Associations between Sperm Epigenetic Age and Semen Parameters: An Evaluation of Clinical and Non-Clinical Cohorts.

The well-documented relationship between chronological age and the sperm methylome has allowed for the construction of epigenetic clocks that estimate the biological age of sperm based on DNA methylation, which we previously termed sperm epigenetic age (SEA). Our lab demonstrated that SEA is positively associated with the time taken to achieve pregnancy; however, its relationship with semen parameters is unknown. A total of 379 men from the Longitudinal Investigation of Fertility and Environment (LIFE) study, a non-clinical cohort, and 192 men seeking fertility treatment from the Sperm Environmental Epigenetics and Development Study (SEEDS) were included in the study. Semen analyses were conducted for both cohorts, and SEA was previously generated using a machine learning algorithm and DNA methylation array data. Association analyses were conducted via multivariable linear regression models adjusting for BMI and smoking status. We found that SEA was not associated with standard semen characteristics in SEEDS and LIFE cohorts. However, SEA was significantly associated with higher sperm head length and perimeter, the presence of pyriform and tapered sperm, and lower sperm elongation factor in the LIFE study (p < 0.05). Based on our results, SEA is mostly associated with defects in sperm head morphological factors that are less commonly evaluated during male infertility assessments. SEA shows promise to be an independent biomarker of sperm quality to assess male fecundity.

DNA methylation profiles reveal sex-specific associations between gestational exposure to ambient air pollution and placenta cell-type composition in the PRISM cohort study.

BACKGROUND: Gestational exposure to ambient air pollution has been associated with adverse health outcomes for mothers and newborns. The placenta is a central regulator of the in utero environment that orchestrates development and postnatal life via fetal programming. Ambient air pollution contaminants can reach the placenta and have been shown to alter bulk placental tissue DNA methylation patterns. Yet the effect of air pollution on placental cell-type composition has not been examined. We aimed to investigate whether the exposure to ambient air pollution during gestation is associated with placental cell types inferred from DNA methylation profiles. METHODS: We leveraged data from 226 mother-infant pairs in the Programming of Intergenerational Stress Mechanisms (PRISM) longitudinal cohort in the Northeastern US. Daily concentrations of fine particulate matter (PM2.5) at 1 km spatial resolution were estimated from a spatiotemporal model developed with satellite data and linked to womens' addresses during pregnancy and infants' date of birth. The proportions of six cell types [syncytiotrophoblasts, trophoblasts, stromal, endothelial, Hofbauer and nucleated red blood cells (nRBCs)] were derived from placental tissue 450K DNA methylation array. We applied compositional regression to examine overall changes in placenta cell-type composition related to PM2.5 average by pregnancy trimester. We also investigated the association between PM2.5 and individual cell types using beta regression. All analyses were performed in the overall sample and stratified by infant sex adjusted for covariates. RESULTS: In male infants, first trimester (T1) PM2.5 was associated with changes in placental cell composition (p = 0.03), driven by a decrease [per one PM2.5 interquartile range (IQR)] of 0.037 in the syncytiotrophoblasts proportion (95% confidence interval (CI) [- 0.066, - 0.012]), accompanied by an increase in trophoblasts of 0.033 (95% CI: [0.009, 0.064]). In females, second and third trimester PM2.5 were associated with overall changes in placental cell-type composition (T2: p = 0.040; T3: p = 0.049), with a decrease in the nRBC proportion. Individual cell-type analysis with beta regression showed similar results with an additional association found for third trimester PM2.5 and stromal cells in females (decrease of 0.054, p = 0.024). CONCLUSION: Gestational exposure to air pollution was associated with placenta cell composition. Further research is needed to corroborate these findings and evaluate their role in PM2.5-related impact in the placenta and consequent fetal programming.

Assessing Differential Variability of High-Throughput DNA Methylation Data.

PURPOSE OF REVIEW: DNA methylation (DNAm) is essential to human development and plays an important role as a biomarker due to its susceptibility to environmental exposures. This article reviews the current state of statistical methods developed for differential variability analysis focusing on DNAm data. RECENT FINDINGS: With the advent of high-throughput technologies allowing for highly reliable and cost-effective measurements of DNAm, many epigenome studies have analyzed DNAm levels to uncover biological mechanisms underlying past environmental exposures and subsequent health outcomes. These studies typically focused on detecting sites or regions which differ in their mean DNAm levels among exposure groups. However, more recent studies highlighted the importance of identifying differentially variable sites or regions as biologically relevant features. Currently, the analysis of differentially variable DNAm sites has not yet gained widespread adoption in environmental studies; yet, it is important to examine the effects of environmental exposures on inter-individual epigenetic variability. In this article, we describe six of the most widely used statistical approaches for analyzing differential variability of DNAm levels and provide a discussion of their advantages and current limitations.

Sperm epigenetic clock associates with pregnancy outcomes in the general population.

STUDY QUESTION: Is sperm epigenetic aging (SEA) associated with probability of pregnancy among couples in the general population? SUMMARY ANSWER: We observed a 17% lower cumulative probability at 12 months for couples with male partners in the older compared to the younger SEA categories. WHAT IS KNOWN ALREADY: The strong relation between chronological age and DNA methylation profiles has enabled the estimation of biological age as epigenetic 'clock' metrics in most somatic tissue. Such clocks in male germ cells are less developed and lack clinical relevance in terms of their utility to predict reproductive outcomes. STUDY DESIGN, SIZE, DURATION: This was a population-based prospective cohort study of couples discontinuing contraception to become pregnant recruited from 16 US counties from 2005 to 2009 and followed for up to 12 months. PARTICIPANTS/MATERIALS, SETTING, METHODS: Sperm DNA methylation from 379 semen samples was assessed via a beadchip array. A state-of-the-art ensemble machine learning algorithm was employed to predict age from the sperm DNA methylation data. SEA was estimated from clocks derived from individual CpGs (SEACpG) and differentially methylated regions (SEADMR). Probability of pregnancy within 1 year was compared by SEA, and discrete-time proportional hazards models were used to evaluate the relations with time-to-pregnancy (TTP) with adjustment for covariates. MAIN RESULTS AND THE ROLE OF CHANCE: Our SEACpG clock had the highest predictive performance with correlation between chronological and predicted age (r = 0.91). In adjusted discrete Cox models, SEACpG was negatively associated with TTP (fecundability odds ratios (FORs)=0.83; 95% CI: 0.76, 0.90; P = 1.2×10-5), indicating a longer TTP with advanced SEACpG. For subsequent birth outcomes, advanced SEACpG was associated with shorter gestational age (n = 192; -2.13 days; 95% CI: -3.67, -0.59; P = 0.007). Current smokers also displayed advanced SEACpG (P < 0.05). Finally, SEACpG showed a strong performance in an independent IVF cohort (n = 173; r = 0.83). SEADMR performance was comparable to SEACpG but with attenuated effect sizes. LIMITATIONS, REASONS FOR CAUTION: This prospective cohort study consisted primarily of Caucasian men and women, and thus analysis of large diverse cohorts is necessary to confirm the associations between SEA and couple pregnancy success in other races/ethnicities. WIDER IMPLICATIONS OF THE FINDINGS: These data suggest that our sperm epigenetic clocks may have utility as a novel biomarker to predict TTP among couples in the general population and underscore the importance of the male partner for reproductive success. STUDY FUNDING/COMPETING INTEREST(S): This work was funded in part by grants the National Institute of Environmental Health Sciences, National Institutes of Health (R01 ES028298; PI: J.R.P. and P30 ES020957); Robert J. Sokol, MD Endowed Chair of Molecular Obstetrics and Gynecology (J.R.P.); and the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland (Contracts N01-HD-3-3355, N01-HD-3-3356 and N01-HD-3-3358). S.L.M. was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health. The authors declare no competing interests. TRIAL REGISTRATION NUMBER: N/A.

Sperm DNA methylation mediates the association of male age on reproductive outcomes among couples undergoing infertility treatment.

Parental age at time of offspring conception is increasing in developed countries. Advanced male age is associated with decreased reproductive success and increased risk of adverse neurodevelopmental outcomes in offspring. Mechanisms for these male age effects remain unclear, but changes in sperm DNA methylation over time is one potential explanation. We assessed genome-wide methylation of sperm DNA from 47 semen samples collected from male participants of couples seeking infertility treatment. We report that higher male age was associated with lower likelihood of fertilization and live birth, and poor embryo development (p < 0.05). Furthermore, our multivariable linear models showed male age was associated with alterations in sperm methylation at 1698 CpGs and 1146 regions (q < 0.05), which were associated with > 750 genes enriched in embryonic development, behavior and neurodevelopment among others. High dimensional mediation analyses identified four genes (DEFB126, TPI1P3, PLCH2 and DLGAP2) with age-related sperm differential methylation that accounted for 64% (95% CI 0.42-0.86%; p < 0.05) of the effect of male age on lower fertilization rate. Our findings from this modest IVF population provide evidence for sperm methylation as a mechanism of age-induced poor reproductive outcomes and identifies possible candidate genes for mediating these effects.

SCSIM: Jointly simulating correlated single-cell and bulk next-generation DNA sequencing data.

BACKGROUND: Recently, it has become possible to collect next-generation DNA sequencing data sets that are composed of multiple samples from multiple biological units where each of these samples may be from a single cell or bulk tissue. Yet, there does not yet exist a tool for simulating DNA sequencing data from such a nested sampling arrangement with single-cell and bulk samples so that developers of analysis methods can assess accuracy and precision. RESULTS: We have developed a tool that simulates DNA sequencing data from hierarchically grouped (correlated) samples where each sample is designated bulk or single-cell. Our tool uses a simple configuration file to define the experimental arrangement and can be integrated into software pipelines for testing of variant callers or other genomic tools. CONCLUSIONS: The DNA sequencing data generated by our simulator is representative of real data and integrates seamlessly with standard downstream analysis tools.

Population-level viral suppression among pregnant and postpartum women in a universal test and treat trial.

OBJECTIVE(S): We sought to determine whether universal 'test and treat' (UTT) can achieve gains in viral suppression beyond universal antiretroviral treatment (ART) eligibility during pregnancy and postpartum, among women living with HIV. DESIGN: A community cluster randomized trial. METHODS: The SEARCH UTT trial compared an intervention of annual population testing and universal ART with a control of baseline population testing with ART by country standard, including ART eligibility for all pregnant/postpartum women, in 32 communities in Kenya and Uganda. When testing, women were asked about current pregnancy and live births over the prior year and, if HIV-infected, had their viral load measured. Between arms, we compared population-level viral suppression (HIV RNA <500 copies/ml) among all pregnant/postpartum HIV-infected women at study close (year 3). We also compared year-3 population-level viral suppression and predictors of viral suppression among all 15 to 45-year-old women by arm. RESULTS: At baseline, 92 and 93% of 15 to 45-year-old women tested for HIV: HIV prevalence was 12.6 and 12.3%, in intervention and control communities, respectively. Among HIV-infected women self-reporting pregnancy/live birth, prevalence of viral suppression was 42 and 44% at baseline, and 81 and 76% (P = 0.02) at year 3, respectively. Among all 15 to 45-year-old HIV-infected women, year-3 population-level viral suppression was higher in intervention (77%) versus control (68%; P < 0.001). Pregnancy/live birth was a predictor of year-3 viral suppression in control (P = 0.016) but not intervention (P = 0.43). Younger age was a risk factor for nonsuppression in both arms. CONCLUSION: The SEARCH intervention resulted in higher population viral suppression among pregnant/postpartum women than a control of baseline universal testing with ART eligibility for pregnant/postpartum women.

GEMINI: a computationally-efficient search engine for large gene expression datasets.

BACKGROUND: Low-cost DNA sequencing allows organizations to accumulate massive amounts of genomic data and use that data to answer a diverse range of research questions. Presently, users must search for relevant genomic data using a keyword, accession number of meta-data tag. However, in this search paradigm the form of the query - a text-based string - is mismatched with the form of the target - a genomic profile. RESULTS: To improve access to massive genomic data resources, we have developed a fast search engine, GEMINI, that uses a genomic profile as a query to search for similar genomic profiles. GEMINI implements a nearest-neighbor search algorithm using a vantage-point tree to store a database of n profiles and in certain circumstances achieves an [Formula: see text] expected query time in the limit. We tested GEMINI on breast and ovarian cancer gene expression data from The Cancer Genome Atlas project and show that it achieves a query time that scales as the logarithm of the number of records in practice on genomic data. In a database with 10(5) samples, GEMINI identifies the nearest neighbor in 0.05 sec compared to a brute force search time of 0.6 sec. CONCLUSIONS: GEMINI is a fast search engine that uses a query genomic profile to search for similar profiles in a very large genomic database. It enables users to identify similar profiles independent of sample label, data origin or other meta-data information.

GLAD: a mixed-membership model for heterogeneous tumor subtype classification.

MOTIVATION: Genomic analyses of many solid cancers have demonstrated extensive genetic heterogeneity between as well as within individual tumors. However, statistical methods for classifying tumors by subtype based on genomic biomarkers generally entail an all-or-none decision, which may be misleading for clinical samples containing a mixture of subtypes and/or normal cell contamination. RESULTS: We have developed a mixed-membership classification model, called glad, that simultaneously learns a sparse biomarker signature for each subtype as well as a distribution over subtypes for each sample. We demonstrate the accuracy of this model on simulated data, in-vitro mixture experiments, and clinical samples from the Cancer Genome Atlas (TCGA) project. We show that many TCGA samples are likely a mixture of multiple subtypes. AVAILABILITY: A python module implementing our algorithm is available from http://genomics.wpi.edu/glad/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.