Messenger RNA and protein expression of tumor necrosis factor α and its receptors in human follicular granulosa cells

Expression of interleukin-1β and its receptor in human granulosa cells and their association with steroidogenesis

This study aimed to investigate whether interleukin 1β (IL-1β) and soluble IL-1 receptor 2 (sIL-1R2) are expressed in human granulosa cells (GCs) and relate to ovarian steroidogenesis. Ninety-six women undergoing in vitro fertilization (IVF) were recruited. RT-PCR and immunocytochemistry were used to detect mRNAs and proteins of IL-1β and IL-1R2, respectively. The steroidogenesis of primary cultured GCs was evaluated following treatment with either IL-1β alone or IL-1β and FSH in combination. There were positive correlations between serum IL-1β and serum progesterone (r = 0.220, p = 0.032) and follicular fluid (FF) estradiol (r = 0.242, p = 0.018). Additionally, serum and FF sIL-1R2 were negatively and positively correlated with FF estradiol (r = -0.376, p = 0.005) and FF progesterone (r = 0.434, p = 0.001), respectively. The mRNA and protein expression of IL-1β and IL-1R2 became evident in GCs. IL-1β alone significantly increased estradiol secretion from GCs, but in the presence of FSH, it could notably promote progesterone secretion in addition to estradiol. In conclusion, IL-1β and sIL-1R2 are expressed in human GCs and substantially contribute to ovarian steroidogenesis, suggesting that the IL-1β system may be a potential target for optimizing ovarian hyperstimulation and steroidogenesis in IVF cycles.

Multi-Omics Analysis and Machine Learning Prediction Model for Pregnancy Outcomes After Intracytoplasmic Sperm Injection–in vitro Fertilization

Background

To explore the methylation profiles in cumulus cells (CCs) of women undergoing intracytoplasmic sperm injection–in vitro fertilization (ICSI–IVF) and establish a prediction model of pregnancy outcomes using machine learning approaches.

Methods

Methylation data were retrieved from the Gene Expression Omnibus (GEO) database, and differentially methylated genes (DMGs) were subjected to gene set analyses. Support vector machine (SVM), random forest (RF), and logistic regression (LR) were used to establish the prediction model, and microarray data from GEO was analyzed to identify differentially expressed genes (DEGs) associated with the dichotomous outcomes of clinical pregnancy (pregnant vs. non-pregnant). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis provided multi-dimensional validation for selected DMGs.

Results

A total of 338 differentially methylated CpG sites associated with 146 unique genes across the genome were identified. Among the identified pathways, the prominent ones were involved in the regulation of cell growth and oocyte development (hsa04340, hsa04012, hsa04914, hsa04614, hsa04913, hsa04020, and hsa00510). The area under the curve (AUC) of machine learning classifiers was 0.94 (SVM) vs. 0.88 (RF) vs. 0.97 (LR). 196 DEGs were found in transcriptional microarray. Mapped genes were selected through overlapping enriched pathways in transcriptional profiles and methylated data of CCs, predictive of successful pregnancy.

Conclusions

Methylated profiles of CCs were significantly different between women receiving ICSI-IVF procedures that conceived successfully and those that did not conceive. Machine learning approaches are powerful tools that may provide crucial information for prognostic assessment. Pathway analysis may be another way in multiomics analysis of cumulus cells.