Kenneth I. Aston, Ph. D. , Philip J. Uren, Ph. D. , Timothy G

Slides:



Advertisements
Similar presentations
Kenneth I. Aston, Ph. D. , Philip J. Uren, Ph. D. , Timothy G
Advertisements

Susannah Baruch, J.D., David Kaufman, Ph.D., Kathy L. Hudson, Ph.D. 
Figure 7 miRNA and mRNA gene expression changes in the Poor Group
Nick Macklon, M.D., Ph.D.  Fertility and Sterility 
Ashok Agarwal, Ph. D. , H. C. L. D. , Rakesh Sharma, Ph. D
Obesity, male infertility, and the sperm epigenome
Altered microRNA expression profiles of human spermatozoa in patients with different spermatogenic impairments  Masood Abu-Halima, M.Sc., Mohamad Hammadeh,
Susannah Baruch, J.D., David Kaufman, Ph.D., Kathy L. Hudson, Ph.D. 
Report of results obtained in 2,934 women using donor sperm: donor insemination versus in vitro fertilization according to indication  Thamara Viloria,
Embryonic imprinting perturbations do not originate from superovulation-induced defects in DNA methylation acquisition  Michelle M. Denomme, B.Sc., Liyue.
MicroRNA expression in the human blastocyst
Potential role of circulating microRNAs as a biomarker for unexplained recurrent spontaneous abortion  Weibing Qin, M.D., Ph.D., Yunge Tang, M.D., Ning.
Ectopic pregnancy diagnosis and the pseudo-sac
Genome-wide epigenetic analysis delineates a biologically distinct immature acute leukemia with myeloid/T-lymphoid features by Maria E. Figueroa, Bas J.
Agnieszka Malcher, M. S. , Natalia Rozwadowska, Ph. D
Sperm DNA damage measured by the alkaline Comet assay as an independent predictor of male infertility and in vitro fertilization success  Luke Simon,
Circulating miR-200–family micro-RNAs have altered plasma levels in patients with endometriosis and vary with blood collection time  Kadri Rekker, M.Sc.,
Window of implantation transcriptomic stratification reveals different endometrial subsignatures associated with live birth and biochemical pregnancy 
Rita Reig-Viader, M. Sc. , Laia Capilla, M. Sc. , Marta Vila-Cejudo, M
Marcos Meseguer, Ph. D. , Rebeca Santiso, Ph. D. , Nicolás Garrido, Ph
Distribution of male infertility specialists in relation to the male population and assisted reproductive technology centers in the United States  Ajay.
Assisted reproductive technologies do not increase risk of abnormal methylation of PEG1/MEST in human early pregnancy loss  Hai-Yan Zheng, M.D., Xiao-Yun.
Chun Feng, M. D. , Shen Tian, Ph. D. , Yu Zhang, M. D. , Jing He, M. D
Walking the Interactome for Prioritization of Candidate Disease Genes
In vitro fertilization availability and utilization in the United States: a study of demographic, social, and economic factors  Ahmad O. Hammoud, M.D.,
Management of the first in vitro fertilization cycle for unexplained infertility: a cost- effectiveness analysis of split in vitro fertilization-intracytoplasmic.
Ying-Ying Yu, Ph. D. , Cui-Xiang Sun, Ph. D. , Yin-Kun Liu, Ph. D
Assessing loss of imprint methylation in sperm from subfertile men using novel methylation polymerase chain reaction Luminex analysis  Akiko Sato, M.E.,
Effect of vitrification on human oocytes: a metabolic profiling study
Epigenetics of the male gamete
Insurance mandates and trends in infertility treatments
Cited2 protein level in cumulus cells is a biomarker for human embryo quality and pregnancy outcome in one in vitro fertilization cycle  Yuan Fang, Ph.D.,
Proteomic analysis of individual human embryos to identify novel biomarkers of development and viability  Mandy G. Katz-Jaffe, Ph.D., David K. Gardner,
MicroRNA expression profiles in human testicular tissues of infertile men with different histopathologic patterns  Masood Abu-Halima, M.Sc., Christina.
Methylation changes in mature sperm deoxyribonucleic acid from oligozoospermic men: assessment of genetic variants and assisted reproductive technology.
Effects of n-6 and n-3 polyunsaturated acid-rich soybean phosphatidylcholine on membrane lipid profile and cryotolerance of human sperm  Alessandra A.
Volume 153, Issue 6, Pages e8 (December 2017)
Embryo incubation and selection in a time-lapse monitoring system improves pregnancy outcome compared with a standard incubator: a retrospective cohort.
Genome-wide sperm deoxyribonucleic acid methylation is altered in some men with abnormal chromatin packaging or poor in vitro fertilization embryogenesis 
Hilde Jørgensen, M. D. , Abby S. Hill, B. S. , Michael T. Beste, Ph. D
Paweł Jóźków, M. D. , Ph. D. , Marek Mędraś, M. D. , Ph. D
Defects in imprinting and genome-wide DNA methylation are not common in the in vitro fertilization population  Verity F. Oliver, Ph.D., Harriet L. Miles,
Reply of the Authors Fertility and Sterility
DNA fragmentation in brighter sperm predicts male fertility independently from age and semen parameters  Monica Muratori, Ph.D., Sara Marchiani, Ph.D.,
Mieke Carine Wim Eeckhaut, Ph.D.  Fertility and Sterility 
Optimal timing for elective egg freezing
Noninvasive metabolomic profiling of human embryo culture media using Raman spectroscopy predicts embryonic reproductive potential: a prospective blinded.
The nature of aneuploidy with increasing age of the female partner: a review of 15,169 consecutive trophectoderm biopsies evaluated with comprehensive.
Freeze-only versus fresh embryo transfer in a multicenter matched cohort study: contribution of progesterone and maternal age to success rates  Ange Wang,
Management of tubal ectopic pregnancy: methotrexate and salpingostomy are preferred to preserve fertility  Stephanie Beall, M.D., Ph.D., Alan H. DeCherney,
What are patients doing with their mosaic embryos
Age-specific probability of live birth with oocyte cryopreservation: an individual patient data meta-analysis  Aylin Pelin Cil, M.D., Heejung Bang, Ph.D.,
Ana Cobo, Ph. D. , Aila Coello, Ph. D. , Jose Remohí, M. D
Michelle Rijsdijk, M.Med.Sc., Daniel Rossouw Franken, Ph.D. 
Sources of heterogeneities in estimating the prevalence of endometriosis in infertile and previously fertile women  Sun-Wei Guo, Ph.D., Yuedong Wang,
Birth weight is associated with inner cell mass grade of blastocysts
Douglas T. Carrell, Ph.D., Benjamin R. Emery, B.S. 
Live babies born per oocyte retrieved in a subpopulation of oocyte donors with repetitive reproductive success  J. Ryan Martin, M.D., Jason G. Bromer,
Microarray analysis in sperm from fertile and infertile men without basic sperm analysis abnormalities reveals a significantly different transcriptome 
Decreased fecundity and sperm DNA methylation patterns
Circulating microRNAs as potential biomarkers for endometriosis
Cost and efficacy comparison of in vitro fertilization and tubal anastomosis for women after tubal ligation  Lauren B. Messinger, M.D., Connie E. Alford,
An investigation into the relationship between the metabolic profile of follicular fluid, oocyte developmental potential, and implantation outcome  Martina.
Genetic evaluation procedures at sperm banks in the United States
Assessing the reproductive competence of individual embryos: a proposal for the validation of new “-omics” technologies  Richard T. Scott, M.D., H.C.L.D.,
MicroRNA-22-3p is down-regulated in the plasma of Han Chinese patients with premature ovarian failure  Yujie Dang, M.D., Ph.D., Shidou Zhao, Ph.D., Yingying.
Are sperm DNA fragmentation, hyperactivation, and hyaluronan-binding ability predictive for fertilization and embryo development in in vitro fertilization.
Brooks A. Keel, Ph.D., H.C.L.D.(A.B.B.)  Fertility and Sterility 
Reda Mahfouz, M. D. , Rakesh Sharma, Ph. D. , Dipika Sharma, M. D
Tamer M. Said, M. D. , Geetha Ranga, M. D. , Ashok Agarwal, Ph. D. , H
Presentation transcript:

Aberrant sperm DNA methylation predicts male fertility status and embryo quality  Kenneth I. Aston, Ph.D., Philip J. Uren, Ph.D., Timothy G. Jenkins, Ph.D., Alan Horsager, Ph.D., Bradley R. Cairns, Ph.D., Andrew D. Smith, Ph.D., Douglas T. Carrell, Ph.D.  Fertility and Sterility  Volume 104, Issue 6, Pages 1388-1397.e5 (December 2015) DOI: 10.1016/j.fertnstert.2015.08.019 Copyright © 2015 American Society for Reproductive Medicine Terms and Conditions

Figure 1 Aberrant global methylation profiles are indicative of fertility status and poor embryo quality. (A) Hierarchic clustering of 163 neat samples based on a global methylation profile; an “out-group” of exclusively patient samples, with a subgroup strongly enriched for poor embryo samples is apparent. (B) The ROC curve (left) for prediction of patient status (IVF patient or fertile donor). Classification was performed by building a 1-level decision tree (Supplemental Methods) based on Euclidean distance between samples. Training and testing is performed using 10-fold cross-validation. The same information is shown for the task of predicting embryo quality (right). In both cases, high confidence predictions (bottom right of plots) have a high probability of being correct. (C) Classification statistics for the ROC curves are presented in (B). Samples were predicted to be positive (i.e., a patient sample or a poor-quality–embryo sample) when the probability exceeded 50%. In both cases, high specificity and positive predictive value are achieved. AUC = area under the curve; FP = false positive; NPV = negative predictive value; PPV = positive predictive value; Se. = sensitivity; Sp. = specificity. Fertility and Sterility 2015 104, 1388-1397.e5DOI: (10.1016/j.fertnstert.2015.08.019) Copyright © 2015 American Society for Reproductive Medicine Terms and Conditions

Figure 2 Gradient purification improves separation of good- and poor-quality sperm samples. (A) Hierarchic clustering of 62 gradient-purified sperm samples based on global methylation profile; 2 clear clusters are apparent, 1 of which contains almost exclusively poor-quality–embryo samples. (B) The ROC curve is shown (left), for prediction of patient status (IVF patient or fertile donor) from the 62 gradient-purified samples. Classification was performed by building a 1-level decision tree (Supplemental Methods) based on Euclidean distance between samples. Training and testing is performed using 10-fold cross-validation. The same information is shown (right) for the task of predicting embryo quality from the subset of 45 samples for which this information is known. Although classification of patient status is degraded from that with unpurified samples, the ability to predict poor-quality embryos is markedly improved. (C) Classification statistics for the ROC curves presented in (B). Samples were predicted as positive (i.e., a patient sample or a poor-quality–embryo sample) when the probability exceeded 50%. In the case of predicting poor-quality embryo, very high specificity and positive predictive value are achieved. AUC = area under the curve; FP = false positive; NPV = negative predictive value; PPV = positive predictive value; Se. = sensitivity; Sp. = specificity. Fertility and Sterility 2015 104, 1388-1397.e5DOI: (10.1016/j.fertnstert.2015.08.019) Copyright © 2015 American Society for Reproductive Medicine Terms and Conditions

Figure 3 Differential methylation between healthy donor and infertile IVF patient samples consistently occurs at a limited number of CpGs. (A) Number of differentially methylated CpGs for unpurified donor vs. IVF patient samples before and after correction for multiple hypothesis testing, using both the actual donor/patient labels and randomly shuffled donor/patient labels as a control. (B) Distribution of P values for all profiled CpGs from tests of differential methylation between donor and IVF patient samples using both actual labels and randomly shuffled donor/patient labels as a control. (C) Proportion of differentially methylated CpGs (before correction for multiple hypothesis testing) that fall within regions annotated as shown, both actual donor/IVF patient labels and randomly permuted labels. (D) Samples are split into 10 stratified, equal-size groups. A fold is formed by taking 9 of these groups and leaving 1 group out (allowing 10 separate analyses). For each fold, we use the samples in the 9 retained groups to identify differentially methylated CpGs using both actual donor/IVF patient labels and randomly permuted labels as a control. A histogram showing the number of CpGs that were contained in the top 100 most differentially methylated CpGs identified in only 1 fold, exactly 2 folds, exactly 3 folds, and so on, is displayed. The inset shows the number of CpGs contained in all 10 folds, for both actual labels and permuted labels in higher detail. (E) Classifiers, analogous to those in presented in Figure 2 were trained using a subset of the top 50, 1,000, 50,000, and 400,000 most significantly differentially methylated CpGs. The ROC curves are plotted for each. (F) A range of classifiers was trained on the top 500 most differentially methylated CpGs; shown are the ROC curves from these classifiers. Fertility and Sterility 2015 104, 1388-1397.e5DOI: (10.1016/j.fertnstert.2015.08.019) Copyright © 2015 American Society for Reproductive Medicine Terms and Conditions

Figure 4 Functional classification of differentially methylated genes. (A) Gene ontology analysis of the top 1,000 genes after sorting genes by number of differentially methylated CpGs. All terms that were significant (correct P value <.05), in comparing either IVF patients with fertile donors, or good- and poor-embryogenesis IVF, patient samples are included, with the exception of 24 terms associated with gene expression and cellular metabolism, which are omitted for clarity of visualization (full list is provided in Supplemental Table 3, available online). (B) The absolute number and proportion of genes with differentially methylated promoter regions (P<.01, Wilcoxon’s signed rank test, purified samples) that are known to be imprinted. Fertility and Sterility 2015 104, 1388-1397.e5DOI: (10.1016/j.fertnstert.2015.08.019) Copyright © 2015 American Society for Reproductive Medicine Terms and Conditions

Supplemental Figure 1 Hierarchic clustering of methylation profiles for the 44 IVF patients for which both purified and unpurified samples were processed (i.e., 88 samples are plotted). For each unpurified sample, its nearest neighbor is the purified sample from the same patient. Fertility and Sterility 2015 104, 1388-1397.e5DOI: (10.1016/j.fertnstert.2015.08.019) Copyright © 2015 American Society for Reproductive Medicine Terms and Conditions

Supplemental Figure 2 Differential methylation between purified good and poor embryogenesis samples is not defined by a small group of consistent single-CpG differences. (A) Number of differentially methylated CpGs of purified good vs. poor embryogenesis samples before and after correction for multiple hypothesis testing, using both the actual good/poor labels and randomly shuffled good/poor labels as a control. (B) Distribution of P values for all profiled CpGs from test of differential methylation between good and poor embryogenesis, using both actual labels and randomly shuffled good/poor labels as a control. (C) Proportion of differentially methylated CpGs (before correction for multiple hypothesis testing) that fall within regions annotated as shown, both actual good/poor embryo quality labels and randomly permuted labels. (D) Samples are split into 10 stratified, equal-size groups. A fold is formed by taking 9 of these groups and leaving 1 group out (allowing 10 ways of doing this). For each fold, we use the samples in the 9 retained groups to identify differentially methylated CpGs, using both actual good/poor embryogenesis labels and randomly permuted labels as a control. A histogram showing the number of CpGs that were contained in the top 100 most differentially methylated CpGs identified in only 1 fold, exactly 2 folds, exactly 3 folds, and so on, is shown. (E) Classifiers, analogous to those in presented in Figure 2, were trained using a subset of the top x most differentially methylated CpGs (where x is varied along the x-axis of the plots), and the sensitivity, specificity, positive predictive value, and negative predictive value of each was evaluated as a function of how many CpGs were selected. Fertility and Sterility 2015 104, 1388-1397.e5DOI: (10.1016/j.fertnstert.2015.08.019) Copyright © 2015 American Society for Reproductive Medicine Terms and Conditions