- Record: found
- Abstract: found
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Maternally expressed NLRP2 links the subcortical maternal complex (SCMC) to fertility, embryogenesis and epigenetic reprogramming
research-article
Author(s):
Sangeetha Mahadevan
1
,
2
,
3 ,
Varsha Sathappan
4 ,
Budi Utama
5 ,
Isabel Lorenzo
6 ,
Khalied Kaskar
1 ,
Ignatia B. Van den Veyver
a
,
1
,
2
,
6
Publication date (Electronic):
20 March 2017
Journal:
Scientific Reports
Publisher:
Nature Publishing Group
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Abstract
Mammalian parental genomes contribute differently to early embryonic development. Before activation of the zygotic genome, the maternal genome provides all transcripts and proteins required for the transition from a highly specialized oocyte to a pluripotent embryo. Depletion of these maternally-encoded transcripts frequently results in failure of preimplantation embryonic development, but their functions in this process are incompletely understood. We found that female mice lacking NLRP2 are subfertile because of early embryonic loss and the production of fewer offspring that have a wide array of developmental phenotypes and abnormal DNA methylation at imprinted loci. By demonstrating that NLRP2 is a member of the subcortical maternal complex (SCMC), an essential cytoplasmic complex in oocytes and preimplantation embryos with poorly understood function, we identified imprinted postzygotic DNA methylation maintenance, likely by directing subcellular localization of proteins involved in this process, such as DNMT1, as a new crucial role of the SCMC for mammalian reproduction.
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Most cited references39
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National, Regional, and Global Trends in Infertility Prevalence Since 1990: A Systematic Analysis of 277 Health Surveys
Maya Mascarenhas, Seth R Flaxman, Ties Boerma … (2012)
Introduction The global health community has had great success in improving maternal and child health in the past decade, partly through a focus on reproductive health [1],[2]. Infertility is a critical component of reproductive health, and has often been neglected in these efforts [3]. The inability to have children affects men and women across the globe. Infertility can lead to distress and depression, as well as discrimination and ostracism [3],[4]. An accurate profile of the prevalence, distribution, and trends of infertility is an important first step towards shaping evidence-based interventions and policies to reduce the burden of this neglected disability globally. Few comparative analyses of global infertility have been conducted, and none, to our knowledge, have applied a consistent algorithm to demographic and reproductive health survey data from both developing and developed countries, nor used these data to estimate regional and global trends in infertility prevalence. Boivin et al. estimated global infertility by summarizing prevalence data from seven studies: five from developed countries and two from developing countries [5]. A Demographic and Health Surveys (DHS) report also estimated infertility for developing countries using survey data from 47 national DHS surveys [6]. The report's estimate of infertility and analysis of trends did not apply to developed countries, nor to China. Ericksen and Brunette [7] and Larsen [8] applied consistent definitions of infertility in their analyses of household survey data, but considered only Sub-Saharan African countries. The main challenges in generating global estimates of infertility are the scarcity of population-based studies and the inconsistent definitions used in the few high-quality studies available [9],[10]. In population-based studies of infertility, there has been little consistency in how prevalence is calculated [9],[11]. An explicit detailing of the numerator and denominator of each definition is needed to make clear what is being measured. The authors of a recent literature review concluded that it is not possible to synthesize infertility prevalence data in the published literature because of the incomparable definitions used [9]. An alternative to synthesizing data found in the literature is to apply a consistent definition to regularly collected demographic and reproductive health survey data. In this paper, we used a consistent algorithm to measure infertility using household survey data. Our measure is a demographic definition that uses live birth as the outcome and a 5-y exposure period based on union status, use of contraceptives, and desire for a child [6]–[8],[12]. There are challenges associated with inferring prevalence from household survey data. Few household surveys ask how long the respondent has tried to get pregnant, and none include a comprehensive medical history and clinical examination. Instead, these surveys may collect information on births, couple status, fertility preferences, and contraceptive use. In a previous analysis we performed sensitivity analyses around each of these components to identify important biases that may arise when information is incomplete [13]. We found that a 5-y exposure period is needed to accommodate the time it takes to become pregnant and give birth, and helps prevent unreported temporary separations, periods of postpartum sexual abstinence, or lactational amenorrhea from unduly affecting the infertility measure. Births, rather than pregnancies, are the preferred outcome, as information on live births is collected more often and reported more accurately: neither pregnancies in the first trimester nor voluntary terminations are reliably reported in household surveys [14]–[16]. Lastly, we argued previously that the intent to have a child serves as a proxy for regular, unprotected sexual intercourse, and may correct for underreporting of contraceptive use [13],[17]. Clinical and epidemiologic infertility definitions are also used to monitor infertility; however, they are not appropriate when making population-based estimates of infertility using household surveys. The clinical definition of infertility used by the World Health Organization (WHO) is “a disease of the reproductive system defined by the failure to achieve a clinical pregnancy after 12 months or more of regular unprotected sexual intercourse” [18], while the WHO's epidemiologic definition is “women of reproductive age at risk of becoming pregnant who report unsuccessfully trying for a pregnancy for more than two years” [19]. Clinical definitions are designed for early detection and treatment of infertility [18]–[20]. A definition and assessment of infertility based on medical histories and diagnostic tests is appropriate for clinical settings, where the aim is to understand causes and provide treatment as soon as it is indicated. However, measuring patterns and trends in infertility at the population level necessitates a measure that may be elicited using a standard set of survey questions [17]. The WHO's epidemiologic definition is more closely aligned with clinical practice than demographic definitions are, and may be measured using survey data. However, few household surveys determine whether a couple is trying to become pregnant, and the majority do not collect information on past pregnancies, only on previous live births. In this study, we analyzed data from a range of reproductive and demographic surveys to estimate infertility prevalence. We applied consistent definitions of primary infertility (inability to have any live birth) and secondary infertility (inability to have an additional live birth). We developed a Bayesian hierarchical model to generate estimates for levels and trends of infertility and their uncertainties by country for the time period 1990 to 2010. Methods Study Design We estimated prevalence of primary and secondary infertility, their trends between 1990 and 2010, and their uncertainties, in 190 countries and territories. We used survey data consisting of interviews with the female partner. Although infertility occurs in couples and may have a male or a female cause, estimates are indexed on the woman in each couple. We made estimates for women aged 20–44 y, excluding infertility during the beginning (15–19 y) and end (45–49 y) of the reproductive period, when fewer couples are seeking a child and estimates of prevalence are less stable. We additionally estimated the proportion of women in each region who were exposed to the risk of pregnancy, i.e., those who were in a union, were not using contraceptives, and had a child or wished to have one, either her first (primary infertility) or an additional (secondary infertility) child. We grouped the countries into the seven regions (High Income, Central/Eastern Europe and Central Asia, East Asia/Pacific, Latin America/Caribbean, North Africa/Middle East, Sub-Saharan Africa, and South Asia) and 21 nested subregions of the Institute for Health Metrics and Evaluation Global Burden of Disease 2010 study (Table A in Text S1). Our analysis included four steps: (1) identification and extraction of data, (2) adjustment of extracted data for known biases as needed, (3) application of a statistical model to estimate infertility prevalence and exposure proportion trends by country and age of the female partner, and (4) calculation of the number of couples currently affected by infertility. We calculated the estimates' uncertainty, taking into account both sampling error and uncertainty from each step of statistical modeling. Data Sources We included data from demographic and reproductive health surveys that we could obtain at the (anonymized) individual level, and hence to which we could apply a consistent definition of infertility. We identified data sources from national demographic studies in a recent systematic literature review of infertility prevalence [9], as well as data that were known to the authors of the present study. To be included, each survey had to collect women's age, current couple status, current contraceptive use, time since first and last births, time since first union, and desire to have a child. Data available only as summary statistics were excluded. We obtained data from the following survey programs: DHS, Reproductive Health Surveys, the World Fertility Survey, the Pan Arab Project for Family Health and Pan Arab Project for Child Development, the European Multicenter Study on Infertility and Subfecundity, the Fertility and Family Survey, the United States National Survey of Family Growth, and the China In-Depth Fertility Sample Surveys (Table 1; Table A and Figure A in Text S1). We included surveys prior to 1990 to capture heterogeneity in levels of infertility in countries that did not have more recent surveys. For each data source, we recorded information on survey population and sampling strategy. For each female survey respondent, we extracted data on union (marriage or cohabitation), birth history, contraceptive use status and history (if available), and the woman's desire for a child or an additional child. We used stated desire for a child to exclude women who take unreported actions to prevent pregnancies or births, including unreported periods of abstinence or contraceptive use, or voluntary terminations [13]. We included women who were undecided about having additional children and women who declared they were unable to become pregnant in the same category as women who stated they wanted another child, because this group is less likely to be preventing pregnancies or births in ways that are not captured by other survey questions. We refer to these women as women who desire a child. We excluded ten Fertility and Family Surveys and three Reproductive Health Surveys because at least one response was missing for more than 15% of respondents. 10.1371/journal.pmed.1001356.t001 Table 1 Surveys included in the analysis. Survey Region or Country Survey Sample Years Number of Countries (Number of Surveys) China In-Depth Fertility Sample Surveys China Eight provinces in China 1985, 1987 1 (7) DHS Developing countries National 1985–2011 75 (193) European Multicenter Study on Infertility and Subfecundity Western Europe Subnational regions 1992 5 Fertility and Family Survey Europe National 1989–1997 12 National Survey of Family and Growth United States National 1988, 1995, 2002, 2007 1 (4) Pan Arab Project for Family Health Middle East National 2002–2004 6 Pan Arab Project for Child Development Middle East National 1990–1997 10 (13) Reproductive Health Surveys Latin America and Eastern Europe National 1989–2008 7 (11) World Fertility Survey Developing countries National 1974–1981 33 Prevalence and Exposure Definitions Mascarenhas et al. evaluated potential bias from using standard demographic or reproductive health surveys to estimate infertility prevalence and recommended the following standard algorithms [13], which we employed (see Figures B and C in Text S1): Primary infertility is defined as the absence of a live birth for women who desire a child and have been in a union for at least five years, during which they have not used any contraceptives. The prevalence of primary infertility is calculated as the number of women in an infertile union divided by the number of women in both infertile and fertile unions, where women in a fertile union have successfully had at least one live birth and have been in the union for at least five years at the time of the survey. Secondary infertility is defined as the absence of a live birth for women who desire a child and have been in a union for at least five years since their last live birth, during which they did not use any contraceptives. The prevalence of secondary infertility is calculated as the number of women in an infertile union divided by the combined number of women in infertile and fertile unions. Women in a fertile union have successfully had at least one live birth in the past five years and, at the time of the survey, have been in a union for at least five years following their first birth. We also calculated the proportion of women of reproductive age (20–44 y) who are exposed to the risk of pregnancy in order to calculate the overall percent of women who are affected by unwanted infertility. Women are exposed if they are fertile, infertile, or their fertility status is not determined at the time of the survey. Specifically: Exposure to primary infertility is defined as the number of women who are currently in a union, are not using any contraceptives, and desire a child, as well as the women who are currently in a union and have given birth to at least one child. The proportion exposed is calculated as the number of women exposed over the total number of women surveyed (Figure B in Text S1). Exposure to secondary infertility is defined as the number of women who have had at least one live birth, are currently in a union, are not using any contraceptives, and desire another child, as well as the women who are currently in a union and have given birth to an additional child in the last 5 y. The proportion exposed is calculated as the number of women exposed over the total number of women surveyed (Figure C in Text S1). A small proportion of DHS surveys in high-fertility countries interview only women who have been in a union. We used exposure data from these surveys for women over age 30 y, as virtually all women in these countries have been in a union by age 30 y. We applied the above definitions to all of the survey data, generating four indicators for each survey: prevalence of primary and secondary infertility and exposure to primary and secondary infertility. We calculated the effective sample size for each indicator to reflect the subset of survey responses used to calculate primary and secondary infertility and to account for sampling uncertainty (Text A and Table B in Text S1). We did not calculate secondary infertility using survey data from China or make estimates of secondary infertility for China, because survey-based estimates of secondary infertility are difficult to interpret in a setting where government regulations strongly affect decisions around limiting family size. Correction of Infertility Prevalence for Incomplete Information on Contraceptive Use and Couple Status Many household surveys ascertain current contraceptive use, but do not collect information on past contraceptive use over a defined exposure period. Using current contraceptive use as a proxy for use over the past 5 y overestimates infertility prevalence, particularly for secondary infertility among younger couples [13],[21]. Likewise, data on time since first union are available more often than data on the length of the current union. Assuming that exposure is continuous from the time of first, rather than current, union can also lead to biases [13]. We developed regressions to correct infertility estimates generated from surveys that did not provide a measure of continuous contraceptive use and couple status over the exposure period, using data from a subset of DHS surveys that provided complete information (Table B in Text S1). The dependent variable in these regressions was the natural log of the less-biased estimate of prevalence, and the independent variables were the biased estimate, age, and, for secondary infertility, prevalence of contraceptive use (further details in Text B in Text S1). The uncertainty of the estimated prevalences included the statistical sampling uncertainty as well as the uncertainty associated with the correction for incomplete information on contraceptive use and union duration. Statistical Analysis Despite the large number of surveys used in this analysis, data were not available for many country-years of interest. In addition, some of the surveys that we used were not nationally representative. As a result, we developed a statistical model to generate estimates for every country and year, including those for which no data were identified. We estimated four indicators: the prevalence of primary infertility, the prevalence of secondary infertility, and the proportion of couples exposed to each type of infertility (see definitions section above). We made these estimates for 190 countries, the years 1990–2010, and each age group. We used a Bayesian hierarchical model to makes estimates for each country-year-age grouping, informed by the unit, if available, and by data from other units. Text C in Text S1 describes the model in detail, the main features of which are summarized below. We fit a hierarchical model in which our estimates for countries were nested within subregional, regional, and global levels. Because the model is hierarchical, estimates for each country are informed by data from the country itself, if available, and by data from other countries, especially countries in the same region. A hierarchical model shares information to a greater degree when data are sparse, uncertain, or inconsistent, and to a lesser degree in data-rich countries and regions. We also modeled hierarchical linear time trends. Specifically, region-specific time trends were nested in a global trend. We used a time-varying covariate to inform our estimates, namely, maternal education (average years of schooling for women of reproductive age) [22]. Subnational studies are less informative than national studies, thus we included separate variance components for subnational and national data sources. These variance components were estimated as part of the model fitting process, allowing national data to have greater influence on estimates than subnational data. Age of the female partner is a major determinant of fertility. We made estimates by 5-y age group for the ages 20–44 y, using indicator variables for each age category. This allowed us to generate a fully flexible age pattern. While the increase in infertility with female age is biologically determined, the age at which women wish to have a child is also culturally determined. Thus, we allowed for different age patterns of exposure to primary fertility in the High Income region, as defined in Table A in Text S1, versus in other regions. We estimated the following sources of uncertainty (see Texts A–C in Text S1 for details): sampling uncertainty in the data sources, uncertainty associated with the conversion from prevalence estimates using incomplete information on contraceptive use and couple status, uncertainty from study design factors for national surveys, additional uncertainty for non-national data sources, and uncertainty from the use of a model to estimate prevalence of primary and secondary infertility by country, year, and age group where data were not available. We fit the Bayesian model using Markov chain Monte Carlo methods to obtain 1,600 samples from the posterior distribution of the model parameters, reflecting the uncertainty from each step of the analysis; these parameter values were in turn used to calculate the posterior distribution of each indicator. We calculated trends by subtracting the estimate for 1990 from the estimate for 2010 for each draw. We calculated central estimates as the mean of the draws, and uncertainty intervals as the 2.5th–97.5th percentiles of these draws. We also reported the posterior probability (pp) that an estimated increase or decrease corresponds to a truly increasing or decreasing trend. pp's are not p-values; they are probabilities: if the pp of an increase is 0.5 then an increase and a decrease are both equally likely, while a high pp of an increase indicates high certainty that an increase occurred. We considered a trend to be statistically significant if its pp was greater than 0.975. Survey analyses were carried out using Stata 10.1, and Markov chain Monte Carlo analysis was carried out in Python using the PyMC package [23]. We evaluated the predictive validity of our models' central estimates and their uncertainty intervals by performing cross-validation. We ran each model five times, each time withholding data from a random sample of 20% of countries. We then compared the model predictions to the known-but-withheld data. For each model, we calculated the root mean square error, median relative error, and the percent of withheld data that fell within the model's 95% uncertainty interval. We report four results: prevalence of primary and secondary infertility among child-seeking women, i.e., among women who are exposed to the risk of pregnancy, and the percent of primary and secondary infertility among all women of reproductive age, calculated as the product of the prevalence of infertility among child-seeking women and the proportion who are exposed to the risk of pregnancy. We also calculated the number of couples affected by infertility using population data from the United Nations Population Division's “World Population Prospects: 2010 revision” [24]. We also report two additional indicators, percent of women exposed to the risk of primary and secondary infertility, in Figures H, I, M, and N in Text S1. All estimates were made by country and age; we calculated all-age, regional, and global estimates by weighting country- and age-specific estimates by the population of women in the relevant age group. Results We identified 277 demographic and reproductive health surveys, including seven multi-country programs and two country-specific surveys, that included questions on infertility and for which we could obtain the individual-level questionnaire responses (Table 1; Table B and Figure A in Text S1). National data were available for 101 countries, and regional data were obtained for a further three countries. At least two surveys were available for 69 countries. The South Asia and Sub-Saharan Africa regions had the greatest data availability, with at least one survey available for 67% of countries and an average of more than two surveys per country. There were fewer data available for the High Income and Central/Eastern Europe and Central Asia regions: we did not identify any data for 38 of 59 countries in these regions (64%), and we identified two or more data sources for only nine of these countries. Predictive validity statistics are shown in Table C in Text S1. Root mean square prediction errors for countries for which data were left out were 1.3% for primary prevalence, 6.1% for secondary prevalence, and 9.1%–13.1% for exposure to primary and secondary fertility (see Figures D–I in Text S1 for graphical presentation of model fit). The models' 95% uncertainty intervals contained 93%–96% of left-out data points. In 2010, 1.9% of child-seeking women aged 20–44 y were unable to have a first live birth (primary infertility; 95% uncertainty interval 1.7%, 2.2%), and 10.5% of child-seeking women with a prior live birth were unable to have an additional live birth (secondary infertility; 9.5%, 11.7%). Levels of infertility were similar in 1990 and 2010, decreasing 0.1 (−0.1, 0.3) percentage points for primary infertility (from 2.0% [1.9%, 2.2%] in 1990; pp = 0.84) and increasing 0.4 (−0.8, 1.6) percentage points for secondary infertility (from 10.2% [9.3%, 11.1%] in 1990; pp of increase = 0.71). Figure 1 presents the prevalence of primary and secondary infertility by age (see Figure J in Text S1 for age pattern of exposure to primary and secondary infertility). The prevalence of primary infertility was higher among women aged 20–24 y (2.7% [2.4%, 3.0%] in 2010) compared to women aged 25–29 y (2.0% [1.8%, 2.2%]) and women aged 30–44 y (ranging from 1.6% to 1.7% in 2010). Prevalence of secondary infertility increased sharply with age, from 2.6% (2.3%, 3.0%) in women aged 20–24 y to 27.1% (24.7%, 29.9%) in women aged 40–44 y. Both age patterns are less pronounced when calculated as a percent of all women (Figure 1). 10.1371/journal.pmed.1001356.g001 Figure 1 Global prevalence of primary and secondary infertility in 2010, by the female partner's age. Infertility is calculated as the percent of women who seek a child and as the percent of all women of reproductive age. The solid line represents the posterior mean, and the shaded area the 95% uncertainty interval. Patterns and Trends in Infertility among Child-Seeking Women Primary infertility prevalence among child-seeking women varied by region in 2010, from 1.5% (1.2%, 1.8%) in the Latin America/Caribbean region, to 2.6% (2.1%, 3.1%) in the North Africa/Middle East region (Figure 2; Dataset S1). Twenty-year trends in infertility prevalence were not statistically significant in most regions, with low-certainty increases in prevalence in Central/Eastern Europe and Central Asia (0.4 [−0.4, 1.6] percentage points; pp = 0.79) and in the East Asia/Pacific region (0.1 percentage points [−0.2, 0.4]; pp = 0.71), and non-significant declines in the High Income, North Africa/Middle East, and Latin America/Caribbean regions (ranging from 0.0 to 0.2 percentage points; pp 0.56–0.93). In South Asia, the prevalence of primary infertility declined 0.6 (0.1, 1.0) percentage points (pp = 0.99); however, this decline was attenuated, declining 0.3 (−0.3, 1.0) percentage points (pp = 0.88), if World Fertility Surveys data from 1974–1981 were excluded from the model (results not shown). The decline in primary infertility was greatest in Sub-Saharan Africa, which experienced a substantial decline in primary infertility, from 2.7% (2.5%, 3.0%) in 1990 to 1.9% (1.8%, 2.1%) in 2010, a decline of 0.8 (0.5, 1.1) percentage points over the 20-y period (pp>0.99). This resulted in a reordering of the regions by primary infertility prevalence: in 1990, Sub-Saharan Africa and South Asia had the two highest prevalences of primary infertility, and in 2010, they were 4th and 2nd highest of seven regions, respectively. 10.1371/journal.pmed.1001356.g002 Figure 2 Prevalence of primary infertility and secondary infertility, presented as the percent of women who seek a child, and as the percent of all women of reproductive age, in 1990 and 2010. Infertility prevalence is indexed on the female partner; age-standardized prevalence among women aged 20–44 y is shown here. Horizontal lines indicate the 95% uncertainty interval. The prevalence of primary infertility varied within these regions (Figure 3; Dataset S2; Figure K in Text S1). Within the Sub-Saharan Africa region, the prevalence was lowest in East Africa and Southern Africa. Kenya, Zimbabwe, and Rwanda all had low prevalences of primary infertility in Sub-Saharan Africa in 2010 (1.0%–1.1%). In contrast, some countries, mostly in central Sub-Saharan Africa, had very high prevalences: Equatorial Guinea, Mozambique, Angola, Gabon, Cameroon, and the Central African Republic all had prevalences of 2.5% or greater. Primary infertility prevalence also varied within the Latin America/Caribbean region: some Caribbean countries had prevalences of 2.5% or greater in 2010: Jamaica, Suriname, Haiti, and Trinidad and Tobago. In contrast, all countries in Central Latin America and Andean Latin America had prevalences of 1.6% or less. 10.1371/journal.pmed.1001356.g003 Figure 3 Prevalence of primary infertility among women who seek a child, in 2010. Infertility prevalence is indexed on the female partner; age-standardized prevalence among women aged 20–44 y is shown here. In 2010, the lowest estimated prevalences of primary infertility occurred in middle-income countries in Latin America (Peru, Bolivia, Ecuador, and El Salvador; 0.8%–1.0%) and in Poland, Kenya, and the Republic of Korea (0.9%–1.0%). At the other extreme, 13 countries in Eastern Europe, North Africa/Middle East, Oceania, and Sub-Saharan Africa had prevalences of 3.0% or greater. Global and country patterns of secondary infertility were similar to those of primary infertility, with two notable exceptions: first, the prevalence of primary infertility was high in some countries in the North Africa/Middle East region, notably Morocco and Yemen, with prevalences greater than 3%, but prevalence of secondary infertility was low in those same countries (Figures 2–4; Dataset S1). Second, the prevalence of primary infertility observed in the Central/Eastern Europe and Central Asia region was low-to-intermediate relative to that of other regions, though this region had the highest prevalence of secondary infertility. 10.1371/journal.pmed.1001356.g004 Figure 4 Prevalence of secondary infertility among women who have had a live birth and seek another, in 2010. Infertility prevalence is indexed on the female partner; age-standardized prevalence among women aged 20–44 y is shown here. The prevalence of secondary infertility ranged from 7.2% (5.0%, 10.2%) in the High Income region and 7.2% (5.9%, 8.6%) in the North Africa/Middle East region to 18.0% (13.8%, 24.1%) in the Central/Eastern Europe and Central Asia region. Most regions experienced non-significant increases in the prevalence of secondary infertility between 1990 and 2010 (pps = 0.64–0.81; Figure 5), with the exception of Sub-Saharan Africa, where the prevalence of secondary infertility declined from 13.5% (12.5%, 14.5%) in 1990 to 11.6% (10.6%, 12.6%; pp>0.99) in 2010. 10.1371/journal.pmed.1001356.g005 Figure 5 Absolute change in prevalence of primary and secondary infertility, measured as the percent of women who seek a child and as the percent of all women of reproductive age, between 1990 and 2010. Infertility prevalence is indexed on the female partner; change in age-standardized prevalence among women aged 20–44 y is shown here. Horizontal lines indicate the 95% uncertainty interval. Like primary infertility, the prevalence of secondary infertility varied by country within each region, particularly in Sub-Saharan Africa (Figure 4; Dataset S2; Figure L in Text S1). In 2010, eight countries in five regions had a prevalence of secondary infertility below 6%: Rwanda, Jordan, Peru, United States of America, Bolivia, Egypt, Tunisia, and Viet Nam. At the other extreme, 19 countries in Central/Eastern Europe and Central Asia and four in Sub-Saharan Africa had prevalences greater than 16%. Patterns and Trends in Infertility among All Women of Reproductive Age Mirroring worldwide declines in fertility, the proportion of women with one or more children who are at risk of pregnancy has decreased since 1990 in every world region (Figure N in Text S1). This has resulted in a decrease in the percent of women of reproductive age who are affected by secondary infertility in every world region (Figure 5; Figures O and P in Text S1). Worldwide, the age-standardized percent of women aged 20–44 y affected by secondary infertility has decreased from 3.9% (3.6%, 4.3%) to 3.0% (2.7%, 3.3%); the pp of this decline being real is ≥0.9 in the High Income and Central/Eastern Europe and Central Asia regions, and ≥0.99 globally and in all other world regions. The proportion of women who want a first child has decreased less over time, meaning that the proportion of women who are affected by primary infertility has changed little, from 1.6% (1.5%, 1.7%) in 1990 to 1.5% (1.3%, 1.7%) in 2010 (pp = 0.90). Worldwide, 48.5 million (45.0 million, 52.6 million) couples are unable to have a child, of which 19.2 million (17.0 million, 21.5 million) couples are unable to have a first child, and 29.3 million (26.3 million, 32.6 million) couples are unable to have an additional child (the latter figure excludes China). 14.4 million (12.2 million, 16.8 million) of these couples live in South Asia, and a further 10.0 million (9.3 million, 10.8 million) live in Sub-Saharan Africa. The number of couples suffering from infertility has increased since 1990, when 42.0 million (39.6 million, 44.8 million) couples were unable to have a child. Though the number of infertile couples has increased globally and in most regions, it has decreased from 4.2 million in 1990 to 3.6 million in 2010 in the High Income region, and from 4.4 million in 1990 to 3.8 million in 2010 in the Central/Eastern Europe and Central Asia region. Although there were no significant changes in the prevalence of infertility amongst child-seeking women, reduced child-seeking behavior coupled with a lack of population growth resulted in a decrease in the absolute number of infertile couples in these regions. Discussion In 2010, an estimated 48.5 million (45.0 million, 52.6 million) couples worldwide were infertile. Between 1990 and 2010, levels of primary and secondary infertility changed little in most world regions. The exceptions were Sub-Saharan Africa and South Asia (for primary infertility only), where infertility prevalence decreased during the 20-y period. Reduced child-seeking behavior (i.e., reduced exposure to pregnancy due to changing fertility preferences) means that even where infertility prevalence among those exposed to the risk of pregnancy did not change, a decreasing proportion of couples were affected by infertility because fewer attempted to have a child. However, the absolute number of infertile couples increased due to population growth. Our estimate of the global number of couples affected by infertility is lower than that of Boivin et al. [5] or Rutstein and Shah [6]. Boiven et al. estimated 72.4 million women were currently infertile in 2006 [5]. They used the median prevalence reported by seven published infertility studies that used a 12- or 24-mo definition of infertility; our estimates differ because we used a larger dataset and a different algorithm to calculate infertility [5],[10]. Rutstein and Shah presented a variety of infertility measures using DHS data from the late 1990s, demonstrating the importance of choices in defining infertility [6]. They estimated that 186 million ever-married women in developing countries (excluding China) were infertile in 2002; this larger number is a result of definitional differences: they included women who may not have been exposed to the risk of pregnancy and women aged 15–20 y and 45–49 y, age groups that have higher prevalences of infertility than women aged 20–44 y. The strengths of this study were the application of consistent algorithms to calculate primary and secondary infertility from 277 survey datasets, most of which were nationally representative; our use of a Bayesian hierarchical model to estimate infertility prevalence and trends; and our systematic quantification of uncertainty. We identified where survey data did not collect information on past contraceptive use or marital status, and corrected for biases that arose when information on contraceptive use or marriage was incomplete. We used definitions of primary and secondary infertility that allowed us to disentangle trends in ability to have a child from trends in fertility preferences [25]. Specifically, women who were not in a union, had used any contraceptive in the previous 5 y, or did not wish to have a child were excluded from both the numerator and the denominator when calculating the prevalence of infertility. This allowed us to calculate trends in infertility that were independent from worldwide declines in the preferred number of children and independent of population growth in that time period. The major limitations of our study are gaps in data for certain countries, the use of proxies to assess exposure to pregnancy, potential reporting inaccuracies, and the inability of our definition to capture all instances of infertility. Despite extensive data seeking, data gaps remained, especially in high-income countries and in Central and Eastern Europe. The use of demographic and reproductive health surveys to infer infertility prevalence requires several assumptions. First, we assume that women who are in a union, wish to have a child, and are not using contraceptives are engaged in regular, unprotected sexual intercourse. We also rely on women's reported couple status, births, contraceptive use, and desire for a child. These assumptions may be violated, as women may not report accurately on sensitive topics, such as past voluntary abortions [26],[27]. Women might also report non-biological children as their own. Furthermore, the reporting of the date of marriage and date of last birth may not be accurate in some settings [7]. Several studies have found that, in China, reporting of births in household surveys may be suppressed or the timing of births may be misreported because of policy considerations, which could affect our infertility estimates [28]–[30]. Finally, infertile women may state that they do not want a child, as a coping mechanism [17],[31]. Our correction of incomplete contraceptive and marriage information, use of birth as the outcome, and use of a 5-y infertility definition reduced the susceptibility of our estimates to these biases [13]. Some types of infertility are not measured using our algorithm [32]. The algorithm cannot capture any infertile men whose female partners conceive and give birth to a child with another man, nor primary infertility in men who have had multiple partners. It is not possible to capture infertile couples trying to have a child but using condoms intermittently for sexually transmitted infection (STI) prevention [21]. Lastly, our 5-y definition excludes from the prevalence estimation men and women who do not maintain a union for 5 y. Our prevalence estimate of infertility, however, is applied to all couples in a union, independent of the length, to calculate absolute numbers of couples affected. To the extent that infertile unions are more likely to dissolve than fertile unions, we expect our estimate to be biased downwards because we only measure infertility in unions that last for 5 y [33]. There are several important implications of the algorithm we use to measure infertility. We measure current infertility using a 5-y exposure with birth as an outcome. An infertility measure based on ability to become pregnant may have different patterns, trends, and levels than those presented in this paper. Infertility prevalences measured using a shorter exposure period would have a similar geographic and temporal pattern, but would be approximately twice as high as our estimates (see Figure Q in Text S1; [13]) The shorter exposure period identifies couples affected by temporary separations or periods of abstinence or lactational amenorrhea, infertility that resolves at between 2 and 5 y, and infertile unions that dissolve after 2 y but before 5 y without a birth. Our algorithm does not capture childlessness experienced by couples who are no longer of reproductive age or infertility experienced by women aged less than 20 y. Infertility that is identified and successfully treated within a 5-y period is not captured by this definition. Finally, men and women who use contraceptives, choose to be childless, or are not in a union, may indeed be infertile. However, these individuals are not included in our estimate of the number of infertile unions. We aimed to calculate the number of couples currently affected by infertility, and these individuals are not currently attempting to have a child, or, in the case of those not in a union, it is not possible to determine whether they are attempting to have a child. Multiple factors—infectious, environmental, genetic, and even dietary in origin—can contribute to infertility [34]. These factors may affect the female, the male, or both partners in a union, resulting in an inability to become pregnant or carry a child to term. Current evidence, mostly from clinical studies with few exceptions [35], indicates that differences in the incidence and prevalence of infectious diseases, leading to fallopian tube blockage in women, are the main reason for changes over time and differences between populations [36]–[39]. Some have hypothesized that sperm quality is declining [40], but the evidence is not conclusive [41]. Increasing age at childbearing could also increase the prevalence of infertility, as the ability to become pregnant and deliver a live birth reduces with age in all populations. Globally, the mean age at childbearing has remained the same (about 28 y) since the 1970s, although this masks regional and temporal heterogeneity in trends [42]. In low- and middle-income countries, age at first birth has increased, although first birth still occurs at young ages: in 40 countries with one DHS survey in the 1990s and another survey during 2000–2011, the overall median of the median age at first birth among women aged 25–49 y increased from 19.8 to 20.3 y [42]. While the age at first birth has increased, the average number of children has decreased, and thus, the mean age at childbearing has not changed in these countries [42]. On the other hand, mean age at first birth and mean age at childbearing have increased in all developed countries since the 1990s [42],[43]. This does not appear to have affected primary infertility levels in those countries. However, it may have contributed to the modest increase in secondary infertility that we estimated. The geographic pattern of infertility prevalence we found is consistent with previous estimates of infertility in Sub-Saharan Africa, specifically high prevalence in some West, Central, and Southern African countries, and low prevalence in most East African countries [7],[8],[44]. This pattern has mainly been attributed to the consequences of untreated reproductive tract infections, including both STIs such as Neisseria gonorrhoeae and Chlamydia trachomatis, and, to a lesser extent, infections from unsafe abortions or obstetric practices [34],[36],[45]. The improved trends for the region as a whole may be due to reduced prevalence of STIs, possibly associated with changes in sexual behavior and STI treatment in response to the HIV epidemic. There are, however, no reliable data on regional trends in the prevalence of STIs. WHO estimated that the prevalence of C. trachomatis infections among adult females in 2005 was 4%–6% in all regions of the world, except the WHO Eastern Mediterranean and South East Asia regions, where prevalence was below 2% [46]. N. gonorrhoeae was considerably more prevalent in the WHO African region than all other regions among adult women and men. If the prevalence of maternal syphilis has decreased since 1990, it may have reduced the risk of stillbirths and therefore increased the ability to have a live birth, which is our definition of fertility [47]–[50]. Infection is also associated with reduced fertility. Infertile women, especially those with primary infertility, are more likely to acquire HIV infection because of greater marital instability [51], and HIV is also associated with reduced fertility in the later stages of infection [52]. However, the population effect of the HIV epidemic on fertility is likely small: despite the epidemic, infertility declined in all Sub-Saharan African subregions. Post-abortion complications are also an important factor contributing to infertility. The risk is higher for unsafe practices than for safe abortion procedures. The relatively high levels of secondary infertility in the Central/Eastern Europe and Central Asia region may be associated with the higher incidence of abortion. In these regions, the abortion rate declined between 1995 and 2003, but stayed at levels higher than the global average [16]. Both induced abortions and higher levels of STIs/HIV may play a role in explaining the elevated levels of secondary infertility in the Caribbean. Declines in unsafe abortion rates in Sub-Saharan Africa between 1995 and 2003 may have contributed to declines in infertility rates [16]. Among women who have had a pregnancy or birth, pregnancy complications may cause infections of the reproductive tract that result in infertility. Maternal mortality ratios—an indicator of obstetric risk—are estimated to have declined slightly in Sub-Saharan Africa and more substantially Southern Asia since 1990, and it is possible that injuries/infections caused or aggravated by childbirth declined together with decreases in maternal mortality [2]. Including questions on how long women have tried to become pregnant in national or international survey programs would allow for the use of a definition that is more closely aligned with clinical practice than the algorithm used in this study. This may lead to more reliable estimation of levels and trends in infertility than current methods, which in turn would inform policy and program requirements to address this neglected area of reproductive health. However, in the absence of widespread data collection on time to pregnancy, the methods used and results presented here provide valuable insights into global, regional, and country patterns and trends in infertility. Supporting Information Dataset S1 Prevalence of primary and secondary infertility by region and globally, 1990 and 2010. (XLSX) Click here for additional data file. Dataset S2 Prevalence of primary and secondary infertility by country, 1990 and 2010. (XLSX) Click here for additional data file. Text S1 Additional methods and results. (PDF) Click here for additional data file.
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In Embryonic Stem Cells, ZFP57/KAP1 Recognize a Methylated Hexanucleotide to Affect Chromatin and DNA Methylation of Imprinting Control Regions
Simon Quenneville, Gaetano Verde, Andrea Corsinotti … (2011)
Introduction In higher mammals, most autosomal genes are expressed from alleles inherited from both the father and the mother. Imprinted genes are exceptions to this rule, being transcribed from only one parental allele along patterns that are determined during gamete formation. Imprinted genes play key developmental roles and are generally organized in clusters controlled by CpG-rich sequences known as imprinting control regions (ICRs) (Weaver et al., 2010). ICRs are targeted by parental allele- specific DNA methylation imprints that are established during late gametogenesis, maintained in the zygote and somatic cells, and erased in primordial germ cells (Reik, 2007). ICRs can exert their effect through various mechanisms. Several ICRs with maternally inherited methylation correspond to the promoters of long noncoding and antisense RNAs with in cis silencing activity toward the imprinted genes of the same cluster (Koerner et al., 2009). In contrast, ICRs with paternally inherited methylation do not contain promoters but at least two of them display enhancer-blocking (insulator) activity. Notably, the promoter or insulator activities of ICRs are manifest only on the allele that is not methylated. In embryonic stem cells (ES cells), the chromatin associated with the ICRs carries allele-specific histone modifications (Kacem and Feil, 2009). In general, H3K9me3, H4K20me3, and H4/H2AR3me2 are associated with methylated ICR alleles, whereas H3K4me2/3 and H3/H4 acetylation are found on their nonmethylated counterparts. Furthermore, depletion of the H3K9 methyltransferase G9a and Polycomb group protein-mediated H3K27 methylation affect the expression of imprinted genes in a gene- and tissue-specific manner (Mager et al., 2003; Nagano et al., 2008; Sanz et al., 2008; Wagschal et al., 2008). In addition, removal of H3K4 methylation by the histone demethylase KDM1B is necessary for establishment of DNA methylation imprints at some loci in oocytes (Ciccone and Chen, 2009). Gene knockout experiments in mice have recently implicated the Krüppel-associated box-containing zinc-finger protein (KRAB-ZFP) ZFP57 in the establishment and maintenance of several imprinted loci (Li et al., 2008), and loss-of-function mutations in the human Zfp57 gene are associated with hypomethylation at multiple imprinted regions in individuals affected by transient neonatal diabetes (Mackay et al., 2008). The genomes of higher vertebrates encode close to 400 KRAB-ZFPs. These proteins all harbor a so-called KRAB domain that is situated upstream of an array of 2–40 C2H2 zinc fingers, which can provide sequence-specific DNA-binding ability. KRAB recruits KRAB-associated protein 1(KAP1), also known as TRIM 28, Tif1b, or KRIP-1, which acts as a scaffold for various heterochromatin-inducing factors, such as heterochromatin protein 1 (HP1), the histone methyltransferase SETDB1 (also known as ESET), the nucleosome remodeling and histone deacetylation (NuRD) complex, and the nuclear receptor corepressor complex 1 (N-CoR1) (Schultz et al., 20001, 2002). Therefore, the binding of KRAB-ZFPs to specific chromosomal loci results in nearby transcriptional repression and establishes heterochromatin marks, such as H3K9me3. Correspondingly, KRAB-ZFPs can no longer mediate transcriptional repression when KAP1 is inactivated (Groner et al., 2010). KRAB-ZFPs exhibit tissue-specific patterns of expression, and ZFP57 can be detected in ES cells, ovaries, testes, and the nervous system (Alonso et al., 2004; Li et al., 2008). Mouse and human ZFP57 contain three and six C2H2 zinc fingers, respectively, two of which are conserved as amino acids predicted to govern nucleotide sequence-specific recognition. Several pieces of evidence link KRAB-ZFPs and KAP1 to DNA methylation during the early embryonic period. First, KRAB-mediated tethering of KAP1 to the vicinity of promoters during the first few days of embryogenesis leads to their permanent silencing via CpG methylation (Wiznerowicz et al., 2007). Second, KAP1 controls endogenous retroviruses in ES cells and, together with ZFP809, is responsible for the methylation and silencing of the murine leukemia virus (MLV) in these targets (Rowe et al., 2010; Wolf and Goff, 2009). Finally, the replacement of wild-type (WT) KAP1 by a mutant defective for HP1 binding in embryonic carcinoma cells leads to loss of methylation and upregulation of the imprinted MEST gene (Riclet et al., 2009). The present work further investigates the roles of ZFP57 and KAP1 in the regulation of imprints during the early embryonic period. Our results demonstrate that, in embryonic stem cells, ZFP57/KAP1 interact in a parental allele-specific fashion with the ICRs and that their presence is necessary for the recruitment of chromatin and DNA modifiers at the imprinted loci. Furthermore, we unveil the role of a methylated hexanucleotidic motif responsible for recruiting ZFP57/KAP1 to ICRs and to a number of additional loci, at least some of which are also methylated in a ZFP57-dependent fashion. Results KAP1 Deletion in Embryonic Stem Cells Leads to Chromatin Changes at Imprinting Control Regions We examined the status of imprinted genes in previously described ES cell lines, allowing for tamoxifen (4-OHT)-induced KAP1 deletion. When the KRAB-ZFP cofactor is removed, these cells lose pluripotency markers, markedly upregulate endogenous retroviruses, undergo differentiation, and stop dividing (Rowe et al., 2010). The levels of H3K9 methylation and H3 acetylation were investigated in control and KAP1-deleted ES cells by chromatin imunoprecipitation (ChIP) combined with quantitative polymerase chain reaction (qPCR)-mediated DNA amplification, using primers specific for several ICRs and isolated imprinted genes (Figures 1A and 1B). In control ES cells, both H3K9me3 and H3K9ac were present at these loci, consistent with previous studies (Kacem and Feil, 2009). Seventy-two hours after KAP1 deletion, levels of H3K9ac were markedly increased, and H3K9me3 was almost completely lost at all tested ICRs. DNA methylation, as assessed by combined bisulfite restriction analysis (COBRA) (Figure S1A available online), was unaffected in Kap1-knockout ES cells, as expected in view of their rapid growth arrest (Figures S1C and S1D). Having found that KAP1 binding correlates with H3K9me3 deposition at ICRs, we tested its association with these loci. We first engineered an ES cell line that expresses a modified KAP1 protein carrying six hemagglutinin (HA) tags at its N-terminus. This 6HA-KAP1 derivative was fully functional, as it could prevent the drop in pluripotency markers and the accumulation in G1 that were otherwise induced by deleting endogenous KAP1 (Figures S1C and S1D). Thus, we performed ChIP with a HA-specific monoclonal antibody in 6HA-KAP1-expressing ES cells, followed by qPCR analysis with ICR-specific primers. KAP1 was found to bind all tested ICRs (Figure 1C). ZFP57 Mediates KAP1 Recruitment to DNA-Methylated Imprinting Control Regions ZFP57 was previously detected at only a limited number of ICRs (Li et al., 2008). Using ChIP-PCR with a 6HA-tagged ZFP57 introduced in ES cells by lentiviral vector-mediated transduction, we found that ZFP57 associated with all tested ICRs (Figure 2A and data not shown). To confirm this result, we repeated the KAP1-specific ChIP studies in Zfp57−/− ES cells (Akagi et al., 2005). Although the master regulator was normally recruited to intracisternal A particles (IAPs), a subgroup of endogenous retroviruses (ERVs) in these cells, it was no longer associated with ICRs, which were also devoid of H3K9me3 (Figures 2B and 2C). Furthermore, a deep-sequencing analysis of polyadenylated RNA revealed that only few genes were dysregulated in Zfp57−/− cells and that the most significantly altered genes were the upregulated Meg3, Peg13, Snrnp, and Snurfs and the downregulated Cdkn1c and Phlda2, which are all parts of known imprinted clusters (Figure 2D and Figure S2). KAP1, ZFP57, SETDB1, and HP1 Colocalize with H3K9me3 at DNA-Methylated Imprinted Control Region Alleles In order to investigate further the role of ZFP57, KAP1, and their known partners, such as the H3K9 methyltransferase SETDB1 and heterochromatin protein 1 (HP1), in the control of the parent of origin-dependent epigenetic status of the imprinted loci, we examined the potential allelic specificity of their recruitment to ICRs. The SF1-G ES cell line derives from a C57-Black6 × Mus spretus F1 hybrid, maintains genomic imprinting, and carries correct allele-specific DNA methylation and histone modifications at the ICRs (Delaval et al., 2007). In these cells, maternal and paternal ICR alleles can be distinguished, owing to sequence polymorphisms. Thus, we selected five maternally methylated ICRs (Snrpn, KvDMR1, Igf2r, Peg3, and Gnas/Nespas) and two paternally methylated ICRs (Igf2/H19 and Rasgrf1), and performed ChIP studies in SF1-G ES cells with antibodies that recognize the endogenous proteins to ask whether ICR recruitment of ZFP57, KAP1, and their associated proteins was parental allele-specific (Figures S3A–S3C). To determine the relative abundance of the maternal and paternal alleles, ChIP-PCR DNA products were digested with a restriction enzyme that cut only one allele or, for Rasgrf1, were run on capillary electrophoresis separating alleles of different lengths (Figures 3 and S3D). To ascertain the validity of our approach, we first analyzed CTCF, the multizinc-finger protein that is known to bind the H19 ICR on the maternal, nonmethylated allele (Stedman et al., 2008). The results confirmed a specific CTCF-H19 ICR interaction with a strong bias toward the maternal allele, revealing, in contrast, that KAP1, ZFP57, SETDB1, and HP1γ interacted preferentially with their methylated paternal counterparts (Figure 3A). When other ICRs were investigated, we found that these four factors were always preferentially immunoprecipitated with the paternal allele at the paternally methylated ICRs and with the maternal allele at the maternally methylated ICRs (Figure 3A). The presence of ZFP57/KAP1 and their associated factors also correlated with high levels of H3K9me3 and low levels of H3K9Ac that marked the opposite parental allele. To confirm and extend these results, we used bisulfite sequencing to determine directly the methylation status of the ZFP57/KAP1 recruiting allele of the H19, Igf2r, and Snrpn ICRs. Methylated and nonmethylated alleles of these elements were amplified with equal efficiencies from input DNA, but only methylated molecules derived from the paternal H19, and maternal Igf2r and Snrpn alleles were detected in the ZFP57- and KAP1-bound DNA (Figure 3B). KAP1 Recruitment to Imprinting Control Regions is DNA Methylation-Dependent Because KAP1 exclusively binds the methylated allele of ICRs, we examined its status in cells deleted for the DNA methyltransferases DNMT1, DNMT3A, and DNMT3B. These cells have drastically reduced levels of DNA methylation over the entire genome, including at ICRs and ERV sequences (Tsumura et al., 2006). ChIP readily detected KAP1 and H3K9me3 at ICRs in parental but not in Dnmt triple-knockout ES cells, although both marks were present at ERVs in both settings (Figure 4). As KAP1 binding to DNA is usually indirect and mediated by KRAB-containing ZFPs (Sripathy et al., 2006), this result suggests that although some KRAB-ZFPs (e.g., ZFP57) require their DNA target to be methylated for binding, other members of this family (e.g., ERV-specific KRAB-ZFPs) recognize their cognate sequences as unmethylated DNA. ZFP57 is Necessary for the Maintenance of DNA Methylation at Imprinted Control Regions Upon examining Zfp57−/− embryos obtained by crossing a heterozygous male with a homozygous null female, Li et al. observed loss of DNA methylation at the DLK1 ICR (IG-DMR), indicating that methylation of a paternally methylated ICR cannot be maintained in the absence of both maternal and zygotic ZFP57 (Li et al., 2008). To investigate further the role of ZFP57 in the maintenance DNA methlylation at ICRs, we turned to the Zfp57−/− ES cell line, which was obtained by successively knocking out the two alleles with two different constructs (i.e., bearing two different antibiotic resistance genes) without passage through gametogenesis, thus achieving epigenetic reprogramming (Akagi et al., 2005). WT parental cells were used as controls because they allowed comparison with the methylation status of ICRs before ZFP57 removal. The Snrpn, KvDMR1, Rasgrf1, Peg3, and Gnas/Nespas ICRs exhibited the expected 50% DNA methylation rates in control ES cells, and IAPs were fully methylated in both these and their Zfp57−/− derivatives. In contrast, ICR DNA methylation was almost completely absent in the knockout cells (Figures 5A and S4). Notably, we could not test the effect of zfp57 deletion on the methylation of the Igf2/H19 and Igf2r ICRs because these sequences were already hypomethylated in the WT parental ES cell line (data not shown). ZFP57/KAP1 Associate with NP95 and DNA Methyltransferases A mass spectroscopic analysis of KAP1-associated proteins in ES cells revealed the presence of the multidomain protein NP95 (also known as UHRF1 or ICBP90; not illustrated). NP95 was previously demonstrated to help recruit DNMT1 to hemimethylated DNA, thereby playing an important role in the maintenance of DNA methylation in ES cells (Arita et al., 2008; Avvakumov et al., 2008; Bostick et al., 2007; Meilinger et al., 2009; Sharif et al., 2007). To confirm the KAP1-NP95 interaction, a FLAG-tagged NP95 variant was introduced in ES cells by lentivector-mediated transduction and immunoprecipitations were performed with KAP1- and FLAG-specific antibodies. Western blot analyses of the immunoprecipitates confirmed that KAP1 associates with NP95 in ES cells (Figure 5B). Furthermore, DNMT1, 3A, and 3B could also be coimmunoprecipitated with KAP1 and ZFP57 in ES cells (Figure 5C). Identification of ZFP57-Dependently Methylated ZFP57/KAP1 Targets in Embryonic Stem Cells Having established the role of ZFP57/KAP1 in the maintenance of histone marks and DNA methylation at ICRs, we sought to explore the full range of DNA loci targeted by this phenomenon. Therefore, we performed ChIP-Seq analyses to identify all KAP1 and ZFP57 binding sites in ES cells. We superimposed these data with those previously obtained for SETDB1 (Bilodeau et al., 2009). Using a low-stringency peak-calling strategy, we detected approximately 11,000 of both KAP1- and ZFP57-specific peaks, among which 2,375 appeared common to the two markers. Of these, 216 also coincided with SETDB1 peaks, a number that was further reduced by direct examination and elimination of sites where ZFP57, KAP1, and SETDB1 peaks were not precisely colocalized. This high stringency criteria left 91 peaks, which included all known ICRs, thus supporting the validity of our approach (Figures 6A and S5 and data not shown). Two representatives of non-ICR peaks were examined in greater depth. The Zfp629 and Fkbp6 sites both recruited ZFP57, KAP1, and SETDB1, and both sites could also be immunoprecipitated with H3K9me3 antibodies (Figure 6B). Furthermore, DNA at these locations was heavily methylated in WT ES cells but was almost completely unmethylated in Zfp57 KO ES cells (Figure 6C). This suggests that ZFP57/KAP1 and associated proteins are involved in maintaining the chromatin status and DNA methylation not only at known ICRs but also at a number of additional selected loci. A Hexanucleotidic Motif Is Responsible for the Methylation-Dependent Recruitment of ZFP57 Although our experiments thus far demonstrated that ZFP57 only binds the methylated allele of ICRs, whether it recognizes a specific DNA sequence remained to be determined. To do so, we subjected the 350 bp-long central fragments of the 91 identified ZFP57/KAP1/SETDB1 peaks to sequence alignment. This revealed that the TGCCGC hexanucleotide was present in 81 of these 91 identified ZFP57/KAP1/SETDB1 peaks, on average at two copies per locus (Figure 7A and Table S1). To confirm the role of this sequence in recruiting the ZFP57/KAP1 complex, we produced in E. coli a fragment of murine ZFP57 comprising the two highly conserved, adjacent C2H2 zinc fingers (Figure S6), fused to MBP or to GST, and tested the ability of this protein to bind in vitro a TGCCGC-containing double-stranded oligonucleotide as previously described (Renda et al., 2007). This ZFP57 derivative could bind the methylated form of this DNA more efficiently than its nonmethylated counterpart. Furthermore, although methylated hexanucleotide-containing sequences could compete for this binding, their effect was abolished by mutations in the motif (Figures 7B and S6D). Supporting a role for the TGCCGC hexanucleotide in imprinting control, it was found in all murine ICRs and also in at least some human ICRs with, for instance, two and three TGCCGC sequences, respectively, in the KvDMR and H19 ICRs from both species (Figure 7C). Discussion The parent-of-origin-specific expression of imprinted genes, which is required for normal embryonic development, remarkably correlates with asymmetric chromatin and DNA methylation signatures at ICRs. The present work demonstrates that, in ES cells, the tethering of ZFP57/KAP1 to the methylated allele of ICRs via the sequence-specific recognition of a hexanucleotidic motif is key to the maintenance of asymmetric histone modifications, heterochromatinization, and DNA methylation at these elements. Therefore, the ZFP57-dependent recruitment of KAP1 at ICRs is pivotal in the maintenance of epigenetic asymmetry in this cellular context, corroborating a recent demonstration that ZFP57 is required for the postfertilization maintenance of maternal and paternal methylation imprints at multiple imprinted domains (Li et al., 2008). Here, a first piece of evidence was obtained by observing that, in conditional Kap1-knockout ES cells, all ICRs largely lost the H3K9me3 chromatin mark and exhibited a rise in histone acetylation consistent with the known association of KAP1 with histone methyltransferases and histone deacetylases (Schultz et al., 2001, 2002). We then found that ZFP57, KAP1, and their associated chromatin modifiers, including SETDB1 and HP1, were recruited specifically to the DNA-methylated allele of ICRs. ZFP57 and KAP1 were bound to all ICRs. KAP1 no longer recruited ICRs in Dnmt triple-knockout cells and, in ZFP57-deleted ES cells, KAP1 no longer bound with ICRs, which were now unmethylated. Correlating this phenomenon, we found that the ZFP57/KAP1 complex associates with DNA methyltransferases and the hemimethylated DNA binding protein NP95. Finally, we identified the methylated TGCCGC motif as a motif recruiting ZFP57 at ICRs and a number of other loci, at least some of which are also ZFP57-dependently methylated in ES cells. On these bases, we propose a model whereby recognition of this motif results in the ZFP57-mediated tethering of a complex comprising KAP1, histone modifiers, and DNA methyltransferases that preserves the chromatin and DNA methylation status of ICRs and other selected loci during the epigenetic instability period that characterizes the first few days of embryogenesis. Imprinted genes were deregulated in kap1−/− ES cells, but these cells exhibit massive transcriptional changes, probably as a consequence of the functional inactivation of the more than 200 different KRAB-ZFPs expressed in this setting (Rowe et al., 2010) (and data not shown). Thus, interpreting the deregulation of imprinted genes in this grossly perturbed context is delicate. In contrast, knocking out zfp57 had subtle consequences, with no gross phenotypic alteration (Akagi et al., 2005) and only a few deregulated transcripts, most of which mapped to known imprinted loci. In addition, the expression changes are those expected by the loss of ICR methylation. Indeed, in zfp57−/− ES cells, the genes (Snrpn, Snurf, Peg13, and Meg3) repressed by DNA methylation are overexpressed, although, conversely, those activated by DNA methylation (Cdkn1c and Phlda2) are downregulated. Even though the generally low basal level of activity of imprinted gene promoters in ES cells may dampen somewhat the consequences of removing ZFP57, these results are consistent with a loss of epigenetic allelic asymmetry of ICRs in the absence of the ICR-binding protein. Intriguingly, ZFP57 is expressed in the central nervous system, and transcriptional analyses of the hippocampus of mice deleted for kap1 in the adult forebrain revealed deregulation of imprinted genes, such as Mkrn3 (Jakobsson et al., 2008). Whether this was an indirect effect, or whether ZFP57 and KAP1 are involved in controlling imprinting, not only in early embryonic and germ cells but also in adult somatic cells warrants investigation. Although ZFP57 associates with the methylated ICRs, it is reciprocally essential for the maintenance of their methylation in ES cells. ES cells are notorious for their epigenetic instability, notably exhibiting many so called bivalent promoters, which harbor both active and inactive chromatin marks. The concomitant expression in ES cells of TET proteins, which most likely partake in active DNA demethylation (Ficz et al., 2011; Ito et al., 2010), and de novo DNA methyltransferases implies that the methylation status of the genome is subjected to dynamic alterations in this setting. Our results demonstrate that a complex encompassing ZFP57 and KAP1, at least at specific sequences, is implicated in this process. This model is corroborated by the observed loss of DNA methylation at all tested ICRs in zfp57−/− ES cells. Importantly, as these cells were obtained by the elimination of ZFP57 from normally methylated ES cells, our finding of unmethylated ICRs can be attributed to a defect in the maintenance of this modification. Involvement of KAP1 in this phenomenon, albeit not directly demonstrated by our work, is supported by the observed loss of Mest promoter methylation in EC cells expressing an HP1-binding mutant of this protein (Riclet et al., 2009) and also by the Zfp809-dependent methylation of murine leukemia virus in embryonic cells (Wolf and Goff, 2009). Therefore, it may seem surprising that we did not observe a significant decrease of ICR DNA methylation following KAP1 deletion in ES cells. However, our RNA-seq analyses indicate that loss of KAP1 results in a marked downregulation of TET1 (data not shown), a major DNA demethylating enzyme (Ito et al., 2010). Furthermore, KAP1 removal rapidly leads to cell-cycle arrest (Figures S1C and S2D), likely depriving cells from the opportunity to lose methyl marks through repeated rounds of DNA replication. Nevertheless, our demonstration that KAP1 forms a complex with DNMTs and NP95, a protein that is required for the maintenance of DNA methylation in ES cells, notably at ICRs (Arita et al., 2008; Avvakumov et al., 2008; Bostick et al., 2007; Meilinger et al., 2009; Sharif et al., 2007), supports the involvement of KAP1 in this phenomenon. Our ChIP analyses revealed the binding of ZFP57, KAP1, and SETDB1 to all tested murine ICRs, always on the methylated allele. In addition, we could detect the three proteins at several tens of additional sites, at least some of which we found to be DNA-methylated in a ZFP57-dependent manner. The great majority of ZFP57-/KAP1-/SETDB1-binding sites contained at least two copies of the TGCCGC hexanucleotide. Confirming the role of this motif in recruiting the multimolecular complex, a recombinant ZFP57 derivative was able to bind in vitro a double-stranded oligonucleotide containing the methylated TGCCGC sequence, but only very weakly its nonmethylated counterpart. Specific recognition of the TGCCmetGC motif was demonstrated by competition with fragments containing methylated WT, unmethylated or point-mutated versions of this sequence. Thus, ZFP57 adds to the list of C2H2 zinc-finger proteins that preferentially bind to methylated DNA in a sequence-specific manner (Sasai et al., 2010). The TGCCGC consensus is found in all murine ICRs and also in at least some of their human homologs. Thus, our data explain not only how these loci are recognized by the ZFP57/KAP1 complex in both species, but also the parent-of-origin specificity of this recognition. Interestingly, the two zinc fingers incorporated in our TGCCmetGC-capturing recombinant protein are highly conserved between mouse and human orthologs of ZFP57, including at residues predicted to dictate nucleotide specificity (Figure S6B). This suggests that each one of these adjacent zinc fingers is responsible for recognizing one of the two triplets that comprise the hexanucleotide motif. It is also noteworthy that, at the H19/Igf2 ICR locus, the ZFP57-binding sequence partly overlaps with the motif recognized by CTCF (Figure S6C). Because this modification blocks CTCF binding, the methylation-dependence of ZFP57 recruitment (Hark et al., 2000) explains how the two factors each interact with a distinct allele of the H19/Igf2 ICR. In addition to ICRs, we found at least two copies of the TGCCGC motif in more than 50 ZFP57-, KAP1-, and SETDB1-binding genomic sites, some of which were verified as DNA methylated in a ZFP57-dependent fashion. This result significantly broadens the range of sites subjected to ZFP57-dependent DNA methylation during early embryogenesis. It will be interesting to explore the functional significance of this phenomenon. How precisely the ZFP57/KAP1 complex prevents demethylation of ICRs and other targeted loci remains undefined. Heterochromatin is often correlated with DNA methylation. For example, SUV39-H1 and -H2 contribute to the maintenance of DNA methylation at pericentric heterochromatin (Lehnertz et al., 2003). At ICRs, repressive H3K9 methylation was previously linked to the presence of DNA methylation (Kacem and Feil, 2009). Our data indicate a role for SETDB1 in this function, consistent with previous findings (Yuan et al., 2009). Similarly, KAP1-recruited SETDB1 is involved in proviral silencing in ES cells (Matsui et al., 2010), an event that culminates in the DNA methylation of these elements. KAP1 recruitment by ZFP809 also induces MLV repression, followed by DNA methylation, in ES cells (Wolf and Goff, 2009), and KAP1 is more broadly essential for the early embryonic control of ERVs, notably IAPs, which during this period are either protected from demethylation or immediately remethylated after losing methylated CpG marks (Lane et al., 2003; Rowe et al., 2010). We previously reported that if the KRAB domain is tethered to the vicinity of a promoter during the first 3–5 days of mouse embryogenesis, it leads to its irreversible silencing by CpG methylation (Wiznerowicz et al., 2007). Whether all these events proceed, or not, along the same general mechanism and what molecular interactions govern the specific contribution of given KRAB-ZFPs, KAP1, chromatin modifiers, and enzymes involved in regulating DNA methylation in each of these situations remain to be determined. Considering that a very high proportion of all KRAB-ZFPs are expressed in ES cells (Rowe et al., 2010), further deciphering their roles and that of KAP1 in the DNA methylation of specific genomic loci should yield important information about this crucial developmental period. Experimental Procedures Cell Culture and RNA-Seq ES cells were maintained in DMEM supplemented with 2-mercaptoethanol, nonessential amino acids, sodium pyruvate, 10% fetal calf serum, and leukemia inhibitory factor (LIF) in gelatinized culture flasks. 4-hydroxy-tamoxifen (4-OHT, Sigma) used at a final concentration of 1μm. Zfp57−/− and WT parental cell lines were obtained from Dr. Yokota (Akagi et al., 2005). Dnmt TKO cells were obtained by Dr. Okano (Tsumura et al., 2006). SF1-G was obtained by crossing C57BL/6 female mice with M. spretus males as previously described (Dean et al., 1998). RNA-seq was performed using standard procedures. RNA was extracted with RNeasy Plus Mini Kit (QIAGEN), hydrolyzed, reverse-transcribed, and then deep-sequenced using Solexa (Illumina). The relative expression was normalized based on GAPDH expression. Chromatin Immunoprecipitation ES cells at 80% confluence were cross-linked with 1% formaldehyde for 10 min at room temperature. Cross-linking was quenched with 125 mM glycine, and whole-cell extracts were prepared for use in the chromatin immunoprecipitations. The fragmented chromatin from 107 cells was used for each reaction. DNA from the immunoprecipitates or from the input (1% of the input for quantification) was analyzed by real-time PCR using Power SBYRGreen PCR Master Mix (Applied Biosystem) performed on an Applied Biosystems 7900HT Real-Time PCR machine. Each reaction was performed in triplicate, and all presented results are representative of experiments performed at least twice. Primer sequences and antibodies are available in the Supplemental Information. ChIP-Seq Analyses Reads were aligned to the Mus musculus genome (assembly NCBI37/mm9) using bowtie (Langmead et al., 2009). Reads with more than four matches were excluded. Normalization of the WT data was performed using MACS (Zhang et al., 2008) and the knockout data for background normalization. Peaks were called using the ChIP-Seq Analysis Server (http://ccg.vital-it.ch/chipseq). The motif-finding program, MDmodule from the MotifRegressor software (Conlon et al., 2003), was used to identify possible consensus DNA motifs. Allele-Specific PCR Analyses The binding of KAP1, SETDB1, HP1y, ZFP57, H3K9me3, and CTCF proteins to the maternal and paternal alleles of ICR was determined by typing for polymorphisms present between the two parental genomes of the SF1-G ES cells, which has a (C57BL/6 × Mus spretus) F1 genotype. ICRs were amplified and radiolabeled (α32PdCTP), then digested with a restriction enzyme that cuts only the paternal or maternal allele. The digested fragments were resolved on a nondenaturing polyacrylamide gel and quantified using PhoshorImager and ImageQuant software by Molecular Dynamics. DNA Methylation Analyses After chromatin immunoprecipitation using KAP1 and ZFP57 antibodies, DNA methylation of Snrpn ICR, was analyzed by bisulfite sequencing. All the immunoprecipitated DNA was treated with sodium bisulfate, using the EpiTect bisulfite kit (QIAGEN), and PCR-amplified. The PCR product was then cloned in Topo pCRII vector (Invitrogen) and the clones were sequenced. The maternal and paternal clones in the SF1-G ES cell line were discriminated using a single nucleotide polymorphism. Methylation in WT and Zfp57−/− ES cells was also investigated using bisulfite sequencing. Protein Immunoprecipitation and Western Blot Half a million cells were pelleted and resuspended on ice in lysis buffer (400 mM NaCl, 10 mM HEPES, pH 7.9, 0.1% NP-40, protease inhibitor, and 10mM N-Ethylmaleimide (Sigma)). Lysate was centrifugated 10 min at 10,000 g and supernatant was incubated with rabbit polyclonal anti-Kap1 antibody (Sripathy et al., 2006), followed by A-protein magnetic beads (Invitrogen), or directly immunoprecipitated with anti-HA Matrix (Roche) or α-FLAG M2 resin, (Sigma). Beads were washed three times with lysis buffer and elution was performed by boiling beads in Laemmli buffer. Western blot was performed with rabbit polyclonal anti-KAP1 antibody, anti-Dnmt1 (Abcam, ab87654), anti-Flag M2 (Sigma), and anti-HA antibody (Roche). Electrophoretic-Mobility Shift Assay A plasmid encoding Glu87-Ala 195 residues of mouse ZFP57 fused to the maltose binding protein (MBP) or to glutathion S-transferase (GST) was expressed in E. coli Bl21 and purified according to the manufacturer's protocol (NEB). Purified proteins (12 pmol) were incubated on ice for 10 min with 2.5 pmol of the specific 32P-labeled duplex oligonucleotide or with the same, but methylated, duplex. Incubation was performed in the presence of 25 mM HEPES pH 7.9, 50 mM KCl, 6.25 mM MgCl2, 1% Nonidet P-40, and 5% glycerol. After incubation, the mixture was loaded on a 5% polyacrylamide gel. Competition was performed in the presence of 50× excess unlabelled oligonucleotides. The NS oligonucleotide, 5′-AGGTTTGACAGTGTCACTTT-3′, was used as a nonspecific competitor.
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A maternal-zygotic effect gene, Zfp57, maintains both maternal and paternal imprints.
Xiajun Li, Mitsuteru Ito, Fen Zhou … (2008)
The mechanisms responsible for maintaining genomic methylation imprints in mouse embryos are not understood. We generated a knockout mouse in the Zfp57 locus encoding a KRAB zinc finger protein. Loss of just the zygotic function of Zfp57 causes partial neonatal lethality, whereas eliminating both the maternal and zygotic functions of Zfp57 results in a highly penetrant embryonic lethality. In oocytes, absence of Zfp57 results in failure to establish maternal methylation imprints at the Snrpn imprinted region. Intriguingly, methylation imprints are reacquired specifically at the maternally derived Snrpn imprinted region when the zygotic Zfp57 is present in embryos. This suggests that there may be DNA methylation-independent memory for genomic imprints. Zfp57 is also required for the postfertilization maintenance of maternal and paternal methylation imprints at multiple imprinted domains. The effects on genomic imprinting are consistent with the maternal-zygotic lethality of Zfp57 mutants.
Author and article information
Journal
Journal ID (nlm-ta): Sci Rep
Journal ID (iso-abbrev): Sci Rep
Title:
Scientific Reports
Publisher:
Nature Publishing Group
ISSN
(Electronic):
2045-2322
Publication date
(Electronic):
20
March
2017
Publication date Collection: 2017
Volume: 7
Electronic Location Identifier: 44667
Affiliations
[1
]Department of Obstetrics and Gynecology, Baylor College of Medicine , Houston, Texas, 77030, USA
Author notes
Article
Publisher Item ID: srep44667DOI: 10.1038/srep44667
PMC ID: 5357799
PubMed ID: 28317850
SO-VID: a6e61275-27c0-453f-9e30-a2f658fedfeb
Copyright © Copyright © 2017, The Author(s)
License:
This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
History
Date
received
: 04
November
2016
Date
accepted
: 13
February
2017
Categories
Subject:
Article
ScienceOpen disciplines: Uncategorized
Data availability:
ScienceOpen disciplines: Uncategorized