Early transcriptomic changes in peripheral blood 7 days after embryo transfer in dairy cattle

A common goal of the dairy industry is to shorten the calving interval to reap several benefits associated with improved fertility. Early pregnancy detection is crucial to shorten this interval, allowing for prompt re-insemination of cows that failed to conceive after the first service. Currently, the industry lacks a method to accurately predict pregnancy within the first 3 wk. The polypeptide cytokine IFN-tau (IFNT) is the primary signal for maternal recognition of pregnancy in ruminants. As IFNT is released from the early conceptus, it initiates a cascade of effects, including upregulation of IFN-stimulated genes (ISG). Expression of ISG can be detected in the peripheral blood. The present study aimed to characterize peripheral transcriptomic changes, including the ISG, as early as d 7 after embryo transfer. A total of 170 Holstein heifers received in vitro-produced embryos. Whole blood was collected from these heifers within 24 h of the embryo transfer (d 0), d 7, and d 14 after embryo transfer. The heifers were divided into 2 groups, pregnant and nonpregnant, based on pregnancy diagnosis on d 28 via ultrasound. Total RNA was extracted from the peripheral blood of pregnant and nonpregnant heifers, pooled and sequenced. Expression analysis on d 7 heifers resulted in 13 significantly differentially expressed genes mostly related to innate immunity. Differential expression analysis comparing pregnant heifers on d 0 to the same heifers on d 14 showed 51 significantly differentially expressed genes. Eight genes were further quantified through reverse-transcription quantitative real-time PCR for biological validation. On d 7 after embryo transfer, mRNA transcriptions of EDN1, CXCL3, CCL4, and IL1A were significantly upregulated in pregnant heifers (n = 14) compared with nonpregnant heifers (n = 14), with respective fold changes of 8.10, 18.12, 29.60, and 29.97. Although on d 14 after embryo transfer, mRNA transcriptions of ISG15, MX2, OASY1, and IFI6 were significantly upregulated in the blood of pregnant heifers (n = 14) compared with the same heifers on d 0, with respective fold changes of 5.09, 2.59, 3.89, and 3.08. These findings demonstrate that several immune-related genes and ISG are activated during the first 2 wk after embryo transfer, which may explain how the maternal immune system accommodates the allogenic conceptus. To further investigate the diagnostic potentials of these genes, future studies are warranted to analyze the specificity and sensitivity of these biomarkers to predict early pregnancy.

Improved pregnancy prediction performance in an updated deep-learning embryo selection model: a retrospective independent validation study

RESEARCH QUESTION

What is the effect of increasing training data on the performance of ongoing pregnancy prediction after single vitrified-warmed blastocyst transfer (SVBT) in a deep-learning model?

DESIGN

A total of 3960 SVBT cycles were retrospectively analysed. Embryos were stratified according to the Society for Assisted Reproductive Technology age groups. Embryos were scored by deep-learning models iDAScore v1.0 (IDA-V1) and iDAScore v2.0 (IDA-V2) (15% more training data than v1.0) and by Gardner grading. The discriminative performance of the pregnancy prediction for each embryo scoring model was compared using the area under the curve (AUC) of the receiver operating characteristic curve for each maternal age group.

RESULTS

The AUC of iDA-V2, iDA-V1 and Gardener grading in all cohort were 0.736, 0.720 and 0.702, respectively. iDA-V2 was significantly higher than iDA-V1 and Gardener grading (P < 0.0001). Group > 35 years (n = 757): the AUC of iDA-V2 was significantly higher than Gardener grading (0.718 versus 0.694, P = 0.015); group aged 35-37 years (n = 821), the AUC of iDA-V2 was significantly higher than iDA-V1 (0.712 versus 0.696, P = 0.035); group aged 41-42 years (n = 715, the AUC of iDA-V2 was significantly higher than Gardener grading (0.745 versus 0.696, P = 0.007); group > 42 years (n = 660) and group aged 38-40 years (n = 1007), no significant differences were found between the groups.

CONCLUSION

The performance of deep learning models for pregnancy prediction will be improved by increasing the size of the training data.

Predicting Pregnancy Outcome in Dairy Cows: The Role of IGF-1 and Progesterone

The purpose of this study was to determine the link between insulin-like growth factor 1 (IGF-1), progesterone (PROG), non-esterified fatty acids (NEFAs), β-hydroxybutyrate (BHB), and glucose (GLU) and pregnancy probability after the first artificial insemination (AI) and during the first 100 days in milk (DIM), during the critical transition period. We determined levels of serum IGF-1, PROG, NEFA, BHB, and GLU in Holstein dairy cows via ELISA, using blood samples collected 7 days before parturition (DAP) until 21 days postparturition (DPP). The group was split into cows diagnosed pregnant at 100 DIM (PREG) and those that did not conceive at 100 and 150 DIM (NPREG). Serum IGF-1 and PROG median levels at 7 DAP were significantly higher in PREG vs. NPREG (p = 0.029), the only statistically significant differences across the subgroups. At 7 DAP, IGF-1 levels within the initial group showed a strong negative correlation with PROG (r = -0.693; p = 0.006), while for the PREG subgroup, the IGF-1 levels exhibited a very strong positive correlation with GLU (r = 0.860; p = 0.011) and NEFA (r = 0.872; p = 0.013). IGF-1 and PROG levels detected at 7 DAP may be useful to predict pregnancy at 100 DIM. The positive correlation of NEFA and GLU levels during the transition period demonstrates that the initial group is not in NEB; thus, the NEFA level was not a decisive factor for reproduction success.

Predicting the Outcome of Equine Artificial Inseminations Using Chilled Semen

This study aimed to determine whether an analysis of stallion ejaculate could accurately predict the likelihood of pregnancy resulting from artificial insemination in mares. This study involved 46 inseminations of 41 mares, using 7 standardbred stallions over a 5-week period at an Australian pacing stud. Semen quality was assessed immediately after collection and again after chilling at ~5 °C for 24 h. The assessment involved evaluating ejaculate volume, sperm concentration, and motility parameters using an iSperm^® Equine portable device. After the initial evaluation, a subpopulation of cells was subjected to a migration assay through a 5 µm polycarbonate filter within a Samson™ isolation chamber over a 15 min period. The cells were assessed for their concentration, motility parameters, and ability to reduce the membrane impermeant tetrazolium salt WST-1. The data, combined with the stallion and mare's ages, were used to predict the likelihood of pregnancy, as confirmed by rectal ultrasound sonography performed 14 days post ovulation. The criteria used to predict pregnancy were optimized for each individual stallion, resulting in an overall accuracy of 87.9% if analyzed pre-chilling and 95% if analyzed post-chilling. This study suggests that an analysis of stallion ejaculate can be used to predict the likelihood of pregnancy resulting from artificial insemination in mares with a high level of accuracy.

Pregnancy status predicted using milk mid-infrared spectra from dairy cattle

Accurate and timely pregnancy diagnosis is an important component of effective herd management in dairy cattle. Predicting pregnancy from Fourier-transform mid-infrared (FT-MIR) spectroscopy data is of particular interest because the data are often already available from routine milk testing. The purpose of this study was to evaluate how well pregnancy status could be predicted in a large data set of 1,161,436 FT-MIR milk spectra records from 863,982 mixed-breed pasture-based New Zealand dairy cattle managed within seasonal calving systems. Three strategies were assessed for defining the nonpregnant cows when partitioning the records according to pregnancy status in the training population. Two of these used records for cows with a subsequent calving only, whereas the third also included records for cows without a subsequent calving. For each partitioning strategy, partial least squares discriminant analysis models were developed, whereby spectra from all the cows in 80% of herds were used to train the models, and predictions on cows in the remaining herds were used for validation. A separate data set was also used as a secondary validation, whereby pregnancy diagnosis had been assigned according to the presence of pregnancy-associated glycoproteins (PAG) in the milk samples. We examined different ways of accounting for stage of lactation in the prediction models, either by including it as an effect in the prediction model, or by pre-adjusting spectra before fitting the model. For a subset of strategies, we also assessed prediction accuracies from deep learning approaches, utilizing either the raw spectra or images of spectra. Across all strategies, prediction accuracies were highest for models using the unadjusted spectra as model predictors. Strategies for cows with a subsequent calving performed well in herd-independent validation with sensitivities above 0.79, specificities above 0.91 and area under the receiver operating characteristic curve (AUC) values over 0.91. However, for these strategies, the specificity to predict nonpregnant cows in the external PAG data set was poor (0.002-0.04). The best performing models were those that included records for cows without a subsequent calving, and used unadjusted spectra and days in milk as predictors, with consistent results observed across the training, herd-independent validation and PAG data sets. For the partial least squares discriminant analysis model, sensitivity was 0.71, specificity was 0.54 and AUC values were 0.68 in the PAG data set; and for an image-based deep learning model, the sensitivity was 0.74, specificity was 0.52 and the AUC value was 0.69. Our results demonstrate that in pasture-based seasonal calving herds, confounding between pregnancy status and spectral changes associated with stage of lactation can inflate prediction accuracies. When the effect of this confounding was reduced, prediction accuracies were not sufficiently high enough to use as a sole indicator of pregnancy status.

GVBD rate is an independent predictor for pregnancy in ICSI patients with surplus immature oocytes

INTRODUCTION

It was reported that there were still up to 30% immature retrieved oocyte at germinal vesicle (GV) or metaphase I (MI) stage. Whether the spontaneous maturity competency of immature oocytes associated to the clinical outcome of in vitro fertilization (IVF) cycles remains unclear and unexplored. This study aimed to investigate how the oocyte developmental parameters in in vitro maturation (IVM) affect clinical outcomes of intracytoplasmic sperm injection (ICSI) cycles.

METHODS

This retrospective cohort study included couples undergoing ICSI in a university-affiliated hospital. Surplus immature oocytes during ICSI were collected and cultured in vitro. The numbers of germinal vesicle (GV) oocytes undergoing GV breakdown (GVBD) and polar body 1 extrusion within 24 h culture were recorded. The main outcome measurements were demographic baselines and oocyte developmental parameters in IVM associated with pregnancy outcomes.

RESULTS

A total of 191 couples were included with an overall GVBD rate of 63.7% (327/513) and oocyte maturation rate of 46.8% (240/513). 53.4% (102/191) of them had embryos transferred freshly, which originated from metaphase II oocytes that matured spontaneously in vivo, and 60.8% (62/102) got pregnant. Among factors with a P-value < 0.2 in univariate logistic regression analyses of pregnancy correlation, GVBD rate (OR 3.220, 95% CI 1.060-9.782, P=0.039) and progesterone level on human chorionic gonadotropin (HCG) day (OR 0.231, 95% CI 0.056-0.949, P=0.042) remained significant in the multivariate model. The area under the curve (AUC) of the predictive nomogram was 0.729 (95% CI 0.632-0.826) with an acceptable calibration. Moreover, decision curve analyses illustrated the superior overall net benefit of models that included the GVBD rate in clinical decisions within a wide range of threshold probabilities.

CONCLUSION

In conclusion, GVBD rate and progesterone level on HCG day may be associated with pregnancy outcomes in infertile couples during the regular ICSI procedure. An elevated GVBD rate within 24 h may greatly increase the likelihood of pregnancy in infertile couples during ICSI. This preliminary study may optimize clinical pregnancy prediction, which provides support in decision-making in clinical practice.

Pregnancy prediction performance of an annotation-free embryo scoring system on the basis of deep learning after single vitrified-warmed blastocyst transfer: a single-center large cohort retrospective study

OBJECTIVE

To analyze the performance of an annotation-free embryo scoring system on the basis of deep learning for pregnancy prediction after single vitrified blastocyst transfer (SVBT) compared with the performance of other blastocyst grading systems dependent on annotation or morphology scores.

DESIGN

A single-center large cohort retrospective study from an independent validation test.

SETTING

Infertility clinic.

PATIENT(S)

Patients who underwent SVBT cycles (3,018 cycles, mean ± SD patient age 39.3 ± 4.0 years).

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

The pregnancy prediction performances of each embryo scoring model were compared using the area under curve (AUC) for predicting the fetal heartbeat status for each maternal age group.

RESULT(S)

The AUCs of the <35 years age group (n = 389) for pregnancy prediction were 0.72 for iDAScore, 0.66 for KIDScore, and 0.64 for the Gardner criteria. The AUC of iDAScore was significantly greater than those of the other two models. For the 35-37 years age group (n = 514), the AUCs were 0.68, 0.68, and 0.65 for iDAScore, KIDScore, and the Gardner criteria, respectively, and were not significantly different. The AUCs of the 38-40 years age group (n = 796) were 0.67 for iDAScore, 0.65 for KIDScore, and 0.64 for the Gardner criteria, and there were no significant differences. The AUCs of the 41-42 years age group (n = 636) were 0.66, 0.66, and 0.63 for iDAScore, KIDScore, and the Gardner criteria, respectively, and there were no significant differences among the pregnancy prediction models. For the >42 years age group (n = 389), the AUCs were 0.76 for iDAScore, 0.75 for KIDScore, and 0.75 for the Gardner criteria, and there were no significant differences. Thus, iDAScore AUC was either the highest or equal to the highest AUC for all age groups, although a significant difference was observed only in the youngest age group.

CONCLUSION(S)

Our results showed that objective embryo assessment by a completely automatic and annotation-free model, iDAScore, performed as well as or even better than more traditional embryo assessment or annotation-dependent ranking tools. iDAScore could be an optimal pregnancy prediction model after SVBT, especially in young patients.

Specific gene expression differences in cumulus cells as potential biomarkers of pregnancy

The development of an objective and accurate test that could help select embryos with the highest chance of achieving pregnancy in IVF procedures is an important goal of reproductive medicine. For this purpose, cumulus cell gene expression is being studied to find biomarkers of pregnancy. Several recent studies have proposed potential biomarkers of pregnancy expressed in cumulus cells; however, these have mostly not been validated on an independent set of samples. The aim of this study was to analyse the expression of EFNB2, RGS2 and VCAN genes proposed as biomarkers of pregnancy in cumulus cells by quantitative polymerase chain reaction. Gene expression was evaluated in 43 individual cumulus cell samples, derived from a highly homogenous group of 43 women. The same protocol for ovarian stimulation was used for all women, and elective single embryo transfer was performed. Expression levels of RGS2 and VCAN did not differ between cumulus cells of implanted and non-implanted embryos. EFNB2 showed borderline higher expression in cumulus cells of non-implanted embryos, which is contradictory to previous studies. Altogether, the results of previous studies in which EFNB2, RGS2 and VCAN were proposed as biomarkers of pregnancy could not be replicated in our set of cumulus cell samples.