No Specific Gene Expression Signature in Human Granulosa and Cumulus Cells for Prediction of Oocyte Fertilisation and Embryo Implantation

In human IVF procedures objective and reliable biomarkers of oocyte and embryo quality are needed in order to increase the use of single embryo transfer (SET) and thus prevent multiple pregnancies. During folliculogenesis there is an intense bi-directional communication between oocyte and follicular cells. For this reason gene expression profile of follicular cells could be an important indicator and biomarker of oocyte and embryo quality. The objective of this study was to identify gene expression signature(s) in human granulosa (GC) and cumulus (CC) cells predictive of successful embryo implantation and oocyte fertilization. Forty-one patients were included in the study and individual GC and CC samples were collected; oocytes were cultivated separately, allowing a correlation with IVF outcome and elective SET was performed. Gene expression analysis was performed using microarrays, followed by a quantitative real-time PCR validation. After statistical analysis of microarray data, there were no significantly differentially expressed genes (FDR<0,05) between non-fertilized and fertilized oocytes and non-implanted and implanted embryos in either of the cell type. Furthermore, the results of quantitative real-time PCR were in consent with microarray data as there were no significant differences in gene expression of genes selected for validation. In conclusion, we did not find biomarkers for prediction of oocyte fertilization and embryo implantation in IVF procedures in the present study.     

A comparison of the beta-geometric model with landmarking for dynamic prediction of time to pregnancy

We conducted a simulation study to compare two methods that have been recently used in clinical literature for the dynamic prediction of time to pregnancy. The first is landmarking, a semi-parametric method where predictions are updated as time progresses using the patient subset still at risk at that time point. The second is the beta-geometric model that updates predictions over time from a parametric model estimated on all data and is specific to applications with a discrete time to event outcome. The beta-geometric model introduces unobserved heterogeneity by modelling the chance of an event per discrete time unit according to a beta distribution. Due to selection of patients with lower chances as time progresses, the predicted probability of an event decreases over time. Both methods were recently used to develop models predicting the chance to conceive naturally. The advantages, disadvantages and accuracy of these two methods are unknown. We simulated time-to-pregnancy data according to different scenarios. We then compared the two methods by the following out-of-sample metrics: bias and root mean squared error in the average prediction, root mean squared error in individual predictions, Brier score and c statistic. We consider different scenarios including data-generating mechanisms for which the models are misspecified. We applied the two methods on a clinical dataset comprising 4999 couples. Finally, we discuss the pros and cons of the two methods based on our results and present recommendations for use of either of the methods in different settings and (effective) sample sizes.     

In vivo analysis of progesterone receptor action in the uterus during embryo implantation

In order for a successful pregnancy to occur, the embryo must attach to the luminal epithelial cells and invade into the stroma. Then, the surrounding stromal cells need to undergo decidualization in order to establish the vasculature necessary for survival of the embryo. These events in early pregnancy are tightly regulated by the steroid hormones, estrogen (E2) and progesterone (P4), through their cognate receptors, the estrogen receptor (ER) and the progesterone receptor (PR), respectively. Using a mouse model in which the PR has been ablated, it was demonstrated that the PR is necessary for embryo implantation and decidualization. Therefore, understanding the mechanism of PR action in the adult uterus is necessary in order to understand the events of early pregnancy. Insights from both mouse models and human samples have been integral in elucidating uterine PR action. These studies have shown that not only PR target genes, but also mediators of PR action are important for correct PR action in early pregnancy. Many of the genes involved in PR action in early pregnancy have also been shown to have roles in uterine diseases such as endometriosis and endometrial cancer. Therefore, the integration of mouse and human studies on PR action in the uterus will be important for the future understanding of uterine diseases and in the development of treatment for these diseases.     

In vivo analysis of progesterone receptor action in the uterus during embryo implantation

In order for a successful pregnancy to occur, the embryo must attach to the luminal epithelial cells and invade into the stroma. Then, the surrounding stromal cells need to undergo decidualization in order to establish the vasculature necessary for survival of the embryo. These events in early pregnancy are tightly regulated by the steroid hormones, estrogen (E2) and progesterone (P4), through their cognate receptors, the estrogen receptor (ER) and the progesterone receptor (PR), respectively. Using a mouse model in which the PR has been ablated, it was demonstrated that the PR is necessary for embryo implantation and decidualization. Therefore, understanding the mechanism of PR action in the adult uterus is necessary in order to understand the events of early pregnancy. Insights from both mouse models and human samples have been integral in elucidating uterine PR action. These studies have shown that not only PR target genes, but also mediators of PR action are important for correct PR action in early pregnancy. Many of the genes involved in PR action in early pregnancy have also been shown to have roles in uterine diseases such as endometriosis and endometrial cancer. Therefore, the integration of mouse and human studies on PR action in the uterus will be important for the future understanding of uterine diseases and in the development of treatment for these diseases.     

Normal calves from transfer of biopsied, sexed and vitrified IVP bovine embryos

Data on biopsied, sexed and cryopreserved in vitro produced (IVP) bovine embryos, and their in vivo developmental competence are very limited. Two preliminary studies were conducted before the primary study. In Experiment 1, post-thaw in vitro developmental competence of biopsied and vitrified IVP embryos was evaluated using re-expansion as an endpoint. In Experiment 2, the pregnancy rates of biopsied fresh, frozen or vitrified embryos following single embryo transfer were compared. Since vitrified embryos resulted in a higher pregnancy rate than frozen-thawed embryos, in the primary study (Experiment 3), all IVP embryos were vitrified following biopsy and sexing (by DNA fingerprinting). In Experiment 3, we compared pregnancy initiation and calving results of heifers in the following treatments: 1) artificial insemination (AI); 2) AI plus contralateral transfer of a single embryo (AI + SET); 3) ipsilateral transfer of single embryo (SET); or 4) bilateral transfer of two embryos (DET). Birth weights, gestation lengths and dystocia scores were recorded. In Experiment 1, post-thaw re-expansion rate of biopsied and vitrified embryos was 85% (70/82). In Experiment 2, pregnancy rates (90 d) were 44% (7/16), 23% (3/13), and 50% (7/14) for vitrified, frozen and fresh embryos, respectively (P < 0.10). In Experiment 3, pregnancy rates of AI and SET were 65% (20/31) and 40% (16/40), respectively (P < 0.05). The pregnancy rate of AI + SET was 75% (27/36) with 11 carrying twins, and the pregnancy rate of DET was 72% (26/36) with 10 carrying twins. All AI fetuses were carried to term, but only half the SET fetuses were carried to term. Similar calving rates were observed in the AI + SET and DET groups, 76 and 70%, respectively, of those pregnant at Day 40. Mean birth weight, dystocia score and gestation length of AI calves were not different from those of SET calves. Mean birth weight and dystocia score of single-born calves were greater than those of twin born calves (P < 0.05). These data demonstrate that biopsied IVP bovine embryos can be successfully cryopreserved by vitrification and following post-thaw embryo transfer, acceptable rates of offspring with normal birth weights can be obtained without major calving difficulties.     

Genomic assessment of human cumulus cell marker genes as predictors of oocyte developmental competence: impact of various experimental factors

Background

Single embryo transfer (SET) is the most successful way to reduce the frequency of multiple pregnancies following in vitro fertilisation. However, selecting the embryo for SET with the highest chances of pregnancy remains a difficult challenge since morphological and kinetics criteria provide poor prediction of both developmental and implantation ability. Partly through the expression of specific genes, the oocyte-cumulus interaction helps the oocyte to acquire its developmental competence. Our aim was therefore to identify at the level of cumulus cells (CCs) genes related to oocyte developmental competence.

Methodology/Principal Findings

197 individual CCs were collected from 106 patients undergoing an intra-cytoplasmic sperm injection procedure. Gene expression of CCs was studied using microarray according to the nuclear maturity of the oocyte (immature vs. mature oocyte) and to the developmental competence of the oocyte (ability to reach the blastocyst stage after fertilisation). Microarray study was followed by a meta-analysis of the behaviour of these genes in other datasets available in Gene Expression Omnibus which showed the consistency of this list of genes. Finally, 8 genes were selected according to oocyte developmental competence from the 308 differentially expressed genes (p<0.0001) for further validation by quantitative PCR (qPCR). Three of these 8 selected genes were validated as potential biomarkers (PLIN2, RGS2 and ANG). Experimental factors such as inter-patient and qPCR series variability were then assessed using the Generalised Linear Mixed Model procedure, and only the expression level of RGS2 was confirmed to be related to oocyte developmental competence. The link between biomarkers and pregnancy was finally evaluated and level of RGS2 expression was also correlated with clinical pregnancy.

Conclusion/Significance

RGS2, known as a regulator of G protein signalling, was the only gene among our 8 selected candidates biomarkers of oocyte competence to cover many factors of variability, including inter-patient factors and experimental conditions.     

CASPAR: a hierarchical bayesian approach to predict survival times in cancer from gene expression data

MOTIVATION: DNA microarrays allow the simultaneous measurement of thousands of gene expression levels in any given patient sample. Gene expression data have been shown to correlate with survival in several cancers, however, analysis of the data is difficult, since typically at most a few hundred patients are available, resulting in severely underdetermined regression or classification models. Several approaches exist to classify patients in different risk classes, however, relatively little has been done with respect to the prediction of actual survival times. We introduce CASPAR, a novel method to predict true survival times for the individual patient based on microarray measurements. CASPAR is based on a multivariate Cox regression model that is embedded in a Bayesian framework. A hierarchical prior distribution on the regression parameters is specifically designed to deal with high dimensionality (large number of genes) and low sample size settings, that are typical for microarray measurements. This enables CASPAR to automatically select small, most informative subsets of genes for prediction. RESULTS: Validity of the method is demonstrated on two publicly available datasets on diffuse large B-cell lymphoma (DLBCL) and on adenocarcinoma of the lung. The method successfully identifies long and short survivors, with high sensitivity and specificity. We compare our method with two alternative methods from the literature, demonstrating superior results of our approach. In addition, we show that CASPAR can further refine predictions made using clinical scoring systems such as the International Prognostic Index (IPI) for DLBCL and clinical staging for lung cancer, thus providing an additional tool for the clinician. An analysis of the genes identified confirms previously published results, and furthermore, new candidate genes correlated with survival are identified.     

Fold prediction by a hierarchy of sequence, threading, and modeling methods.

Several fold recognition algorithms are compared to each other in terms of prediction accuracy and significance. It is shown that on standard benchmarks, hybrid methods, which combine scoring based on sequence-sequence and sequence-structure matching, surpass both sequence and threading methods in the number of accurate predictions. However, the sequence similarity contributes most to the prediction accuracy. This strongly argues that most examples of apparently nonhomologous proteins with similar folds are actually related by evolution. While disappointing from the perspective of the fundamental understanding of protein folding, this adds a new significance to fold recognition methods as a possible first step in function prediction. Despite hybrid methods being more accurate at fold prediction than either the sequence or threading methods, each of the methods is correct in some cases where others have failed. This partly reflects a different perspective on sequence/structure relationship embedded in various methods. To combine predictions from different methods, estimates of significance of predictions are made for all methods. With the help of such estimates, it is possible to develop a "jury" method, which has accuracy higher than any of the single methods. Finally, building full three-dimensional models for all top predictions helps to eliminate possible false positives where alignments, which are optimal in the one-dimensional sequences, lead to unsolvable sterical conflicts for the full three-dimensional models.     

Docking and scoring protein complexes: CAPRI 3rd Edition

The performance of methods for predicting protein-protein interactions at the atomic scale is assessed by evaluating blind predictions performed during 2005-2007 as part of Rounds 6-12 of the community-wide experiment on Critical Assessment of PRedicted Interactions (CAPRI). These Rounds also included a new scoring experiment, where a larger set of models contributed by the predictors was made available to groups developing scoring functions. These groups scored the uploaded set and submitted their own best models for assessment. The structures of nine protein complexes including one homodimer were used as targets. These targets represent biologically relevant interactions involved in gene expression, signal transduction, RNA, or protein processing and membrane maintenance. For all the targets except one, predictions started from the experimentally determined structures of the free (unbound) components or from models derived by homology, making it mandatory for docking methods to model the conformational changes that often accompany association. In total, 63 groups and eight automatic servers, a substantial increase from previous years, submitted docking predictions, of which 1994 were evaluated here. Fifteen groups submitted 305 models for five targets in the scoring experiment. Assessment of the predictions reveals that 31 different groups produced models of acceptable and medium accuracy-but only one high accuracy submission-for all the targets, except the homodimer. In the latter, none of the docking procedures reproduced the large conformational adjustment required for correct assembly, underscoring yet again that handling protein flexibility remains a major challenge. In the scoring experiment, a large fraction of the groups attained the set goal of singling out the correct association modes from incorrect solutions in the limited ensembles of contributed models. But in general they seemed unable to identify the best models, indicating that current scoring methods are probably not sensitive enough. With the increased focus on protein assemblies, in particular by structural genomics efforts, the growing community of CAPRI predictors is engaged more actively than ever in the development of better scoring functions and means of modeling conformational flexibility, which hold promise for much progress in the future.     

Data pattern analysis for the individualised pretherapeutic identification of high-risk diffuse large B-cell lymphoma (DLBCL) patients by cytomics.

BACKGROUND: Clinical outcome predictions in phase III studies are mostly derived for patient groups, but not for individual patients, although individualised predictions are an ultimate goal to permit a personalised fine tuning of therapy. This may permit earlier application of target therapies, minimise general damage to the organism, and result in improved complete remission rates in malignant diseases. METHODS: In this study, Lymphochip cDNA microarray gene expression results of DLBCL patients, from a published prospective meta-analysis study on the prediction of group prognosis, were analysed for individualised predictions using a nonstatistical data pattern classification approach. The training set was comprised of the same 160 DLBCL patients as in the prognosis study, with the validation set of 80 patients remaining unknown to the learning process. This permits the assessment of prospective classifier performance towards unknown patients. RESULTS: Pretherapeutic predictions for the training and validation set patients were correct in 98.1% and 78.3% of the cases for nonsurvival and in 67.3% and 45.3% for survival. The discriminatory data pattern consisted of 14 known and 10 unknown gene products. CONCLUSIONS: The better than 95% correct pretherapeutic prediction for about one-half of the ultimately nonsurviving high-risk patients of the training set is promising for clinical considerations about individualised therapy in such cases. Reliable individualised survival predictions are not possible with the information content of the present dataset. It seems necessary to investigate additional gene products, since survival may significantly depend on non-lymphocyte-associated genes that escape to the lymphocyte-oriented Lymphochip gene activation analysis.     

Analysis of genetic effects of nuclear–cytoplasmic interaction on quantitative traits: Genetic models for seed traits of plants

Two Genetic models (an embryo model and an endosperm model) were proposed for analyzing genetic effects of nuclear genes, cytoplasmic genes, maternal genes, and nuclear–cytoplasmic interaction (NCI) as well as their genotype by environment interaction for quantitative traits of plant seed. In these models, the NCI effects were partitioned into direct additive and dominance NCI components. Mixed linear model approaches were employed for statistical analysis. For both balanced and unbalanced diallel cross designs, Monte Carlo simulations were conducted to evaluate unbiasedness and precision of estimated variance components of these models. The results showed that the proposed methods work well. Random genetic effects were predicted with an adjusted unbiased prediction method. Seed traits (protein content and oil content) of Upland cotton (Gossypium hirsutum L.) were analyzed as worked examples to demonstrate the use of the models.     

Germline fitness-based scoring of cancer mutations

A key goal in cancer research is to find the genomic alterations that underlie malignant cells. Genomics has proved successful in identifying somatic variants at a large scale. However, it has become evident that a typical cancer exhibits a heterogenous mutation pattern across samples. Cases where the same alteration is observed repeatedly seem to be the exception rather than the norm. Thus, pinpointing the key alterations (driver mutations) from a background of variations with no direct causal link to cancer (passenger mutations) is difficult. Here we analyze somatic missense mutations from cancer samples and their healthy tissue counterparts (germline mutations) from the viewpoint of germline fitness. We calibrate a scoring system from protein domain alignments to score mutations and their target loci. We show first that this score predicts to a good degree the rate of polymorphism of the observed germline variation. The scoring is then applied to somatic mutations. We show that candidate cancer genes prone to copy number loss harbor mutations with germline fitness effects that are significantly more deleterious than expected by chance. This suggests that missense mutations play a driving role in tumor suppressor genes. Furthermore, these mutations fall preferably onto loci in sequence neighborhoods that are high scoring in terms of germline fitness. In contrast, for somatic mutations in candidate onco genes we do not observe a statistically significant effect. These results help to inform how to exploit germline fitness predictions in discovering new genes and mutations responsible for cancer.     

A similarity-based method for genome-wide prediction of disease-relevant human genes

MOTIVATION: A method for prediction of disease relevant human genes from the phenotypic appearance of a query disease is presented. Diseases of known genetic origin are clustered according to their phenotypic similarity. Each cluster entry consists of a disease and its underlying disease gene. Potential disease genes from the human genome are scored by their functional similarity to known disease genes in these clusters, which are phenotypically similar to the query disease. RESULTS: For assessment of the approach, a leave-one-out cross-validation of 878 diseases from the OMIM database, using 10672 candidate genes from the human genome, is performed. Depending on the applied parameters, in roughly one-third of cases the true solution is contained within the top scoring 3% of predictions and in two-third of cases the true solution is contained within the top scoring 15% of predictions. The prediction results can either be used to identify target genes, when searching for a mutation in monogenic diseases or for selection of loci in genotyping experiments in genetically complex diseases.     

Context effect in a temporal bisection task with the choice keys available during the sample

In a symbolic matching to sample task, six pigeons learned to associate different sample durations with different comparison stimuli. On “Short” trials, choice of Red and Green keylight comparisons were reinforced following 3-s and 9-s samples, respectively; on “Long” trials, Blue and Yellow keylight comparisons were reinforced following 9-s and 27-s samples, respectively. In contrast with previous studies, the comparison keys were available during the samples. After the temporal discriminations were learned, new pairs of comparison keys were presented and the preference for each was assessed during 27-s samples. One pair in particular, Green and Blue, was critical because it tested the predictions of two timing models, Scalar Expectancy Theory (SET) and the Learning-to-Time (LeT) model. The results showed that preference for Green increased during the sample, a result consistent with LeT but not with SET. Other test results, however, were predicted by neither model.     

Accumulation of the SET protein in HEK293T cells and mild oxidative stress: cell survival or death signaling

SET protein (I2PP2A) is an inhibitor of PP2A, which regulates the phosphorylated Akt (protein kinase B) levels. We assessed the effects of SET overexpression in HEK293T cells, both in the presence and the absence of mild oxidative stress induced by 50 μM tert-butyl hydroperoxide. Immunoblotting assays demonstrated that SET accumulated in HEK293T cells and increased the levels of phosphorylated Akt and PTEN; in addition, SET decreased glutathione antioxidant defense of cell and increased expression of genes encoding antioxidant defense proteins. Immunofluorescence analysis demonstrated that accumulated SET was equally distributed in cytoplasm and nucleus; however, in cells that had been exposed to oxidative stress, SET was found in large aggregates in the cytoplasm. SET accumulation in HEK293T cells correlated with inhibition of basal apoptosis as evidenced by a decrease in annexin V staining and activity of caspases; under mild oxidative stress, SET accumulation correlated with caspase-independent cell death, as evidenced by increased PI and annexin V/PI double staining. The results suggest that accumulated SET could act via Akt/PTEN either as cell survival signal or as oxidative stress sensor for cell death.     

Prediction of various grades of cervical neoplasia on plastic-embedded cytobrush samples. Discriminant analysis with qualitative and quantitative predictors.

The purpose of this study was to investigate whether discrimination into five groups of various grades of cervical preneoplasia and neoplasia is possible using discriminant analysis models. Data were analyzed for 242 cases diagnosed as either slight dysplasia (n = 50), moderate dysplasia (n = 50), severe dysplasia (n = 50), carcinoma in situ (n = 50) or invasive carcinoma (n = 42) and consisted of qualitative and quantitative features of cells derived from a repeat sample taken from the ectocervix as well as the endocervix using Cytobrushes. The samples were embedded in plastic, and thin sections were prepared, resulting in a monolayer of cut nuclei. The percentages of expected correct prediction were obtained by using 10,000 double cross-validation samples; the mean percentage of correct prediction into five groups using cross-validation was 65% (in the original analysis, 72%) and into two groups (dysplasia versus carcinoma in situ and invasive carcinoma) was 91% (93%). The results reflect group discrimination potential; we do not claim reliability of prediction for an individual patient. The patients were not a representative sample of the population; to investigate whether groups of patients could be discriminated on the basis of both qualitative and quantitative features, the data analyzed contain an almost equal number of observations in each of the five groups. The results indicate that features do not classify the cases in the same way; the discriminant analyses suggest that quantitative features play an important role in the discrimination of dysplasia from carcinoma cases, while the majority of the qualitative features are important in discrimination within the three dysplasia groups.     

Assessment of Fusion Gene Status in Sarcomas Using a Custom Made Fusion Gene Microarray

Sarcomas are relatively rare malignancies and include a large number of histological subgroups. Based on morphology alone, the differential diagnoses of sarcoma subtypes can be challenging, but the identification of specific fusion genes aids correct diagnostication. The presence of individual fusion products are routinely investigated in Pathology labs. However, the methods used are time-consuming and based on prior knowledge about the expected fusion gene and often the most likely break-point. In this study, 16 sarcoma samples, representing seven different sarcoma subtypes with known fusion gene status from a diagnostic setting, were investigated using a fusion gene microarray. The microarray was designed to detect all possible exon-exon breakpoints between all known fusion genes in a single analysis. An automated scoring of the microarray data from the 38 known sarcoma-related fusion genes identified the correct fusion gene among the top-three hits in 11 of the samples. The analytical sensitivity may be further optimised, but we conclude that a sarcoma-fusion gene microarray is suitable as a time-saving screening tool to identify the majority of the correct fusion genes.     

Clinical implications of developments in in vitro fertilisation.

During February 1979 to December 1983, 831 infertile couples were treated by in vitro fertilisation and embryo transfer. The problems they faced included deciding on the number of oocytes to be collected at laparoscopy, the numbers to be donated or fertilised, the numbers of embryos to be transferred and frozen, and whether abnormal embryos should be used for research or discarded. The 831 patients received a total of 1530 treatment cycles. Of the 763 patients for whom complete data were available, 136 (17.8%) became pregnant. The rate of pregnancy, however, increased dramatically from 7.4% when only one embryo was transferred to 21.1% and 28.1% when two and three embryos were transferred, respectively. The chance of multiple pregnancy also increased with the number of embryos transferred, but the risk (2% for twins) was far outweighed by the relatively poor result after transferring a single embryo. Out of 40 embryos freeze-thawed, 23 survived thawing and were transferred; of these, 4 (17%) resulted in pregnancy. Thirty four transfers of donor oocyte embryos also resulted in four pregnancies (12%), but two of these ended in abortion. Neither microscopy nor any other available test can determine the potential of an oocyte to result in pregnancy, so that discarding oocytes that may look abnormal simply reduces the chances of conception--both for the patient and for any prospective recipient of donor oocyte embryos. In any case, abnormal embryos tend to die when growth is allowed to continue in vitro. Probably all oocytes harvested from a patient should be inseminated and the utilisation of the embryos decided once the number developed is known.