Toll-like receptor 4 (TLR4) may play a critical role in regulating follicular development. Data are scarce on the role of TLR4 in the follicle. This study investigated the effects of TLR4 on steroidogenesis in human granulosa cells. Immunohistochemical analysis revealed stage-specific expression of TLR4 in the mouse ovarian cycle, and immunofluorescence showed TLR4 expression in the human granulosa-like tumor cell line (KGN). TLR4 agonist lipopolysaccharides (LPS) significantly inhibited follicular development and synthesis of estradiol (E2) in mice. In KGN cells, TLR4 activation significantly inhibited CYP19A1, FSHR and StAR, and TLR4 inhibition reversed these effects. TLR4 activation also inhibited forskolin-induced secretion of E2 by inhibiting CYP19A1, with no effect on progesterone. Further studies showed activation of p38, JNK and NF-κB signaling after TLR4 activation. Subsequent analyses showed that an NF-κB antagonist reversed the inhibitory effects on CYP19A1 expression and E2 secretion. Together, our results suggest that TLR4 activation may suppress CYP19A1 expression and E2 secretion via NF-κB signaling in human granulosa cells, with important implications for the regulation of ovarian pathophysiology.

Background. The role of uric acid on metabolic syndrome (MetS) has always been controversial. This study aims to explore associations between uric acid with MetS and its components in Chinese female health check‐up population. Methods. 1381 subjects constituted the longitudinal health check‐up cohort. Health examination and genotyping were performed. Unadjusted and adjusted observational analyses were implemented to evaluate observational associations between uric acid with MetS and its components. Mendelian randomization analysis was performed to estimate the causal effect using variation at rs11722228 (SLC2A9) as an instrument for uric acid. Results. An increase of 65% in risk of MetS per standard deviation increase in uric acid was found using unadjusted observational analyses. This association attenuated on adjustment for potential confounders. Similar patterns were found in the association analyses of uric acid with hyperglycemia, hypertension, and dyslipidemia. Neither by performing unadjusted nor adjusted analysis did we see evidence for association of uric acid on overweight and obesity. Mendelian randomization analyses showed no evidence of causal association between uric acid and MetS and MetS components. Conclusions. We found no causal evidence to support that increased serum uric acid is a causal risk factor for MetS or its components. Hence, there remains no strong evidence for the effeteness of undergoing urate‐lowering therapy to prevent the onset of MetS or cardiovascular disease in health management.

Mendelian randomization (MR) is a common analytic tool for exploring the causal relationship among complex traits. Existing MR methods require selecting a small set of single nucleotide polymorphisms (SNPs) to serve as instrument variables. However, selecting a small set of SNPs may not be ideal, as most complex traits have a polygenic or omnigenic architecture and are each influenced by thousands of SNPs. Here, motivated by the recent omnigenic hypothesis, we present an MR method that uses all genome-wide SNPs for causal inference. Our method uses summary statistics from genome-wide association studies as input, accommodates the commonly encountered horizontal pleiotropy effects and relies on a composite likelihood framework for scalable computation. We refer to our method as the omnigenic Mendelian randomization, or OMR. We examine the power and robustness of OMR through extensive simulations including those under various modeling misspecifications. We apply OMR to several real data applications, where we identify multiple complex traits that potentially causally influence coronary artery disease (CAD) and asthma. The identified new associations reveal important roles of blood lipids, blood pressure and immunity underlying CAD as well as important roles of immunity and obesity underlying asthma.

Considering the lack of efficient breast cancer prediction models suitable for general population screening in China. We aimed to develop a risk prediction model to identify high-risk populations, to help with primary prevention of breast cancer among Han Chinese women. A cause-specific competing risk model was used to develop the Han Chinese Breast Cancer Prediction model. Data from the Shandong Case-Control Study (328 cases and 656 controls) and Taixing Prospective Cohort Study (13,176 participants) were used to develop and validate the model. The expected/observed (E/O) ratio and C-statistic were calculated to evaluate calibration and discriminative accuracy of the model, respectively. Compared with the reference level, the relative risks (RRs) for highest level of number of abortions, age at first live birth, history of benign breast disease, body mass index (BMI), family history of breast cancer, and life satisfaction scores were 6.3, 3.6, 4.3, 1.9, 3.3, 2.4, respectively. The model showed good calibration and discriminatory accuracy with an E/O ratio of 1.03 and C-statistic of 0.64. We developed a risk prediction model including fertility status and relevant disease history, as well as other modifiable risk factors. The model demonstrated good calibration and discrimination ability.

STUDY QUESTION: What are the dynamic expression features of plasma microRNAs (miRNAs) during the peri-implantation period in women with successful pregnancy via single frozen-thawed blastocyst transfer? SUMMARY ANSWER: There is a significant change in the plasma miRNA expression profile before and after blastocyst transfer, during the window of implantation. WHAT IS KNOWN ALREADY: The expression of miRNAs in peripheral blood has indicative functions during the peri-implantation period. Nevertheless, the dynamic expression profile of circulating miRNAs during the peri-implantation stage in women with a successful pregnancy has not been studied. STUDY DESIGN, SIZE, DURATION: Seventy-six women treated for infertility with a single frozen-thawed blastocyst transfer in a natural cycle were included in this study. Among them, 57 women had implantation success and a live birth, while 19 patients experienced implantation failure. Peripheral blood samples were collected at five different time points throughout the peri-implantation period, including D0 (ovulation day), D3, D5, D7, and D9 in this cycle of embryo transfer. The plasma miRNAs in women with blastocyst transfer were isolated, sequenced, and analyzed. PARTICIPANTS/MATERIALS, SETTING, METHODS: Peripheral blood samples were collected in EDTA tubes and stored at −80°C until further use. miRNAs were isolated from blood, cDNA libraries were constructed, and the resulting sequences were mapped to the human genome. The plasma miRNAs were initially analyzed in a screening cohort (n = 34) with successful pregnancy. Trajectory analysis, including a global test and pairwise comparisons, was performed to detect dynamic differentially expressed (DE) miRNAs. Fuzzy c-means clustering was conducted for all dynamic DE miRNAs. The correlation between DE miRNAs and clinical characteristics of patients was investigated using a linear mixed model. Target genes of the miRNAs were predicted, and functional annotation analysis was performed. The expression of DE miRNAs was also identified in a validation set consisting of women with successful (n = 23) and unsuccessful (n = 19) pregnancies. MAIN RESULTS AND THE ROLE OF CHANCE: Following small RNA sequencing, a total of 2656 miRNAs were determined as valid read values. After trajectory analysis, 26 DE miRNAs (false discovery rate < 0.05) were identified by the global test, while pairwise comparisons in addition identified 20 DE miRNAs. A total of seven distinct clusters representing different temporal patterns of miRNA expression were discovered. Nineteen DE miRNAs were further identified to be associated with at least one clinical trait. Endometrium thickness and progesterone level showed a correlation with multiple DE miRNAs (including two of the same miRNAs, hsa-miR-1-3p and hsa-miR-6741-3p). Moreover, the 19 DE miRNAs were predicted to have 403 gene targets, and there were 51 (12.7%) predicted genes likely involved in both decidualization and embryo implantation. Functional annotation for predicted targets of those clinically related DE miRNAs suggested the involvement of vascular endothelial growth factor and Wnt signaling pathways, as well as responses to hormones, immune responses, and cell adhesion-related signaling pathways during the peri-implantation stage. LARGE SCALE DATA: The raw miRNA sequence data reported in this article have been deposited in the Genome Sequence Archive (GSA-Human: HRA005227) and are publicly accessible at https://ngdc.cncb.ac.cn/gsa-human/browse/HRA005227. LIMITATIONS, REASONS FOR CAUTION: Although the RNA sequencing results revealed the global dynamic changes of miRNA expression, further experiments examining the clinical significance of the identified DE miRNAs in embryo implantation outcome and the relevant regulatory mechanisms involved are warranted. WIDER IMPLICATIONS OF THE FINDINGS: Understanding the dynamic landscape of the miRNA transcriptome could shed light on the physiological mechanisms involved from ovulation to the post-implantation stage, as well as identifying biomarkers that characterize stage-related biological process. STUDY FUNDING/COMPETING INTEREST(S): The study was funded by the Major clinical research project of Tangdu Hospital (2021LCYJ004) and the Discipline Platform Improvement Plan of Tangdu Hospital (2020XKPT003). The funders had no influence on the study design, data collection, and analysis, decision to publish, or preparation of the article. There are no conflicts of interest to declare.

Recurrent implantation failure (RIF) refers to patients who have undergone at least three implantation failures with several high-quality embryos. Identifying patients with RIF in advance is crucial for effective clinical managements. However, uterine biopsy, the primary research approach, is not only invasive but also poses a risk to the integrity of the endometrial microenvironment. Here, we provided a novel, safe, and immediate methodology for RIF populations identification through plasma microRNA (miRNA) profiles. Peripheral blood samples of 163 women were collected and sequenced to quantify the relative expression levels of plasma miRNAs. Through a comparative analysis of the temporal expression between patients with RIF and those with successful embryo implantation during the peri-implantation period, we determined that the RIF-associated miRNA signature was established at the early stage of the window of implantation. By global screening, we identified 217 miRNAs that were differentially expressed between groups on the ovulation day (D0), using a threshold of |fold change| ≥ 1.5. Through refining selection criteria, 10 eligible miRNAs were ultimately selected and further validated through real-time quantitative polymerase chain reaction. These miRNAs were employed to construct a logistic regression-based RIF model. The model demonstrated robust performance in the validation and test cohorts, with area under the curve of 0.947 and 0.912, respectively. We also demonstrated that miRNAs maintain relative stability within a specific phase, making them suitable as clinical biomarkers. This study provides a noninvasive methodology for identifying women with RIF and suggests a potential role in predicting RIF risk before embryo transfer.