Characterization of the Endometrial Microbiome in Patients with Recurrent Implantation Failure.

An abnormal endometrial microbiota has been associated with implantation failure; therefore, it may be important to evaluate it in order to improve reproductive outcomes in infertile patients. The main objective of our study was to compare the endometrial microbiome of patients with recurrent implantation failure (RIF) and control patients undergoing assisted reproduction treatment (ART). A prospective cohort study including forty-five patients with their own or donated gametes. The endometrial microbiome was analysed by massive sequencing of the bacterial 16S rRNA gene. Different bacterial communities were detected in RIF and control patients. Lactobacillus stands out as the most frequent genus, with 92.27% in RIF patients and 97.96% in control patients, and significant differences were reported between the two groups (p = 0.002). No significant differences were found regarding alpha diversity index. In beta diversity analysis, a significant trend was observed in the separation of the bacterial community between established groups (p < 0.07). Relative abundance analysis identified genera Prevotella (p < 0.001), Streptococcus (p < 0.001), Bifidobacterium (p = 0.002), Lactobacillus (p = 0.002) and Dialister (p = 0.003). Our results demonstrated the existence of an endometrial microbiota characteristic of RIF patients and showed that there might be a relationship between population of the endometrial microbiome and embryo implantation failure, providing us the possibility to improve clinical results in this patients.

Application of machine learning to predict aneuploidy and mosaicism in embryos from in vitro fertilization cycles.

BACKGROUND: The factors associated with embryo aneuploidy have been extensively studied. Mostly maternal age and to a lesser extent male factor and ovarian stimulation have been related to the occurrence of chromosomal alterations in the embryo. On the other hand, the main factors that may increase the incidence of embryo mosaicism have not yet been established. OBJECTIVE: This study aimed to establish a machine learning model that would allow prediction of aneuploidies and mosaicism in embryos conceived via in vitro fertilization, and thus help to determine which variables are associated with these chromosomal alterations. STUDY DESIGN: The study design was observational and retrospective. A total of 6989 embryos from 2476 cycles of preimplantation genetic testing for aneuploidies were included (January 2013 to December 2020). The trophoectoderm biopsies on day-5, -6, or -7 blastocysts were analyzed by preimplantation genetic testing for aneuploidies (PGT-A). The different maternal, paternal, couple, embryo, and in vitro fertilization cycle characteristics were recorded in a database (22 predictor variables) from which predictive models of embryo aneuploidy and mosaicism were developed; 16 different unsupervised classification machine learning algorithms were used to establish the predictive models. RESULTS: Two different predictive models were performed: one for aneuploidy and the other for mosaicism. The predictor variable was of multiclass type because it included the segmental- and whole-chromosome alteration categories. The best predicting models for both aneuploidies and mosaicism were those obtained from the Random Forest algorithm. The area under ROC curve (AUC) value was 0.792 for the aneuploidy explanatory model and 0.776 for mosaicism. The most important variable in the final aneuploidy model was maternal age, followed by paternal and maternal karyotype and embryo quality. In the predictive model of mosaicism, the most important variable was the technique used in preimplantation genetic testing for aneuploidies and embryo quality, followed by maternal age and day of biopsy. CONCLUSION: It is possible to predict embryo aneuploidy and mosaicism from certain characteristics of the patients and their embryos.

FSH receptor genotype and its influence on the results of donor ovarian stimulation using corifollitropin alfa.

RESEARCH QUESTION: Does the FSH receptor (FSHR) genotype influence the results of donor ovarian stimulation using corifollitropin alfa? DESIGN: A prospective cohort study was performed including 152 oocyte donor ovarian stimulations: group 1 (n = 80) using a single dose of 150 µg of corifollitropin alpha; and group 2 (n = 72) using in addition to corifollitropin alpha, continued stimulation using recombinant FSH 225 IU daily. Allelic discrimination was used to genotype the FSHR p.N680S polymorphism. Linear regression analysis was performed to study the differences between groups. RESULTS: No differences in clinical characteristics between genotypes were reported. Overall, the results of ovarian stimulation were better in oocyte donors with SN and NN genotypes compared with SS in terms of the number of retrieved oocytes (15.78 versus 10.83; P = 0.008) and retrieved metaphase II (MII) oocytes (12.34 versus 9.00; P = 0.032). Corresponding differences were also observed in group 1 for the number of retrieved oocytes (13.83 versus 7.50, P = 0.018) and retrieved MII oocytes (10.24 versus 5.42; P = 0.038). However, in group 2 no significant differences were found for oocytes retrieved (17.55 versus 13.06, P = 0.064) or MII oocytes (14.25 versus 11.39; P = 0.12). CONCLUSIONS: This study suggests that ovarian stimulation protocols with corifollitropin alfa in women with the SS genotypes could be associated with fewer oocytes and MII oocytes retrieved. Despite the fact that corifollitropin alfa has a longer half-life, the results for the SS genotype do not match those for the other genotypes, so other factors must be involved. Therefore, to tailor treatments, it would be advisable to genotype women at p.N680S of the FSHR.

Methylenetetrahydrofolate reductase gene polymorphisms are not associated with embryo chromosomal abnormalities and IVF outcomes.

The aim of our study was to investigate the effect of maternal and embryo MTHFR C677T and A1298C polymorphisms on embryo aneuploidies and mosaicism and the correlation between these genetic variants in transferred euploid embryos and IVF outcomes. MTHFR genotype was analyzed in 77 women who performed an IVF/ICSI cycle with PGT-A. Moreover, to evaluate the effect of embryo MTHFR polymorphisms on embryo aneuploidies and mosaicism, the MTHFR genotype was analyzed in 191 biopsied embryos from the PGT-A cycles of these patients. Additionally, 218 DNA samples from trophectoderm biopsies belonging to a different group of patients were also genotyped. MTHFR polymorphisms were analyzed in a total amount of 409 trophectoderm samples. The main parameters analyzed were embryo aneuploidy and mosaicism rates. Finally, the IVF outcomes of 241 single euploid embryo transfers were assessed and compared between different MTHFR embryo genotypes. The aneuploidy rates were similar in embryos from homozygous normal women and women with at least one mutated allele (54.7% vs. 30.2% in 677C>T and 37.8% vs. 42.7% in 1298A>C). Furthermore, no differences were observed in the mosaicism rate (24.0% vs. 13.8% in 677C>T and 17.1% vs. 17.3% in 1298A>C). A similar analysis was performed, taking into account the embryo genotype results. No differences in aneuploidy rate were observed between the study groups. The only significant difference was the mosaicism rate among 677C>T genotype (13.5% in 677CC group vs. 5.4% in 677CT/TT; p = 0.019). Implantation rate, biochemical and clinical miscarriage rates, and ongoing pregnancy rate were compared between different embryo genotypes, and no statistically significant differences were found. In conclusion, the maternal MTHFR genotype did not influence embryo chromosomal abnormalities. Moreover, the embryo MTHFR genotype was not associated with embryo aneuploidy or IVF outcomes such as implantation, pregnancy loss, and ongoing pregnancy when euploid embryos were transferred.Abbreviations: MTHFR: methylenetetrahydrofolate reductase; IVF: in vitro fertilization; PGT-A: preimplantation genetic testing for aneuploidies; SAM: S-adenosyl methionine; SNP: single nucleotide polymorphism; SPSS: Statistical Package for Social Sciences; RIF: recurrent implantation failure; RPL: recurrent pregnancy loss; hCG: human chorionic gonadotropin; PBS: phosphate buffered saline; CGH: comparative genomic hybridization; NGS: next generation sequencing.

Effect of ovarian stimulation on embryo aneuploidy and mosaicism rate.

There is a high incidence of chromosome abnormalities in human embryos that leads to a failed IVF cycle. Different studies have shown that maternal age is the determining factor in the appearance of chromosomal alterations in the embryo. However, the possible influence of ovarian stimulation on oocyte and embryo aneuploidies and mosaicism is controversial. A retrospective study was carried out in which 835 embryos from 280 couples undergoing reproductive treatment using their oocytes were chromosomally analyzed. A binary logistic regression analysis was performed to evaluate the relationship between different parameters characterizing controlled ovarian stimulation (COS) and the rate of aneuploidy and embryonic mosaicism. The embryo aneuploidy rate showed no association with the use of oral contraceptives, type, total and daily doses of gonadotropins, stimulation protocol type, and drugs used for ovulation trigger (p > 0.05). In contrast, the duration of the ovarian stimulation treatment was correlated with the aneuploidy rate: patients requiring more days of stimulation presented a lower rate of aneuploid embryos (p = 0.015). None of the variables studied showed any association with the rate of embryo mosaicism. However, the duration of COS showed association with the appearance of aneuploidy, suggesting that faster recruitment could be deleterious for those reassuming meiosis, yielding more abnormal karyotype.Abbreviations: IVF: in vitro fertilization; COS: controlled ovarian stimulation; PGT-A: preimplantation genetic test for aneuploidy; hCG: human chorionic gonadotropin; GnRH: gonadotropin-releasing hormone; LH: luteinizing hormone; FSH: follicle-stimulating hormone; NGS: next-generation sequencing; a-CGH: comparative genomic hybridization; TUNEL: Terminal transferase dUTP Nick End Labeling; FISH: fluorescent in situ hybridization.