CONCLUSIONS: This study showed that reduced androgen action during early development affects the function and density of several cell types in the human fetal testis, with similar effects observed after ketoconazole and flutamide treatment. The effects were only observed within the GW 7–14 period—thereby indicating the presence of a window of androgen sensitivity in the human fetal testis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02602-y
Competence Classification of Cumulus and Granulosa Cell Transcriptome in Embryos Matched by Morphology and Female Age
<div><p>Objective</p><p>By focussing on differences in the mural granulosa cell (MGC) and cumulus cell (CC) transcriptomes from follicles resulting in competent (live birth) and non-competent (no pregnancy) oocytes the study aims on defining a competence classifier expression profile in the two cellular compartments. Design: A case-control study. Setting: University based facilities for clinical services and research. Patients: MGC and CC samples from 60 women undergoing IVF treatment following the long GnRH-agonist protocol were collected. Samples from 16 oocytes where live birth was achieved and 16 age- and embryo morphology matched incompetent oocytes were included in the study.</p><p>Methods</p><p>MGC and CC were isolated immediately after oocyte retrieval. From the 16 competent and non-competent follicles, mRNA was extracted and expression profile generated on the Human Gene 1.0 ST Affymetrix array. Live birth prediction analysis using machine learning algorithms (support vector machines) with performance estimation by leave-one-out cross validation and independent validation on an external data set.</p><p>Results</p><p>We defined a signature of 30 genes expressed in CC predictive of live birth. This live birth prediction model had an accuracy of 81%, a sensitivity of 0.83, a specificity of 0.80, a positive predictive value of 0.77, and a negative predictive value of 0.86. Receiver operating characteristic analysis found an area under the curve of 0.86, significantly greater than random chance. When applied on 3 external data sets with the end-point outcome measure of blastocyst formation, the signature resulted in 62%, 75% and 88% accuracy, respectively. The genes in the classifier are primarily connected to apoptosis and involvement in formation of extracellular matrix. We were not able to define a robust MGC classifier signature that could classify live birth with accuracy above random chance level.</p><p>Conclusion</p><p>We have developed a cumulus cell classifier, which showed a promising performance on external data. This suggests that the gene signature at least partly include genes that relates to competence in the developing blastocyst.</p></div
Performance of cumulus and granulosa cell support vector machine (SVM).
<p>Performance of cumulus and granulosa cell support vector machine (SVM).</p
ROC curve of the training set.
<p>Receiver operating characteristic (ROC) curve for the binary classifier built to distinguish between live birth (LB) and no pregnancy (NP). The curve shows the true positive rate versus false positive rate, i.e. the tradeoff between sensitivity and specificity. The area under the curve (AUC), which captures the ability of the classifier to correctly group the patients with follicular adenoma and those with follicular carcinoma, is equal to 0.86. A perfect classifier will have an AUC of 1.0, whereas an AUC value of 0.5 indicates that the classification is random.</p
Performance of the gene signature obtained during the formulation and training of the cumulus cell support vector machine on the external validation data set.
<p>Performance of the gene signature obtained during the formulation and training of the cumulus cell support vector machine on the external validation data set.</p