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Hyperandrogenic women with PCOS show disrupted decidualization (DE) and placentation. Dihydrotestosterone (DHT) is reported to enhance DE in non-PCOS endometrial stromal cells (eSCCtrl); however, this has not been assessed in PCOS cells (eSCPCOS). Therefore, we studied the transcriptome profile of non-decidualized (non-DE) and DE eSCs from women with PCOS and Ctrl in response to short-term estradiol (E2) and/or progesterone (P4) exposure with/without (±) DHT. The non-DE eSCs were subjected to E2 ± DHT treatment, whereas the DE (0.5 mM 8-Br-cAMP, 96 h) eSCs were post-treated with E2 and P4 ± DHT, and RNA-sequenced. Validation was performed by immunofluorescence and immunohistochemistry. The results showed that, regardless of treatment, the PCOS and Ctrl samples clustered separately. The comparison of DE vs. non-DE eSCPCOS without DHT revealed PCOS-specific differentially expressed genes (DEGs) involved in mitochondrial function and progesterone signaling. When further adding DHT, we detected altered responses for lysophosphatidic acid (LPA), inflammation, and androgen signaling. Overall, the results highlight an underlying defect in decidualized eSCPCOS, present with or without DHT exposure, and possibly linked to the altered pregnancy outcomes. We also report novel factors which elucidate the mechanisms of endometrial dysfunction in PCOS.

Background: Ovarian insufficiency is a major concern for long-term cancer survivors. Although semen freezing is well established to preserve male fertility, the possibilities to secure post-cancer female fertility are mostly limited to oocyte or embryo freezing. These methods require time-consuming ovarian stimulation with or without in vitro fertilization (IVF) that evidently delays cancer therapy. Ovarian tissue cryopreservation and subsequent thawed tissue autotransplantation are considered the most promising alternative strategy for restoring the fertility of oncology patients, which has not yet received the full clinical acceptance. Therefore, all successful cases are needed to prove its reliability and safety.Case presentation: Here we report a single case in Estonia, where a 28-year-old woman with malignant breast neoplasm had ovarian cortex cryopreserved before commencing gonadotoxic chemo- and radiotherapy. Two years after cancer therapy, the patient underwent heterotopic ovarian tissue transplantation into the lateral pelvic wall. The folliculogenesis was stimulated in the transplanted tissue by exogenous follicle-stimulating hormone and oocytes were collected under ultrasound guidance for IVF and embryo transfer. The healthy boy was born after full-term gestation in 2014, first in Eastern Europe.Conclusion: Despite many countries have reported the first implementation of the ovarian tissue freezing and transplantation protocols, the data is still limited on the effectiveness of heterotopic ovarian transplant techniques. Thus, all case reports of heterotopic ovarian tissue transplantation and long-term follow-ups to describe the children's health are valuable source of clinical experience.

RIC8A functions as a chaperone and guanine nucleotide exchange factor for a subset of G protein α subunits. Multiple G protein subunits mediate various signalling events that regulate cell adhesion and migration and the involvement of RIC8A in some of these processes has been demonstrated. We have previously shown that the deficiency of RIC8A causes a failure in mouse gastrulation and neurogenesis - major events in embryogenesis that rely on proper association of cells with the extracellular matrix (ECM) and involve active cell migration. To elaborate on these findings, we used Ric8a-/- mouse embryonic stem cells and Ric8a-deficient mouse embryonic fibroblasts, and found that RIC8A plays an important role in the organisation and remodelling of actin cytoskeleton and cell-ECM association. Ric8a-deficient cells were able to attach to different ECM components, but were unable to spread correctly, and did not form stress fibres or focal adhesion complexes. We also found that the presence of RIC8A is necessary for the activation of β1 integrins and integrin-mediated cell migration.

Targeting of G proteins to the cell cortex and their activation is one of the triggers of both asymmetric and symmetric cell division. Resistance to inhibitors of cholinesterase 8 (RIC8), a guanine nucleotide exchange factor, activates a certain subgroup of G protein α-subunits in a receptor independent manner. RIC8 controls the asymmetric cell division in Caenorhabditis elegans and Drosophila melanogaster, and symmetric cell division in cultured mammalian cells, where it regulates the mitotic spindle orientation. Although intensely studied in mitosis, the function of RIC8 in mammalian meiosis has remained unknown. Here we demonstrate that the expression and subcellular localization of RIC8 changes profoundly during mouse oogenesis. Immunofluorescence studies revealed that RIC8 expression is dependent on oocyte growth and cell cycle phase. During oocyte growth, RIC8 is abundantly present in cytoplasm of oocytes at primordial, primary and secondary preantral follicle stages. Later, upon oocyte maturation RIC8 also populates the germinal vesicle, its localization becomes cell cycle dependent, and it associates with chromatin and the meiotic spindle. After fertilization, RIC8 protein converges to the pronuclei and is also detectable at high levels in the nucleolus precursor bodies of both maternal and paternal pronucleus. During first cleavage of zygote RIC8 localizes in the mitotic spindle and cell cortex of forming blastomeres. In addition, we demonstrate that RIC8 co-localizes with its interaction partners Gαi1/2:GDP and LGN in meiotic/mitotic spindle, cell cortex and polar bodies of maturing oocytes and zygotes. Downregulation of Ric8 by siRNA leads to interferred translocation of Gαi1/2 to cortical region of maturing oocytes and reduction of its levels. RIC8 is also expressed at high level in female reproductive organs e.g. oviduct. Therefore we suggest a regulatory function for RIC8 in mammalian gametogenesis and fertility.

Simple Summary Cancer metastasis is considered to be one of the most significant causes of cancer morbidity, accounting for up to 90% of cancer deaths. The accurate identification of a cancer’s origin and the types of cancer cells it comprises is crucial in enabling clinicians to decide better treatment options for patients. DNA methylation changes are increasingly recognized as determining cancer prediction, especially for the transition to metastasis. Research in the last decade has shown the incredible promise of the use of artificial intelligence (AI) in cancer classification. In this study, we applied several machine learning techniques, a branch of AI, to identify cancer tissue or origin and further classified cancer samples as primary and metastatic cancers based on publicly available DNA methylation data. Overall, our analysis resulted in a 99% accuracy for predicting cancer subtypes based on the tissue of origin. Abstract Metastatic cancers account for up to 90% of cancer-related deaths. The clear differentiation of metastatic cancers from primary cancers is crucial for cancer type identification and developing targeted treatment for each cancer type. DNA methylation patterns are suggested to be an intriguing target for cancer prediction and are also considered to be an important mediator for the transition to metastatic cancer. In the present study, we used 24 cancer types and 9303 methylome samples downloaded from publicly available data repositories, including The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). We constructed machine learning classifiers to discriminate metastatic, primary, and non-cancerous methylome samples. We applied support vector machines (SVM), Naive Bayes (NB), extreme gradient boosting (XGBoost), and random forest (RF) machine learning models to classify the cancer types based on their tissue of origin. RF outperformed the other classifiers, with an average accuracy of 99%. Moreover, we applied local interpretable model-agnostic explanations (LIME) to explain important methylation biomarkers to classify cancer types.