Female ageing alters oocyte DNA methylation, gene transcription and H3 lysine methylation. A single-cell study

Erika Yamile Herrera Puerta

Universidad Nacional de Colombia Faculty of Sciences, Biotechnology department. Medellín, Colombia 2018

Female ageing alters oocyte DNA methylation, gene transcription and H3 lysine methylation. A single-cell study

Erika Yamile Herrera Puerta

Thesis presented as a requirement to apply for the title of: PhD in Biotechnology

Director: Ph.D., Gavin Kelsey Co-director: Ph.D., Delmis Omar Camargo-Rodriguez

Biotechnology program Animal Biotechnology research group

Universidad Nacional de Colombia Faculty of Sciences, Biotechnology department Medellín, Colombia 2018

This work has been the work of lots. The work of amazing people, who gave their time and knowledge. What else can a scientist ask for?

Acknowledgements

This work was done with the guidance and academic support of Gavin Kelsey, Courtney Hanna and Hannah Demond, to whom I am sincerely thankful.

I thank all other members of Gavin Kelsey group: Elena Ivanova, Antonio Galvao and Gintare Sendzikaite, who accompanied my learning process throughout my visit in The Babraham Institute. They made my stay really enjoyable and amiable.

Especial thanks to Stephen Clark and Juan Castillo-Fernández who kindly shared their knowledge on single-cell techniques and bioinformatic analysis. I thank to Myriam Hemberger for facilitating ageing mice colonies and offering guidance and academic discussion spaces. Alike I would like to thank Simon Andrews from Babraham Bioinformatics and Hanneke Okkenhaug from Babraham Imaging Facility who kindly shared their time and contributed to the data interpretation.

This research had the economical and administrative support from Gavin Kelsey and the epigenetics programme of The Babraham Institute, and from the department of Science, Technology and innovation – Colciencias, National University of Colombia and CES University.

Abstract and Resumen IX

Abstract

Female reproductive ageing is associated with oocyte quality decline and increased risk of maternofoetal complications during early embryo implantation and pregnancy. DNA methylation is an important epigenetic mark involved in gene regulation that can be affected by environmental factors, such as age and diet. The aim of this study was to assess oocyte methylome and transcriptome in order to identify age-associated changes that may in part explain the aged- associated decline in female fertility. Single oocytes were collected from young and old female mice. Parallel whole-genome bisulfite sequencing and RNA sequencing analysis was performed in individual cells in both groups. Results showed that DNA methylation is predominantly lost in differentially methylated domains. Gene expression of lowly expressed genes were also affected. A positive correlation was identified between gene expression and DNA methylation at gene bodies of specific developmental genes involved in BMP signalling pathways and extracellular matrix function. An immunostaining assay revealed that old oocytes present altered deposition of histone 3 lysine 4 tri-methylation (H3K4me3) and lysine 27 acetylation (H3K27ac), epigenetic modifications that are associated with active transcription. Furthermore, old oocytes presented irregular chromatin configuration and bigger nuclear size than their young counterpart. Overall, this study reveals that female age influences oocyte gene expression and DNA methylation, and moreover, leads to notable nuclear and cellular organizational changes in germinal vesicle oocytes.

Keywords: epigenome, transcriptome, bisulfite-sequencing, histone marks, germ cell, reproductive ageing, infertility

X Female ageing alters oocyte DNA methylation, gene transcription and H3 lysine methylation. A single-cell study

Resumen

El envejecimiento reproductivo femenino se asocia con baja calidad de los ovocitos y complicaciones en la implantación y gestación. La metilación del DNA es una marca epigenética ampliamente estudiada involucrada en la regulación génica y es afectada por factores ambientales como la dieta y la edad. El objetivo de este estudio era evaluar el metiloma y el transcriptoma de ovocitos con el fin de identificar aspectos asociados a la disminución de la fertilidad femenina con la edad. Se colectaron ovocitos individuales de ratonas jóvenes y viejas. Para cada muestra se realizó un estudio de amplificación del genoma completo tratado con bisulfito, en paralelo con la secuenciación del transcriptoma. Los resultados obtenidos mostraron que la metilación del DNA disminuye en amplias regiones con metilación diferencial en los ovocitos viejos, al igual que la expresión de genes específicos del desarrollo también se ve afectada. Se encontró una relación positiva entre la metilación intra-génica y la expresión de genes asociados a la vía de señalización de BMP (proteína morfogénica de hueso, BMP por sus siglas en inglés) y el funcionamiento de la matriz extracelular. En un ensayo de inmunofluorescencia se encontró que los ovocitos viejos tienen alterada la cantidad de lisina 4 tri-metilada y lisina 27 acetilada en la histona 3 (H3K4me3 y H3K27ac respectivamente), ambas marcas asociadas con regiones transcripcionalmente activas. El tamaño del citoplasma y del núcleo son mayores en los ovocitos viejos que en los jóvenes. En conclusión, en este estudio se encontró que la edad femenina afecta significativamente la expresión génica y la metilación de DNA en los ovocitos, y adicionalmente conlleva cambios celulares a nivel estructural.

Palabras clave: epigenoma, transcriptoma, bisulfito, marcas de histonas, célula germinal, envejeciemiento reproductivo, infertilidad

Content XI

Content

Pág. Abstract ...... IX Figures ...... XII Tables ...... XVI 1. Introduction ...... 1 2. Chapter 1. DNA Methylation in Young and old GV oocytes ...... 13 3. Chapter 2. Gene Transcription in Young d Old GV ooctyes ...... 53 4. Chapter 3. Histone 3 chemical modification in Young and Old GV oocytes ...... 89 5. Concluding remarks and future directions ...... 107 6. References ...... 137

Content XII

Figures

Figure 1. Maternal age influences over fertility, oocyte reservoir and pregnancy outcomes. Red squares represent average maternal age increase between 1900’s – 2000´s ______6 Figure 2. Chromatin chemical modification. DNA (yellow) can be methylated at cytosine residues and histone tails can be either methylated or acetylated at specific aminoacidic residues. Four histone proteins ensemble by pairs (H3 – H4 and H2A – H2B) forming an octamer that wraps DNA and controls chromatin accessibility to transcription factors and other proteins. ______8 Figure 3. Oocyte dynamic changes in DNA methylation. Primordial germ cells erase DNA methylation marks during migration, resulting in a nearly unmethylated primary oocyte. DNA methylation is re-established post-natally during oocyte growth. DNAme is again lost after fertilization and reset during lineage differentiation in the post-implantation embryo, except for genomic imprints. ______14 Figure 4. Overview of oocyte PBAT library preparation protocol. Pools of 70-100 oocytes are lysed, and the DNA is bisulfite converted. Random priming and extension are used to amplify and incorporate forward and, subsequently, reverse adaptor sequences. Finally, PCR is used to amplify and index the libraries before they are sequenced. Some of the icons were free-downloaded from https://www.flaticon.com. ______19 Figure 5. Overview of scBS-seq library preparation protocol. Single oocytes are lysed, and the DNA is bisulfite converted. Five rounds of random priming and extension are used to pre-amplify and incorporate forward adaptor sequences and, subsequently, reverse adaptor sequences are incorporated with an additional extension round. Finally, PCR is used to amplify and index the libraries before they are sequenced. ______22 Figure 6. Left panel: Sequencing and Alignment Report taken from Bismark Summary Report. Right panel: cytosine methylation at CpG and non-CpG context (CHH and CHG). mtDNA methylation was calculated counting individual bases using Bisulfite quantitation pipeline throughout the entire MT-chromosome with minimum 1 count per position and at least 1 observation per feature (99.89% of cytosines where converted). ______27 Figure 7. Methylation at HypoDs and HyperDs. Left panel: Methylation calls at HypoDs presented less than 10% DNAme and more than 90% DNAme at HyperDs in the six pooled libraries. Right panel: DNAme at HypoDs and HyperDs in all bulk libraries was comparable to published data (Shirane et al. 2013). ______28 Figure 8. DNAme distribution of CpG islands (CGIs) on the X chromosome. Left panel: Scatter plot showing DNAme of autosomal CGIs (blue dots) and CGIs on X chromosome (red dots) plotted with all PBAT libraries grouped (y-axis) against BS-seq libraries from public data as reference (x-axis). Right panel: Boxplot of DNAme at CGIs on the X chromosome in all bulk libraries. ______28 Figure 9. Correlation matrix calculated for all 6 PBAT libraries using 100 CpG windows. Reference GV libraries were taken from publicly available data generated by Shirane K, et al. 2013. ______29 Figure 10. Left panel: Sequencing and Alignment Report taken from Bismark Summary Report. Right panel: cytosine methylation at CpG and non-CpG context (CHH and CHG). mtDNA methylation was calculated counting individual bases using Bisulfite quantitation pipeline throughout the entire MT-chromosome with Content XIII minimum 1 count per position and at least 1 observation per feature (99.90% of cytosines where converted). ______31 Figure 11. Left panel: Alignment summary of individual cells showing number of reads aligned and CpG methylation calls per cell. -C: Negative controls. Right panel: Bimodal distribution of CpG DNAme of 100 CpG probes in pooled young and old cells. ______32 Figure 12. a) Distribution of random probes (100CpG windows) for all 10 groups of single cell libraries (Pooled scGV) and the 5 replicates of PBAT (bulk) libraries. b) correlation values for each PBAT and pooled single cell libraries. ______33 Figure 13. Principal Component Analysis plot. Principal components were generated using DNAme of 100 CpG probes genome-wide. Here both principal component 1 and 2 together explain 28% of overall variability in both groups. Old oocytes cluster separately from young oocytes in PC1. Each dot represents one grouped sample, comprised of 6-7 randomly pooled cells. ______34 Figure 14. a) Correlation matrix for scPBAT libraries. Cells were polled into 5 groups for young GV (y1-y5) and 5 groups for old GV (o1-o5). b) Vulcano plot for Levene test results on variance homogeneity for young and old GV groups. ______35 Figure 15. Left panel: Scatter plot showing young vs. old GV oocytes for Hyper- and Hypo-methylated domains (HyperD & HypoD). Highlighted probes are filtered by value differences above 10%. Red and blue dots represent probes gaining (N=715) and losing methylation (N=1758) in old GV oocytes, respectively. Right panel: HyperD and HypoD distribution across all groups. ______36 Figure 16. a) Significantly different (P<0.05) probes over HyperD (over 75% DNAme) and HypoD (under 25% DNAme) for young and old GV oocytes. b) Heatmap illustrating the widespread loss in DNAme at both HyperDs and HypoDs. ______37 Figure 17. Significant different methylated probes (p<0.05) filtered by values in young and old GV oocytes. a) probes with values below 25%. b) probes with values between 25-50%. c) probes with values between 50- 75%. d) Probes with values above 75%. t.test was performed to compare mean values (represented as black dots). ______38 Figure 18. Scatter plot representing all intermediate methylated domains (inter-Ds) for young and old group. Red dots: Probes with 10% higher DNAme in old oocytes. Blue dots: Probes with 10% less DNAme in old oocytes. ______39 Figure 19. Summary of Differentially methylated domains (DMDs) DNAme changes. Probes were generated for each feature and were quantified filtering for 10 minimum valid positions. All DM: all probes generated after logistic regression (p<005). DNAme loss: DM filtered for >10% value differences. DNAme gain: DM filtered for >10% value differences ______40 Figure 20. Genomic features overlapping differentially methylated probes at HyperDs, HypoDs and InterDs. All probes are statistically significant between groups (p<0.05). Black and orange areas represent number of differentially methylated HyperDs/HypoDs and InterDs respectively that overlap genomic features. * gDMRs bar was plotted using different axis values so it can be shown within this figure. ______41 Figure 21. Genes overlapping differentially methylated domains (DMDs). a) scatter plot representing all DM (yellow dots), genes gaining DNAme (red dots) and genes losing DNAme (green dots) with a difference above 10% between young and old GV groups. b) distribution of all genes, genes overlapping DMD and genes with DNAme differences above 10%. ______42 Figure 22. Gene ontology analysis for genes overlapping DMD probes. Values represent percentage of enrichment and Fisher's Exact corrected p-value - %(p-value). ______44 Figure 23. left panel: Genomic regions for DNAme Ddx60 and Mid1 genes showing separated groups and consistency. Abbreviations: CDS: Coding domain sequence; CGIs: CpG islands. Right panel: MELISSA clustering test for single-oocytes. Probes used are DMD (N=533) obtained from pooled data. ______45 XIV Female ageing alters oocyte DNA methylation, gene transcription and H3 lysine methylation. A single-cell study Título de la tesis o trabajo de investigación

Figure 24. Dynamic of oocyte DNA transcription activity through oogenesis and oocyte maturation process. ______54 Figure 25. scRNA-seq library preparation overview. PolyA(+) mRNA is captured using magnetic oligo(dT)- biotin beads and reversed transcribed. Subsequently, cDNA is amplified, purified and tagmented (fragmented & tagged) for sequencing. Library quantity and quality is assessed using the Bioanalyzer 2100 (Agilent) prior to sequencing. ______58 Figure 26. Quality control evaluation of scRNA-seq libraries. Left panel: Percentage of reads falling into different genomic features. Right panel: Duplication plots for all samples. ______62 Figure 27. a) Sequence base composition for technical outliers (up) and normal cells (down). b) Read count distribution for all reads (left) and read count per gene feature (right) representing outlier cells. ______63 Figure 28. Gene expression values distribution for young and old oocytes. a) probe values histograms and b) relative distribution of probe trend for young and old oocytes. ______64 Figure 29. Technical confounders. PCA plot showing the relationship between cells regarding possible batch effects (experiment number). PC1 explains 11% of the variation but is also affected by experiment. _____ 65 Figure 30. Correlation matrix including technical confounders and biological groups; young and old (age). 66 Figure 31. Reads over exons quantitation for young and old GV oocytes. Left Panel) All probes (25,965) distribution. Right Panel) Scatterplot representing all probes plotted young vs. old. Quantitation is corrected to gene length. ______67 Figure 32. a) Principal component analysis. Data store is clustered according to high and low rotation values and experiment, both generated for young and old GV oocytes. Clustered values around PC1 are genes with high read count consistencies among all cells. b) Gene expression level across experiments and cell type. Read count differences are significant for all experiments. ______68 Figure 33. Differential expression analysis for detectable expressed genes. a) Scatter plot for all DESeq genes. Red and green dots represent under- and over- expressed genes respectively that are at least 1 unit different between groups. b) Total features. Box plots of number of expressed features by age group. The means of the groups show a significant difference (p = 3.6x10-6) ______69

Figure 34. Differentially expressed genes filtered by position in distribution. Data presented as log2RPKM corrected for gene length. Horizontal black bars represent mean ± SD. ______70 Figure 35. DEGs heatmap. Hierarchical clustering of samples based on the relatedness of gene expression levels of 314 DEGs. Data is presented in Log2 RPKM corrected for gene length obtained with the normalise function from the scatter package in R. DEGs clusters are shown by technical confounders and biological group; young and old (age). ______71 Figure 36. Differentially expressed genes hierarchically clustered for old GV oocytes behaving young-like (yL). Values are normalized log transformed read counts for young and old oocytes. ______72 Figure 37. Top genes changing (fold-change) in young like and old-like gene sets. Young like gene set is referred as genes that behave like young group in old oocytes. Old-like genes are genes that separate young from old oocytes. Prl8a2 presents 8.82 RPKM for young group. ______73 Figure 38. a) Differential dispersion test based on fold-change calculating distance from gene mean in old and young group. b) Volcano plot highlighting DEGs (FDR>2%) showing high variability in young and old GV. ______74 Figure 39. Gene ontology analysis of genes differentially expressed in old compared to young GV oocytes. Results are shown for biological process ontology. ______75 Figure 40. Left panel: Distribution of differentially methylated probes and DEGs. Red dots: all genes overlapping differentially methylated probes. Blue dots: DEGs that overlap differentially methylated regions

(33 genes). Right panel: Mean expression values (Log2 RPKM) for both, all genes and DEGs that overlap differentially methylated regions. Differentially expressed genes are taken from DESeq2 analysis. ______77 Content XV

Figure 41. a) Comparison of gene body DNAme and transcription for genes that identified to be both differentially methylated and expressed between young and old oocytes (N=33). Differences are standardized as: (young-old)/young *100. ______78 Figure 42. Histone marks dynamics during oocyte growth. H3K4me3 accumulates during growth and it goes from being enriched at active promoters to be located also at inactive domains. H3K27ac is enriched at active enhancers/promoters co-existing with H3K4me3. Appears to have low presence at MII oocyte but it gains enrichment at distal enhancers after fertilization. There is no evidence from fully-grown GV state -MII transition. H3K9me3 is associated to heterochromatin in somatic tissue and is maternally inherited by the pre-implantation embryo. There is lack of dynamics information during oogenesis. ______91 Figure 43. a) Distribution of fluorescence intensity for H3K4me3, H3K9me3 and H3K27ac immunostainings in young and old oocytes. b) Imaging for DNA (DAPI – blue), all histones (PanH – green), specific histone marks (anti-H3K3me3, H3k9me3 and H3K27ac – red) and overlay, for young and old oocytes. ______97 Figure 44. a) Cell and nucleus areas for young and old oocytes. b) Chromatin distribution in old oocytes. Blue: DAPI staining for DNA. Green: All histones immunostaining. ______98 Figure 45. Gene expression of histone modifiers. Purple and orange bars indicate genes gaining and losing expression in old oocytes respectively. Statistical significance is highlighted by * ______99

Content XVI

Tables

Table 1. Sequences of primers used in PBAT library preparation: ______21 Table 2. Sequences used for scBS-seq library preparation ______23 Table 3. Alignment and cytosine methylation summary for (bulk) PBAT libraries. Data was taken from Bismark Summary report for all six replicates. ______30 Table 4. Alignment and cytosine methylation summary for scPBAT libraries. Data was taken from Bismark Summary report for individual cell. ______32 Table 5. Oligo sequences for single-cell RNA-seq library preparation ______59 Table 6. Primary antibodies used for histone immunostaining. ______94

Content XVII

Introduction

1.1 Societal, economic and health impacts of delayed motherhood

Delayed motherhood is a phenomenon that is gradually increasing and becoming particularly prevalent in developed countries. Since contraception methods appeared in the 1960´s, women´s decision over maternity has changed reproductive behaviour in several populations all over the world. This has brought implications for the health, vitality and economic welfare of human societies due to adverse consequences elicited by a marked demographic transition (Bhasin et al., 2019). It has been estimated that the time that women are postponing motherhood is directly associated to their educational level (Mills et al., 2011), what logically agrees with family’s desire to have children and simultaneously get adapted to actual professional and socio-economical demands.

Despite general awareness of the end of reproductive life for women, there is still lack of information about the real optimum fertile window and the consequences of delaying motherhood (Mills et al., 2011). Since early 90´s it was pointed out that the optimal fertility for women ends around 35-40 years old (Faddy et al., 1992). This means that female fertility terminates, in many cases, before other systems of the body start declining and usually coincides with periods of career development.

Advanced maternal age leads to increased risks of unwanted pregnancy outcomes, such as preterm birth (<37 weeks of gestation), stillbirths, early neonatal mortality, perinatal mortality, low birthweight (<2500 g), and neonatal intensive care unit (NICU) admission (Carolan, 2013; Laopaiboon et al., 2014).

2 Introduction

Advanced maternal age has also been defined as a major risk for trisomic and monosomic (aneuploid) embryos, that for women nearing the end of their reproductive lifespan, the incidence may exceed 50% of pregnancies (Hassold and Hunt, 2009; Nagaoka et al., 2012). Interestingly, the incidence of non-chromosomal anomalies does not increase in foetuses of pregnant women aged over 35 years in contrast to chromosomal anomalies (Goetzinger et al., 2016; Okmen Ozkan et al., 2019), suggesting that major failures might be related to oocyte meiotic machinery disturbances.

The age-associated mechanisms for these clinical outcomes are not yet completely understood. As is exposed below, evidences suggest that health risks of female reproductive ageing likely involve a combination of factors; including declined quality of the oocyte pool, impaired ovarian and endometrial responsiveness, and hypothalamic-pituitary-gonadal (HPG) axis misregulation. Thus, leading to the idea that the end of reproductive life is strikingly influenced by highly diverse mechanisms.

1.2 Female reproductive ageing in humans

Fertility decline and menopause

Women reproductive ageing occurs during a highly dynamic period determined by cessation of ovarian production of oocytes and significant changes to menstrual cycles. Women reproductive ageing has been classified by the STRAW criteria (Stages of Reproductive Aging Workshop) into three major phases: reproductive, menopausal transition, and postmenopause (Djahanbakhch et al., 2007; Harlow et al., 2012). Reproductive phase goes from menarche to late reproductive stage at late 30´s when menstrual cycles become variable and fecundity begins to decline. Following this, menopause transition starts with gradual increase in cycle length until they reach up to a year of amenorrhea. This exact period is called by definition “Menopause” (Takahashi and Johnson, 2015), and most women enter between the ages of 49 and 52 years. Following postmenopausal period is characterized by a marked oestradiol decrease and follicular stimulation hormone increase that lasts up to 8 years before stabilization.

Introduction 3

Reproductive competence comprises a dynamic and complex postnatal development of pituitary- gonadal axis correlated to ovary and endometrial maturation, which is cyclically changing from the onset at puberty until menopause (Hall, 2014). Consequently, women are born with their full complement of oocytes and during their reproductive years, these oocytes are gradually depleted through ovulation and atresia. Therefore, fecundity declines with advanced maternal age due to a marked reduction in the number of oocytes (from a peak of 6 to 7 million during fetal life to ~1 million at birth; 300,000 at puberty; 25,000 at age 37; and 1,000 at age 51) (Wallace and Kelsey, 2010; Wilkosz et al., 2014), and an increase in the proportion of poor-quality oocytes.

Hypothalamic–pituitary– gonadal axis

In normal cycling women, dynamic changes in the frequency of pulsatile gonadotropin-releasing hormone (GnRH) stimulate the integrated actions of follicle stimulating hormone (FSH) and luteinizing hormone (LH), which are responsible for: (1) follicular development with secretion of oestradiol (E2), inhibin B and inhibin A; (2) the preovulatory LH surge and ovulation; and (3) secretion of progesterone (P4), inhibin A and E2 from the corpus luteum. Furthermore, secretion of E2 and P4 result in proliferative and secretory changes in the endometrium, preparing it for implantation (Hall, 2014).

Upon fertility decline, the decreased numbers of follicles secrete less inhibin B, decreasing the ovarian negative feedback on follicle-stimulating hormone (FSH). The resultant increase in FSH level leads to more follicular recruitment and an accelerated follicular loss, with preservation of E2 levels in early menopausal transition. Eventually, the depletion of follicles results in variability in the ovarian response to FSH, widely fluctuating estrogen levels, and loss of the normal reproductive cycle. When all the ovarian follicles are depleted, the ovary is unable to respond to even high levels of FSH, so estrogen levels decline (Hall, 2014), finally defining the end of reproductive life for women.

1.3 Female reproductive ageing in rodents

4 Introduction

Rodent reproductive senescence proceeds through different alternate pathways to anestrus (terminal acyclicity). First, during peak fecundability, regular 4 – 5 days cycles occur across first 3 – 7 months of life. Next, these are followed by irregular cycles (prolonged cycles >5 days interspersed with 4 – 5 days cycles). After this, constant estrus (CE) period commonly occur. CE is characterized by moderate to high 17�-estradiol levels and moderate P4, LH, and FSH levels as a result of disrupted feedback between the ovaries and hypothalamus/pituitary.

Lastly, the end to anestrus take place, the ovarian equivalent of the human post menopause with extremely low plasma E2 and P4, and elevated gonadotropins. As an alternative pathway, CE may be followed by 10 – 14 days cycles resembling repetitive pseudo-pregnancy (RPP), with growing follicles and corpus luteum (prolonged luteal phases), and intermittent ovulation; or sometimes, a direct transition from irregular cycles to anestrus can also occur (Finch, 2014; Koebele and Bimonte- Nelson, 2016).

An essential difference from women, is that ovarian failure in mice is not accompanied by complete loss of oocyte reservoir. Rodents experience some natural ovarian follicular depletion and are also suspected to have a finite follicle pool, however, at the end of life they still have the presence of potentially mature ovulatory follicles (Gosden et al., 1983; Koebele and Bimonte-Nelson, 2016) and oocytes can still be obtained naturally.

A series of studies suggest a loss of activation of GnRH neurons prior to ovarian follicular failure in middle-aged in rodents, thus indicating neuroendocrine alterations preceding oocyte decline. In female mice, ageing causes a loss of responsiveness of GnRH neurons to regulatory signals (Hou et al., 2002). Consequently, pulsatile GnRH release diminishes and there is attenuation of the GnRH/LH surge, with an overall decrease in the drive from GnRH neurons to the pituitary, and subsequently, to the ovary (Gore et al., 2015; Hou et al., 2002).

Although these findings suggest that reproductive failure in rodents is not limited by the ovary as it is in women, transplantation experiments in rats have shown that the ovary is a key organ involved in rodent reproduction recovery (Banerjee et al., 2014). When ovaries from young donors are transplanted into old females, they can further recover their ovary function (Banerjee et al., 2014; Krohn, 1962). Thus, indicating that when ovaries lose sensitivity to hormonal stimulation, the Introduction 5 pituitary gonadotropic secretion still retain some functionally. Therefore, regardless physiological differences between rodent and human, differences in ovarian and oocyte molecular mechanisms are not that clear. Female reproductive ageing share common features, such as oocyte decline, ovary misfunction and impaired oestrous cycle (Gore et al., 2015), which make mice models valuable for human reproductive studies.

1.4 Oocyte ageing in human and rodent

Primordial germ cells (PGCs) in mice are assigned in the epiblast at embryonic day (E) 7.25 prior their migration to the genital ridge (E9.5–E11.5). Shortly after the migration, there is massive mitotic expansion of germ cell pool, resulting in millions of gamete precursors that commence meiosis. Following in time, between E13.5 and around the time of birth, oocytes become arrested in prophase I at dictyate state, holding homologous chromosomes together as single bivalent units packed in the nucleus, named “germinal vesicle state” (GV). Later, at reproductive period, oocytes are ovulated by groups in each oestral cycle until the end of reproductive life marked by anoestrous (McLaren, 2003; Pepling, 2006; Petronczki et al., 2003).

Human PGCs instead, start to differentiate around the 4 weeks post-fertilization, and after 6 weeks most of the PGCs colonize the genital ridge throughout the migration and proliferation phase where they develop into oogonia (Fujimoto et al., 1977). Between weeks 9-11, oogonium start dividing and enters the initial stage of meiosis (meiosis I) to become the diploid primary oocyte arrested at GV state. This process can last up to 20 weeks post-fertilization. In contrast to rodents, the primary oocytes are maintained for years until puberty. Upon puberty only a few – 15 to 20 – primary oocytes/follicles are recruited during each menstrual cycle, and only one oocyte in the dominant follicle matures and is ovulated (Bendsen et al., 2006; Fulton et al., 2005; Kurilo, 1981).

Non-growing oocytes at GV state are assembled into primordial follicles and they can remain quiescent up to ~24 months in mice or ~5 decades in humans, comprising the total female germ cell pool (Grive and Freiman, 2015). Oocytes meiotically arrested inside ovarian follicles for long periods are exposed to gonadal-pituitary ageing that contributes to ovulatory failure and oocyte quality decline (Djahanbakhch et al., 2007; Tatone et al., 2008). It has been shown that oocytes 6 Introduction coming from advanced-age females present increased incidence of chromosomal aberrations (Battaglia et al., 1996; Wang et al., 2011), deficient mitochondrial activity and increased sensitivity to reactive oxygen species (ROS) (Babayev et al., 2016; Tatone et al., 2008) (Figure 1). In addition to this, aged oocytes have been found to present altered maternal RNA abundance and chromatin configuration, as well as increased DNA double-strand-breaks (Diederich et al., 2012; Goldmann et al., 2018; Hamatani et al., 2004; Liang et al., 2012).

Taken together, female reproductive ageing is accompanied by a series of biological phenomenon that include pituitary hormones changes, ovary function alterations and meiotically arrested oocytes failure. As the oocyte is a key female contributor to next generation endurance, and reproduction at advanced has become frequent, it becomes highly relevant to preform ageing studies on oocyte quality and its capability to sustain life when exposed to female ageing.

Follicle Number Birth Optimal Fertility Decreased Fertility End of Fertility

§ Hypothalamus- § Spindle failure § Irregular cycles gonadal axis active § Mitochondrial § Menopause § High ovary response misfunction § Oocyte depletion § Oocyte recruitment § Oxidative Stress 1.000.000

↑ Maternal and foetal health problems 100.000

10.000

Maternal Age increase through time 1.000 1900`s 2000`s

0 18 28 38 48 51

Maternal Age

Figure 1. Maternal age influences over fertility, oocyte reservoir and pregnancy outcomes. Red squares represent average maternal age increase between 1900’s – 2000´s

1.5 Epigentics and ageing

Introduction 7

Epigenetic refers to what is in addition to changes in genetic sequence. Epigenetic studies have evolved to include any process that alters gene activity without changing the DNA sequence, and lead to modifications that can be transmitted to daughter cells (Weinhold, 2006).

There are three systems that can interact with each other to alter gene expression: DNA methylation (DNAme), histone modifications, and RNA-associated silencing (Egger et al., 2004). DNAme is a chemical process that adds a methyl group to DNA. It is highly specific and preferentially happens in a region in which a cytosine nucleotide is located next to a guanine nucleotide that is linked by a phosphate; this is called a CpG site (Figure 2) (Egger et al., 2004). Adding methyl groups to DNA changes its appearance and structure, modifying gene interaction with the transcription machinery. DNAme is also used in some genes to differentiate allele parental origin, a phenomenon known as “imprinting”, that will be addressed later on next chapter.

Likewise, DNA is arranged into chromatin by wrapping up around histone proteins, which can be modified post-translationally and influence how chromatin is arranged (Figure 2). Chromatin packing level can determine whether the associated chromosomal DNA will be transcribed. Accordingly, open chromatin is active, and the associated DNA can be transcribed. Conversely, if chromatin is condensed (heterochromatin), then it is inactive, and DNA transcription does not occur. Better studied histone modification are methylation and acetylation of specific lysine residues, that are associated with active or inactive gene transcription (Robertson, 2002).

8 Introduction

Histone tail chemical modification

DNA methylation -COCH3 -CH4

-CH4 CpG H3 H4 Transcription factors

H1

H2A H2B

Open chromatin

Figure 2. Chromatin chemical modification. DNA (yellow) can be methylated at cytosine residues and histone tails can be either methylated or acetylated at specific aminoacidic residues. Four histone proteins ensemble by pairs (H3 – H4 and H2A – H2B) forming an octamer that wraps DNA and controls chromatin accessibility to transcription factors and other proteins.

Ageing and epigenetic processes have been linked since DNAme was found to be an accurate predictor of the age of different human tissues (Horvath, 2013), highlighting the influence of environmental factors on gene regulation and health. Epigenetic clock has also been estimated for mice (BI Ageing Clock Team et al., 2017), leading to idea of a general mechanism where DNAme measures the cumulative work done by an epigenetic maintenance system during ageing. However, it is still needed to clarify whether DNAme age is a marker or an effector of aging.

Germ cells differentiation and function are also governed by epigenetic features, such as DNAme and histone modification. Both are highly dynamic throughout oogenesis and early embryo development, defining cell lineage specification (Sasaki and Matsui, 2008). Several studies show that DNAme has an active role during oocyte ageing (Manosalva and González, 2010; Zhang et al., 2011), however, single-base resolution studies are still on the way to giving a more complete panorama of age-associate female germ cell depletion. Therefore, the aim of this study was to evaluate the effect of female ageing on epigenetic features such as DNA methylation and histone modification, and its relation to gene expression.

Introduction 9

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Chapter 1. DNA Methylation in Young and old GV oocytes

2.1 Introduction

DNAme is a well-characterized epigenetic modification that is established through the activities of specific enzymes, DNA methyltransferases (DNMTs), which are capable of adding a methyl group to the fifth carbon atom of the cytosine residues within cytosine-phosphate-guanine (CpG) and non-CpG dinucleotides (Reviewed by Uysal et al., 2015). DNAme can act as repressive epigenetic modification mediating gene silencing through inhibiting the binding of transcriptional machinery and/or mediating the deposition of other repressive epigenetic marks. The genome of somatic cells is highly methylated, except for CpG-rich regions termed CpG islands (CGIs), which are generally unmethylated and overlap regulatory regions, such as gene promoters. DNAme is crucial for mammalian development considering its role in genomic imprinting, X-chromosome inactivation and epigenetic reprogramming (Sasaki and Matsui, 2008); it is likely that cytosine methylation is involved in other mechanisms underlying gametogenesis and embryo development.

The DNA methylome is highly dynamic during gametogenesis and embryogenesis, with two major waves of epigenetic reprogramming. During the first wave, DNAme is globally erased during migration of primordial germ cells (PGCs) towards the genital ridge, followed by de novo establishment of new methylation landscapes after birth in females, during the phase of follicular growth in GV oocytes (Figure 3). During this DNAme accumulation, genomic imprints are set, the vast majority of which originate in the oocyte.

14 Female ageing alters oocyte DNA methylation, gene transcription and lysine methylation. A single-cell study

Figure 3. Oocyte dynamic changes in DNA methylation. Primordial germ cells erase DNA methylation marks during migration, resulting in a nearly unmethylated primary oocyte. DNA methylation is re- established post-natally during oocyte growth. DNAme is again lost after fertilization and reset during lineage differentiation in the post-implantation embryo, except for genomic imprints.

Genomic imprinting is an epigenetic process by which the male and female germline acquire different DNAme marks onto specific genomic regions, termed germline differentially methylated regions (gDMRs). This gamete-derived methylation is maintained allele-specifically after fertilisation, resulting in parent-of-origin specific gene expression, where one allele is active and the other silenced, generating approximately one hundred mono-allelically expressed genes that are essential for healthy development (reviewed by Ferguson-Smith, 2011; Stewart et al., 2016).

The second wave of epigenetic programming occurs after fertilisation and is characterized by global demethylation of the sperm and oocyte DNA in the preimplantation embryo and resetting of DNAme in a canonical pattern during lineage-specification in the post-implantation embryo (Figure 3). Importantly, gDMRs escape this DNAme resetting and keep their methylation state throughout development and adulthood. In humans, loss of differential methylation at imprinted genes leads Chapter 1. DNA methylation in Young and Old GV oocytes 15 to severe imprinting syndromes, e.g. Beckwith-Wiedemann and Angelman syndrome (Horsthemke and Wagstaff, 2008), highlighting the importance of DNAme setting during germ cell development.

Assessing the complete methylome in female gametes and early embryos is limited by the scarce material available. Only recently, whole genome bisulphite sequencing (WGBS) techniques and a combination of several low-input methods have become available thanks to the advent of low- cell/single-cell technologies. These advances have dramatically changed our understanding of what is happening during gamete development and epigenetic reprogramming (Angermueller et al., 2016; Kelsey et al., 2017; Miura et al., 2012), and even more, have allowed the investigation of regulatory relationships between chromatin accessibility, DNA methylation and transcriptome. Together, this technology has made it possible for the first time to investigate the epigenome of naturally scarce cells such as aged oocytes, which are particularly difficult to obtain.

Methylome analysis using WGBS has shown that the oocyte has a unique methylation pattern. Unlike somatic and sperm cells, which are generally hypermethylated except for the CpG islands, DNAme in fully-grown GV oocyte almost exclusively overlaps actively transcribed genes (Veselovska et al., 2015). This results in a bimodal distribution of hypermethylated domains (over 75% of methylation) and hypomethylated domains (less than 25% of methylation) (Kobayashi et al., 2012; Shirane et al., 2013; Smallwood et al., 2011). Interestingly, oocytes also have unusual high methylation outside of the CpG context. It is not clear if this ‘non-CpG’ methylation is functional or simply a consequence of the extensive period during which the de-novo DNMTs are active in oocytes without cell division (Shirane et al., 2013; Tomizawa et al., 2012).

This unique bimodal DNAme distribution of the fully-grown GV oocyte have been extensively described by Veselovska et al., 2015 and co-workers, who defined bioinformatically both hypermethylated domains (HyperD) and hypomethylated domains (HypoD), and found that are discrete large-scale domains composed by CpGs that cluster together and have an average size of 35.9 kbp (median 20.9 kbp) and 59.2 kbp (median 24.9 kbp), respectively. Importantly, they found 16 Female ageing alters oocyte DNA methylation, gene transcription and lysine methylation. A single-cell study that HyperD overlap active transcription units and that transcription is functionally required for DNAme establishment.

Fraga and colleagues where the first to show that DNAme can vary during the ageing process (Fraga et al., 2005). Since then, several studies have observed that specific genome locations can either increase or decrease in methylation level with age in different tissues (Horvath, 2013), most likely due to differences in chromatin and regulative state (McClay et al., 2014; Sun and Yi, 2015). Together with epigenetic clock model, ageing studies for somatic cells and several organs are evolving to more complex molecular mechanisms, where findings on DNAme establishment and maintenance reinforces the importance of environment contribution to gene regulation.

It is not known if similar changes may occur in oocytes through female reproductive ageing, where DNAme patterns need to be stably maintained in GV meiotically-arrested oocytes over decades in the ovary. Investigations for the impact of maternal ageing using bovine model revealed that oocyte DNAme establishment is highly protected for genes critically involved in oocyte and embryo development. Performing limiting-dilution bisulfite (pyro)sequencing, this study showed abnormal DNAme in GV oocytes for genes bDnmt3Lo and bH19 in old caws (8 – 11 years old), but interestingly, mRNA levels did not show significant changes (Mattern et al., 2016). Using fluorescence staining in old female mice, it was shown that DNAme of super ovulated metaphase II (MII) oocytes decreases significantly compared to young, and even more, DNAme and expression levels of DNMTs Dnmt1, Dnmt3a, Dnmt3b and Dnmt3L also decreased significantly with maternal ageing (Yue et al., 2012).

Since a fraction of the oocyte DNA methylome is partially maintained in pre-implantation embryos (Borgel et al., 2010; Smallwood et al., 2011), age-induced changes in DNAme could lead to alterations in the epigenome of the next generation. As previously demonstrated, delayed motherhood increases the risk of several development disruptions; therefore, it is important to analyse ageing quiescent GV oocytes to improve our understanding of the fundamentals of oocyte quality failures and the effects seen in offspring of aged mothers. Chapter 1. DNA methylation in Young and Old GV oocytes 17

As previously shown, aged oocytes are a limited source most of female ageing studies utilise super- ovulated MII oocytes and DNAme fluorescent staining to perform epigenetic studies, and in some cases BS-seq based techniques for individual loci. Therefore, the aim of this study was to evaluate genome-wide DNAme in GV oocytes from female mice aged 12 weeks (young) and ≥44 weeks (old) at single-cell/single-base resolution, and to identify possible DNAme alterations that may contribute to the oocyte quality decline observed during female ageing.

2.2 Methods

2.2.1 Animal handling and oocyte retrieval

C57BL/6Babr mice were used throughout this study. Virgin females were housed in groups up to 5 until they had reached the desired age as indicated. All animal experiments were conducted in full compliance with UK Home Office regulations and with approval of the local animal welfare committee (AWERB) at The Babraham Institute. “Young” females were defined as 12 weeks old and “Old” females were between 44 - 54 weeks old, as previously defined (Woods et al., 2017). Ovaries were collected for both groups and transferred into M2 medium (Sigma-Aldrich) at room temperature. Oocyte retrieval was performed mechanically using needles to release them from the ovarian follicles. Fully grown germinal vesicle (GV) oocytes were collected with glass mouth pipettes and freed from somatic cells by gently pipetting up-and-down and consecutive washes in M2 medium. For bulk samples, Post-Bisulfite Adaptor Tagging (PBAT) libraries were generated with 6 groups of 70-100 clean GV oocytes pooled into UV- treated 200µl PCR tubes in less than 5µl PBS. For individual oocytes, single-cell bisulfite sequencing libraries (scBS-seq) were obtained from clean single GV oocytes collected individually in 2µl RLT Plus Lysis Buffer (Qiagen) in UV-treated 200µl PCR tubes. Samples were collected from 2-3 mice at the time, so libraries were generated with 18 Female ageing alters oocyte DNA methylation, gene transcription and lysine methylation. A single-cell study mixed samples coming from different animals in all groups. All samples were stored at -80oC until library preparation.

2.2.2 Whole-genome Post-Bisulfite Adaptor Tagging (PBAT) libraries for young GV oocytes

Genome-wide DNAme of pooled fully-grown GV oocytes from 12 week old C57BL/6Babr mice was analysed generated using the PBAT method (Miura et al., 2012), as adapted by Hanna CW (Hanna et al., 2018). Bisulfite (BS) converts unmethylated cytosines to uracil, enabling methylated and unmethylated sites to be differentiated in the sequencing libraries as cytosine or thymine, respectively. However, bisulfite also causes DNA breaks leading to loss of material and increasing the requirement of starting material up to millions of cells when standard BS-seq protocols are used (Miura et al., 2012). In the PBAT protocol, this problem is overcome by doing the BS treatment prior to adaptor tagging. In this case BS treatment is used to both convert unmethylated cytosine to uracil and fragment DNA before sample tagging with Illumina sequencing adaptors, so it limits the loss of informative sequences that normally occurs when adaptor-tagged molecules are BS- treated. The PBAT adaptation has enabled DNAme analysis in only 100-200 cells or on a single-cell level (Clark et al., 2017; Smallwood et al., 2011). Next there is an overview of the PBAT protocol used for library preparation (Figure 4):

Chapter 1. DNA methylation in Young and Old GV oocytes 19

Figure 4. Overview of oocyte PBAT library preparation protocol. Pools of 70-100 oocytes are lysed, and the DNA is bisulfite converted. Random priming and extension are used to amplify and incorporate forward and, subsequently, reverse adaptor sequences. Finally, PCR is used to amplify and index the libraries before they are sequenced. Some of the icons were free-downloaded from https://www.flaticon.com.

Six PBAT libraries were generated, each with 70-100 GV oocytes. In brief, cells were lysed in EB buffer (Qiagen) with 0.5% SDS (Sigma) and 4% Proteinase K (Thermo Scientific) for 1 hour at 37oC. Bisulfite conversion was carried out directly on the cell lysate using the one-step modification procedure of the Imprint DNA modification kit (Sigma), incubating as follows: 99oC for 6min, 65oC for 90 min. The resulting DNA was purified on columns using the EZ DNA methylation direct kit (Zymo) according to manufacturer`s instructions.

First strand synthesis was performed using DNA polymerase I Klenow Exo− (New England Biolabs) and a custom streptavidin-conjugated adaptor containing standard Illumina adaptor at the 5ʹ end and 9 base pairs (bp) of random nucleotide stretch at the 3ʹ end (9N) designed so that collectively the oligos should bind to all locations in the genome. (Table 1). This was followed by exonuclease I 20 Female ageing alters oocyte DNA methylation, gene transcription and lysine methylation. A single-cell study treatment (New England Biolabs) to avoid single strand oligos that otherwise will amplify during the second strand synthesis.

DNA purification and precipitation were performed applying two rounds of DNA immobilization. First, solid-phase reversible immobilization (SPRI) with Neutravidin Magnetic Beads (Fisher Scientific) was performed using 1:0.8 ratio to avoid <200bp fragments. SPRI beads reversibly bind DNA in the presence of polyethylene glycol (PEG) and salt (20% PEG, 2.5M NaCl). DNA fragment size affects the total charge per molecule with larger DNAs having larger charges; this promotes their electrostatic interaction with the beads and displaces smaller DNA fragments, so that the ratio of SPRI:DNA defines the length of fragments binding the beads, the lower the ratio, the larger the fragments obtained (DeAngelis et al., 1995). Following this, samples were subjected to a second round of DNA-capture by binding the biotinylated DNA to streptavidin beads (Dynabeads M-280. Life Technologies) to concentrate the samples prior to next amplification round.

Samples were then subjected to second strand synthesis, again using custom primer random sequences (9N). Next, 10 cycles of library amplification were performed, and each sample was tagged in the 5´-end Illumina adapter with a unique barcode, used for sample identification in multiplex sequencing runs. Adapters include platform-specific sequences for fragment recognition by the sequencer enabling library fragments to bind to the flow cells of sequencing platforms (Illumina in this case). This last amplification was performed using Phusion polymerase (Thermo Scientific) followed by a final purification with SPRI beads.

The fragment length, library quality and quantity were analyzed on a High Sensitivity DNA chip on the Agilent Bioanalyzer 2100 according to the manufacturer’s instructions. Fragments suitable for sequencing should be >200bp with an average length of 400–600bp and smooth profiles. All libraries were pooled and quantified with Kapa Library Quantification Kit for Illumina platforms following the manufacturer’s instructions. Average final concentration for all libraries was 4.622nM (size adjusted). Chapter 1. DNA methylation in Young and Old GV oocytes 21

Table 1. Sequences of primers used in PBAT library preparation:

Step Sequence (5´- 3’) First strand-9N-F Btn- CTA CAC GAC GCT CTT CCG ATC TNN NNN NNN N Second strand-9N-R TGC TGA ACC GCT CTT CCG ATC TNN NNN NNN N aPE 1.0 - F P-GAT CGG AAG AGC GGT TCA GAC GGA ATG CCG AG PE 2.0 - R^ ACA CTC TTT CCC TAC ACG ACG CTC TTC CGA TC*T Abbreviations: a - Random primer for whole genome library amplification; * - Phosphorothioate; P – phosphorylated; Btn – Biotin; ^ - Reverse primer is replaced by a unique Illumina iTAG for each library.

2.2.3 Single-cell BS-seq for young and old GV oocytes

Because female mice were not subjected to hormonal hyperstimulation, the amount of quality fully- grown GV oocytes obtained for the old group was on average 6-7 cells per animal. This represents a substantial limitation in trying to use a bulk PBAT library preparation, which requires ≥70 cells. Therefore, scBS-seq was performed as previously described by Clark SJ (Clark et al., 2017). scBS-seq was used in combination with parallel single-cell transcriptome sequencing to measure whole-genome DNAme and transcription within the same single oocyte. This section focuses on the scBS-seq part of the protocol. scBS-seq also implements an approach based on post-bisulfite adaptor tagging (PBAT) due to the limited starting material. Next there is an overview of the single- cell PBAT (scBS-seq) protocol used for individual oocyte libraries preparation (Figure 5):

22 Female ageing alters oocyte DNA methylation, gene transcription and lysine methylation. A single-cell study

Figure 5. Overview of scBS-seq library preparation protocol. Single oocytes are lysed, and the DNA is bisulfite converted. Five rounds of random priming and extension are used to pre-amplify and incorporate forward adaptor sequences and, subsequently, reverse adaptor sequences are incorporated with an additional extension round. Finally, PCR is used to amplify and index the libraries before they are sequenced.

In short, bisulfite conversion was carried out on genomic DNA (gDNA) physically separated from the RNA. Like bulk PBAT, adaptors were incorporated during two rounds of random priming and extension, but sequences containing Illumina adaptor sequences are hexamers (6N-oligo) instead of 9N-oligo (Table 2), this to diminish read trimming after sequencing. Another important modification is that because there is only DNA from one cell in each sample, during the first pre- amplification step, the random priming and extension was repeated a total of five times with intermediate heat denaturation of the DNA fragments and adding new Klenow Exo− polymerase each time (New England Biolabs).

Same exonuclease I treatment was carried out and SPRI purification (1:0.8 ratio) was performed using AMPure XP beads (Agencourt). Importantly, magnetic beads were kept for the rest of the Chapter 1. DNA methylation in Young and Old GV oocytes 23 protocol allowing all following SPRI purification steps to be done using the same beads within the same PCR tubes, so there is no need for DNA biotin-capture and the number of transfer steps are reduced together with the chances of losing material while pipetting. Following this, second strand synthesis was performed, and each sample was then tagged at the 5´end using a unique Illumina adaptor for each oocyte in the final scBS-seq library amplification. Two negative controls (empty wells) were included in the library preparation procedure to exclude the possibility of DNA or RNA contamination.

Finally, libraries were purified and checked for size distribution on an Agilent high-sensitivity DNA chip according to the manufacturer’s instructions. Similarly, fragments suitable for sequencing should be >200bp with an average length of 400–600bp and smooth profiles. For the preparation of the sequencing libraries, samples were pooled in four groups, cleaned (AMPure XP bead cleanup with 1:0.8 ratio) and quantified with Kapa Library Quantification Kit for Illumina platforms following the manufacturer’s instructions.

Table 2. Sequences used for scBS-seq library preparation

Step Sequence (5’ – 3’) Pre-amp oligo (first strand CTACACGACGCTCTTCCGATCTNNNNNN synthesis) Adapter 2 oligo (second strand TGCTGAACCGCTCTTCCGATCTNNNNNN synthesis) a PE 1.0 – f (library amplification) AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACA CGACGCTCTTCCGATC*T ipcr tag CAAGCAGAAGACGGCATACGAGATXXXXXXXXGAGATCGG TCTCGGCATTCCTGCTGAACCGCTCTT CCGATC*T Abbreviations: a - Random primer for whole genome library amplification; * - Phosphorothioate; XXXXXXXX - eight-base index added to each sample during final library amplification.

24 Female ageing alters oocyte DNA methylation, gene transcription and lysine methylation. A single-cell study

2.2.4 Sequencing and Mapping

Paired-end 125 bp sequencing of both bulk and scBS-seq libraries was performed on an Illumina HiSeq2500 platform. scBS-seq libraries were divided in four groups of ~20 libraries (cells) and run in four lanes, while the 6 bulk PBAT libraries were pooled and run in one lane.

For bulk and scBS-seq libraries, raw sequence reads were trimmed to remove the first 9 (or 6) base pairs (the N random priming portion of the reads) and poor-quality calls, using Trim Galore (v0.3.8, parameters: --clip_r1 9 (or 6) --clip_r2 9 (or 6); https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/). Trimmed reads were aligned to the mouse genome (GRCm38 assembly) using the bisulfite converted sequences mapping tool Bismark (v0.13.1, parameters: --bowtie2 --non-directional) (Krueger and Andrews, 2011).

Reads were then deduplicated with $deduplicate_bismark selecting a random alignment for positions that were covered more than once. CG methylation calls were extracted from the deduplicated mapping output ignoring the first 4bp to reduce the methylation bias typically observed in PBAT libraries using the Bismark methylation extractor with the following parameters: a) paired-end mode: --no_overlap --report --ignore 4 --ignore_r2 4; b) single-end mode: --report -- ignore 4.

For scBS-seq libraries, Bismark may be run with default parameters, but it should be noted that alignments were carried out in single-end and non-directional mode. Single-end mode is used also for paired-end libraries because PBAT-type libraries often generate hybrid fragments that cannot be aligned as valid paired-end alignments. The non-directional mode is required because the five rounds of pre-amplification not only generate the PBAT strands (complementary to original top (CTOT) and complementary to original bottom Chapter 1. DNA methylation in Young and Old GV oocytes 25

(CTOB), but also start to anneal to previously extended products, thereby giving rise to reads aligning to the original top (OT) and original bottom (OB) strands. A typical strand ratio observed for single-cell libraries would roughly be 2:3:3:2 (OT:CTOT:CTOB:OB), so the single-cell bisulfite libraries are non-directional. Alignments were also deduplicated using the command $deduplicate_bismark –bam and methylation calls were extracted using default parameters.

2.2.5 Data Analysis

Methylation analysis was performed in SeqMonk Mapped Sequence Data Analyser version 1.45.0 (https://www.bioinformatics.babraham.ac.uk/projects/seqmonk/), which enables the visualisation and analysis of mapped sequence data linked to R software environment for statistical analysis (https://www.r-project.org/). The percentage of methylation was calculated using individual methylation calls (each read is only 1 base long) and quantitation was performed using SeqMonk Bisulfite Quantitation Pipeline that provides a way to filter each call position by the degree of coverage and then produces an overall methylation value which weights each valid call position equally.

Three types of probes were used in this analysis; probes previously designed by Veselovska et al. 2015 to quantify HyperDs and HypoDs, probes for intermediately methylated domains, and random probes over fixed windows of 100 CpG positions to measure genome-wide changes. Each set of probes were used when required as indicated below. Unbiased quantitation was performed with a minimum count of 1 call per position, and at least 10 observations per feature.

For scBS-seq libraries, even though each single GV oocyte has 4 sets of chromosomes, the coverage is still low for a differential methylation comparison using SeqMonk filters. Therefore, samples were grouped into 2 replicate sets: Young and Old, and each replicate set had 5 grouped samples 26 Female ageing alters oocyte DNA methylation, gene transcription and lysine methylation. A single-cell study containing 6-7 cells randomly pooled. Statistical differences were calculated applying a logistic regression to contrast Young and Old replicate sets, which allows the inclusion of variability as a factor in the statistics. Significance was defined with an �-level below 0.05 and multiple testing correction Benjamini-Hochberg was applied to avoid erroneous significance increase (Benjamini and Hochberg, 1995).

Annotated probe reports were generated as annotation tracks to compare distribution and consistency between single-cell and bulk samples using the same quantitation pipelines and statistical tests. Gene ontology (GO) analysis was performed using mouse genome informatics (MGI) functional annotation project, developed by The Jackson Laboratory (http://www.informatics.jax.org/function.shtml) to obtain biological functions and processes in which affected genes are involved.

2.3 Results

2.3.1 Bulk BS-seq libraries quality control

The total number of reads obtained was in average 29,521,664 ± 12,622,213 (mean ± SD) per library, with 11,733,737 ± 4,490,199 (mean ± SD) unique aligned reads. Average duplication rate was 8% and mapping efficiency was 48% (Figure 6. Left panel). Conversion rate was highly efficient according to mitochondrial DNA methylation (0.1%) (Figure 6. Right panel).

Chapter 1. DNA methylation in Young and Old GV oocytes 27

Sequencing and Alignment Report Methylation Context

MT CHH 0.1% 29,521,664.0 7.82%

CpH 7.06% 14,266,264.5 11,733,737.3

2,532,527.2

CpGs 41.36% Total Reads Aligned Reads Unique Reads Duplicate Reads (remaining) (removed)

Figure 6. Left panel: Sequencing and Alignment Report taken from Bismark Summary Report. Right panel: cytosine methylation at CpG and non-CpG context (CHH and CHG). mtDNA methylation was calculated counting individual bases using Bisulfite quantitation pipeline throughout the entire MT-chromosome with minimum 1 count per position and at least 1 observation per feature (99.89% of cytosines where converted).

As previously said, fully grown GV oocytes present a bimodal distribution with large regions being exclusively highly or lowly methylated, while somatic cells present a more even distribution of DNA methylation across the whole genome. Therefore, to evaluate possible somatic DNA contamination, samples were checked for the distribution of the DNAme in addition to the methylation percentage in CpG context, both at whole genome level and X-chromosome specifically. In fully grown GV oocytes, CpG methylation across whole genome is expected to be close to 40% while methylation in the X-chromosome should be less than 10% (Shirane et al., 2013). Methylation at CpG context for all samples was in average 41% genome-wide and 0.1% for the X- chromosome (Figure 8. Right panel). Quantitation over HyperD and HypoD probes showed that all six libraries presented the characteristic DNA methylation distribution of the full grown GV oocyte without any signs of somatic DNA contamination (Figures 7 and 8).

28 Female ageing alters oocyte DNA methylation, gene transcription and lysine methylation. A single-cell study

Figure 7. Methylation at HypoDs and HyperDs. Left panel: Methylation calls at HypoDs presented less than 10% DNAme and more than 90% DNAme at HyperDs in the six pooled libraries. Right panel: DNAme at HypoDs and HyperDs in all bulk libraries was comparable to published data (Shirane et al. 2013).

Figure 8. DNAme distribution of CpG islands (CGIs) on the X chromosome. Left panel: Scatter plot showing DNAme of autosomal CGIs (blue dots) and CGIs on X chromosome (red dots) plotted with all PBAT libraries grouped (y-axis) against BS-seq libraries from public data as reference (x-axis). Right panel: Boxplot of DNAme at CGIs on the X chromosome in all bulk libraries.

Chapter 1. DNA methylation in Young and Old GV oocytes 29

With the aim to corroborate the similarity and consistency between all 6 PBAT libraries, a correlation matrix was calculated using same 100CpG windows across all genome (Figure 9). Data was compared against reference GV oocyte libraries (Shirane K, et al., 2013). It came out that replicate 1 appear to have the minor correlation with the rest of the replicates, as well as with reference data. Bismark summary report of alignment showed that replicate 1 also presented the lowest read content and cytosine coverage compared to the rest of PBAT libraries (Table 3). Therefore replicate 1 was excluded for further analysis.

Correlation matrix for PBAT libraries

Replicate 1 2 3 4 5 6 Ref

1 0.98 2 0.975

3 0.97

4 0.965

5 0.96

6 0.955

Ref 0.95

Figure 9. Correlation matrix calculated for all 6 PBAT libraries using 100 CpG windows. Reference GV libraries were taken from publicly available data generated by Shirane K, et al. 2013.

Alignment and cytosine methylation summary for young PBAT libraries is presented below (Table 3).

30 Female ageing alters oocyte DNA methylation, gene transcription and lysine methylation. A single-cell study

Table 3. Alignment and cytosine methylation summary for (bulk) PBAT libraries. Data was taken from Bismark Summary report for all six replicates.

(bulk) PBAT Alignment Summary Rep 1 Rep 2 Rep 3 Rep 4 Rep 5 Rep 6 Average Total Reads 11,618,050 40,716,665 46,631,164 28,134,598 27,794,188 22,235,319 29,521,664 Total Cs covered 195,242,863 575,283,497 679,419,655 425,219,858 440,340,070 360,110,718 445,936,110 Aligned Reads 5,748,838 18,587,798 22,642,757 13,803,872 13,840,946 10,973,376 14,266,265 Unaligned Reads 5,207,758 20,043,142 21,471,412 12,709,868 12,436,975 10,077,484 13,657,773 Ambiguously Aligned Reads 661,427 2,085,651 2,516,883 1,620,803 1,516,211 1,184,411 1,597,564 Duplicate Reads (removed) 710,935 3,040,479 4,906,996 2,729,404 2,351,073 1,456,276 2,532,527 Unique Reads (remaining) 5,037,903 15,547,319 17,735,761 11,074,468 11,489,873 9,517,100 11,733,737 %Duplication 12% 16% 22% 20% 17% 13% 17% Methylation Summary Methylated CpGs 4,336,845 12,291,734 14,890,117 10,051,612 9,964,097 8,071,785 9,934,365 Unmethylated CpGs 6,448,523 18,262,192 21,141,697 13,353,523 13,949,634 11,246,719 14,067,048 Methylated CpHs 3,337,892 8,811,295 11,411,466 7,862,008 6,841,555 5,693,261 7,326,246 Unmethylated CpHs 42,033,996 125,675,953 147,225,821 92,146,669 96,726,087 78,259,280 97,011,301 Methylated CHHs 11,435,954 29,498,712 37,755,309 26,816,079 22,999,921 19,183,901 24,614,979 Unmethylated CHHs 127,649,653 380,743,611 446,995,245 274,989,967 289,858,776 237,655,772 292,982,171 %Methylation CpGs 40% 40% 41% 43% 42% 42% 41% %Metylation CpHs 7% 7% 7% 8% 7% 7% 7% %Methylation CHHs 8% 7% 8% 9% 7% 7% 8%

2.3.2 Single-cell BS libraries quality control

A total of 80 scBS-seq libraries were generated. In concordance with above shown quality control for bulk libraries, cells with over 41% CpG methylation where exclude from the analysis. Additionally, as previously reported quality control for scDNA-seq libraries, the following exclusion criteria for scBS-seq libraries were employed: 1) samples with low mapping efficiency (alignment rates less than 7%) and 2) poor bisulfite conversion <95% (based on Bismark CHH and CHG methylation estimate) (Angermueller et al., 2016).

After the quality control check of the scBS-seq libraries, 29 young oocytes and 35 old oocytes were included in the analysis. On average 7,997,660 ± 4,049,239 (mean ± SD) total reads were obtained per oocyte with an average mapping efficiency of 39.8% (Figure 10. Left panel). Bisulfite conversion was over 99% according to mtDNA methylation (Figure 10. Right panel). After deduplication, a total of 1,962,445 ± 858,754 (mean ± SD) uniquely aligned reads were obtained and used in the analysis Chapter 1. DNA methylation in Young and Old GV oocytes 31

(Table 4). Total reads from negative controls had less than 1 % mapping efficiency (% raw sequencing reads aligned) (Figure 11. Left panel).

Sequencing and Alignment Report Methylation Context

7,997,660

MT , 0.1% CHH , 5.1%

CpH , 4.6% 3,363,367

1,975,537 1,387,830

CpG , 30.0% Total Reads Aligned Reads Unique Reads Duplicate Reads (remaining) (removed)

Figure 10. Left panel: Sequencing and Alignment Report taken from Bismark Summary Report. Right panel: cytosine methylation at CpG and non-CpG context (CHH and CHG). mtDNA methylation was calculated counting individual bases using Bisulfite quantitation pipeline throughout the entire MT-chromosome with minimum 1 count per position and at least 1 observation per feature (99.90% of cytosines where converted).

32 Female ageing alters oocyte DNA methylation, gene transcription and lysine methylation. A single-cell study

100 Unmethylated CpG 80 Methylated CpG

60

# Reads 40

20 Cytosine methylation Cytosine 0

8M Not Aligned Aligned Ambiguously Duplicate alignments Deduplicated Unique Alignments

6M

4M % Calls

Alignment 2M

0M -C -C Samples

Figure 11. Left panel: Alignment summary of individual cells showing number of reads aligned and CpG methylation calls per cell. -C: Negative controls. Right panel: Bimodal distribution of CpG DNAme of 100 CpG probes in pooled young and old cells.

Next, there is summary for CpG methylation and alignment results (Table 4)

Table 4. Alignment and cytosine methylation summary for scPBAT libraries. Data was taken from Bismark Summary report for individual cell.

scPBAT alignment Summary All cells young old Average Total Reads 7,901,597 8,834,104 7,161,216 7,997,660 Total Cs covered 48,310,068 53,181,524 53,181,524 53,181,524 Aligned Reads 3,323,978 3,798,207 2,928,526 3,363,367 Unaligned Reads 3,919,426 4,322,458 3,675,130 3,998,794 Ambiguously Aligned Reads 627,912 713,422 557,548 635,485 Duplicate Reads (removed) 1,295,635 1,637,004 1,138,656 1,387,830 Unique Reads (remaining) 1,962,445 2,161,203 1,789,871 1,975,537 %Duplication 39% 41% 37% Methylation Summary Methylated CpGs 981,873 1,081,601 886,539 984,070 Unmethylated CpGs 2,294,571 2,500,434 2,123,514 2,311,974 Methylated CpHs 552,701 587,999 524,380 556,189 Unmethylated CpHs 11,634,240 12,805,801 10,609,502 11,707,652 Methylated CHHs 1,635,765 1,764,244 1,532,555 1,648,400 Unmethylated CHHs 31,210,918 34,441,445 28,386,987 31,414,216 %Methylation CpGs 30% 30% 30% %Metylation CpHs 5% 4% 5% %Methylation CHHs 5% 5% 5% Chapter 1. DNA methylation in Young and Old GV oocytes 33

It is known that the scBS-seq data does have some skew towards unmethylated CpG features, probably more so with the less deeply sequenced libraries (Clark et al., 2017), but this skew tends to disappear when the datasets are merged. Accordingly, single cell data was compared to PBAT samples as a validation of the scPBAT libraries quality. Same 100 CpG windows were used for all 10 groups of single cell libraries and the 5 replicates of PBAT libraries. Methylation distribution shows a high correlation and similar distribution of probes for young group (Figure 12). Thus, indicating that pooled single GV oocytes resemble bulk PBAT and are comparable as they are to reference data generated by Shirane K (Figure 12)

a) Pooled b) scGV

0.98

PBAT (bulk) 0.96 Pooled scGV PBAT vs pooled scGV Correlation DNA methylation (%) methylation DNA

0.94

R=0.991 PBAT Pooled PBAT vs pooled scGV scGV PBAT (bulk)

Figure 12. a) Distribution of random probes (100CpG windows) for all 10 groups of single cell libraries (Pooled scGV) and the 5 replicates of PBAT (bulk) libraries. b) correlation values for each PBAT and pooled single cell libraries.

2.3.3 Methylation comparison in old and young GV oocytes

34 Female ageing alters oocyte DNA methylation, gene transcription and lysine methylation. A single-cell study

2.3.4 Genome-wide methylation patterns

Using 100 CpG windows, a total of 350,848 probes were generated and they show the typical bimodal distribution pattern of oocytes (Figure 11. Right panel). Therefore, in order to look at the relatedness of the samples within the biological groups (young and old), samples were clustered according to their similarity of different subsets of probes in all cells performing a principal Component Analysis (PCA). A set of principal components was generated based on a set of rotation values or transformations that are applied to each probe after methylation quantification.

For this purpose, cells were pooled into 10 groups; 5 groups for young GV (6-7 cells each) and 5 groups for old GV (6-7 cells each). Components 1 and 2 together explain 28% of the overall variability observed in the data, and clearly shows that young oocytes cluster separately from old oocytes within PC1 (Figure 13), indicating that there are differences in methylation distribution between groups.

Figure 13. Principal Component Analysis plot. Principal components were generated using DNAme of 100 CpG probes genome-wide. Here both principal component 1 and 2 together explain 28% of overall variability in both groups. Old oocytes cluster separately from young oocytes in PC1. Each dot represents one grouped sample, comprised of 6-7 randomly pooled cells.

Chapter 1. DNA methylation in Young and Old GV oocytes 35

Old group also seem to be more consistent. In order to check for relatedness within groups, a correlation matrix was also calculated for all groups. This corroborates that old cells tend to behave more homogenously (Figure 14a). Based on this, Wilcoxon signed rank test with continuity correction was performed for a variation comparison. Results indeed verified that young group present more variable behaviour (V = 1.318e+09, p-value <2.2e-16). Therefore, Leaven test was performed for variance homogeneity comparison. This test identified 264 variable regions that resulted to have higher coverage and more variability for young GV (Figure 14b).

a) b) Group y1 y2 y3 y4 y5 o1 o2 o3 o4 o5 old young

y1 0.970 y2 0.967 6 y3 0.966 y4 value 0.964 - 4 y5 0.962 log10 p log10 o1 - 0.960 2 o2 0.958 o3 0.956 o4 0 -4 -2 0 2 4 o5 0.954 Log10 FC

Figure 14. a) Correlation matrix for scPBAT libraries. Cells were polled into 5 groups for young GV (y1-y5) and 5 groups for old GV (o1-o5). b) Vulcano plot for Levene test results on variance homogeneity for young and old GV groups.

2.3.5 Methylation in hypermethylated (HyperDs) and hypomethylated (HypoDs) domains

Probes across all HyperDs and HypoDs were used and quantitation was performed using the bisulfite methylation pipeline with a requisite of 10 informative CpG positions per probe. A total of 89,724 probes were generated, and as general view, probes were filtered by value differences above 10% (Figure 15). This first exploration revealed that almost double of generated probes lose methylation (N = 1,758) in old group compared to young (N=715). 36 Female ageing alters oocyte DNA methylation, gene transcription and lysine methylation. A single-cell study

Figure 15. Left panel: Scatter plot showing young vs. old GV oocytes for Hyper- and Hypo-methylated domains (HyperD & HypoD). Highlighted probes are filtered by value differences above 10%. Red and blue dots represent probes gaining (N=715) and losing methylation (N=1758) in old GV oocytes, respectively. Right panel: HyperD and HypoD distribution across all groups.

After logistic regression a total of 21,154 probes (23.57%) showed significant difference (minimum 10 observations per feature, corrected p<0.05). Differentially methylated probes were then filtered for differences between groups above 10%, revealing that there is a consistent loss of DNAme for both HyperDs and HypoDs, with 1,394 (6.58%) differentially methylated probes losing DNAme compared to 517 probes (2.44%) gaining DNAme in old group (Figure 16).

Chapter 1. DNA methylation in Young and Old GV oocytes 37

Figure 16. a) Significantly different (P<0.05) probes over HyperD (over 75% DNAme) and HypoD (under 25% DNAme) for young and old GV oocytes. b) Heatmap illustrating the widespread loss in DNAme at both HyperDs and HypoDs.

To further evaluate whether a patterning of methylation was susceptible to DNAme loss in old oocytes, significantly differentially methylated probes were divided into unmethylated (below 25%), intermediately methylated (25-50% and 50-75%), and methylated (above 75%) subsets. This strikingly shows that old GV oocytes appear to particularly lose DNAme in regions with intermediate cytosine methylation, between 25 and ~75% (Figure 17).

38 Female ageing alters oocyte DNA methylation, gene transcription and lysine methylation. A single-cell study

Figure 17. Significant different methylated probes (p<0.05) filtered by values in young and old GV oocytes. a) probes with values below 25%. b) probes with values between 25-50%. c) probes with values between 50- 75%. d) Probes with values above 75%. t.test was performed to compare mean values (represented as black dots).

2.3.6 Methylation at intermediate methylated domains

Importantly, HyperDs and HypoDs defined in GV oocytes do not encapsulate the vast majority of intermediately methylated regions. Therefore, to truly evaluate the extent of DNAme loss in old GV oocytes, probes were then generated across all interDs, exactly overlapping the gaps between HyperDs and HypoDs. A total of 88,103 probes were generated. Logistic regression gave a total of 8,497 probes (9.64%) that were significantly different, of which 2,286 (26.9%) were hypomethylated and 1,367 (16%) were hypermethylated in old GV oocytes in comparison to young (>10% difference). Results confirm the methylation loss at intermediate methylated domains in old GV oocytes (Figure 18).

Chapter 1. DNA methylation in Young and Old GV oocytes 39

Figure 18. Scatter plot representing all intermediate methylated domains (inter-Ds) for young and old group. Red dots: Probes with 10% higher DNAme in old oocytes. Blue dots: Probes with 10% less DNAme in old oocytes.

In summary, for random 100CpG windows, old GV oocytes presented a marked DNAme decrease across hyper-, hypo- and inter-domains; loosing methylation differentially in a total of 3,680 probes. For HyperDs and HypoDs, a total of 1,394 probes were significantly demethylated, whereas for interDs 2,286 probes lose significant DNAme (p<0.05). Interestingly, even though InterDs present the biggest loss of DNAme, they also present a substantial proportion of DNAme gain in contrast to the HyperDs and HypoDs (16.08% vs 2.44%). This may indicate that DNAme at interDs is less stable during GV oocyte long periods of quiescence. Together these findings suggest that the starting methylation level (high or low) is not predictive of the loss or gain of methylation in old oocytes, therefore something else must be influencing this age-related effect.

To generate a more specific quantitation for each domain, probes were generated exactly matching HyperDs (>75%CpGme), HypoDs (<25%CpGme) and InterDs, and they quantitated using bisulfite 40 Female ageing alters oocyte DNA methylation, gene transcription and lysine methylation. A single-cell study quantitation pipeline with minimum 10 valid positions. Results showed that HypoDs present the biggest difference with more than 30% of DNAme change (Figure 19), however, all domains seem to be affected similarly when values were filtered over 10% differences, thus indicating that DMDs DNAme is highly consistent.

35.9% 26.1%

All DM (p<0.05) 9.6% DNAme loss DNAme gain 2.6% 2.3% 1.9% 1.6% 0.8% 0.7%

HyperDs HypoDs InterDs

Domain Total probes DM (p<0.05) DNAme gain DNAme loss HyperDs 27,761 7,233 (26.1%) 224 (0.8%) 644(2.3%) HypoDs 38,699 1,3895 (35.9%) 269 (0.7%) 745 (1.9%) InterDs 88,103 8,497 (9.6%) 1367 (1.6%) 2,286 (2.6%)

Figure 19. Summary of Differentially methylated domains (DMDs) DNAme changes. Probes were generated for each feature and were quantified filtering for 10 minimum valid positions. All DM: all probes generated after logistic regression (p<005). DNAme loss: DM filtered for >10% value differences. DNAme gain: DM filtered for >10% value differences

2.3.7 Genomic distribution of probes differentially methylated in old GV oocytes

Chapter 1. DNA methylation in Young and Old GV oocytes 41

Using differentially methylated probes obtained previously over HyperDs, HypoDs and InterDs, the number of probes that fall into different genomic regions features were calculated. Features included CGIs (Illingworth et al.), promoters (defined as -1000/+ 500bp of transcription start sites), maternal germinal imprinted DMRs (gDMRs), intergenic regions and gene bodies (Figure 20).

HyperDs/ HyperD&HypoD InterDs Feature All probes InterDs Total (%) HypoDs (22,261) (21,740) Genes 32,025 17,873 3,867 21,740 (19,119) body (67.88) Intergenic 25,047 15,011 4,108 19,119 (13,890) (76.33) Promoter 32,025 18,212 4,049 13,890 (42.37) CGI 23,017 13,036 854 13,890 (16.90) (30) gDMR 30 23 7 30 gene body intergenic promoter CGI gDMR* (100) Genomic features

Figure 20. Genomic features overlapping differentially methylated probes at HyperDs, HypoDs and InterDs. All probes are statistically significant between groups (p<0.05). Black and orange areas represent number of differentially methylated HyperDs/HypoDs and InterDs respectively that overlap genomic features. * gDMRs bar was plotted using different axis values so it can be shown within this figure.

Intergenic region contained the biggest proportion of differentially methylated probes, probably due to is major extension. However, is to highlight that more than half of gene bodies overlap at some context to differentially methylated probes and all maternal imprinted DMRs (figure 20). Thus, suggesting possible relevant biological meaning of age-associated DNAme changes in old GV oocytes.

2.3.8 Gene body methylation and gDMR in old and young GV oocytes

42 Female ageing alters oocyte DNA methylation, gene transcription and lysine methylation. A single-cell study

With the aim to identify genes that are affected at DNAme level in the oocytes during meiotic arrest through ovary ageing process, same probe list generated for genes overlapping differentially methylated probes at HyperDs, HypoDs and InterDs was used. A total of 19,427 were identified as unique probes for all differentially methylated domains (DMDs), from which 533 (2.74%) probes presented more than 10% difference between groups (Figure 21). This shows that differences at DNAme level in gene bodies is highly consistent, suggesting that even though genes are significantly affected by age-associated DNAme changes, the vast majority does preserves its DNAme state.

Figure 21. Genes overlapping differentially methylated domains (DMDs). a) scatter plot representing all DM (yellow dots), genes gaining DNAme (red dots) and genes losing DNAme (green dots) with a difference above 10% between young and old GV groups. b) distribution of all genes, genes overlapping DMD and genes with DNAme differences above 10%.

According to value differences, 333 genes were found to be hypomethylated and 170 genes were hypermethylated in old compared to young GV oocytes. Top genes losing DNAme in old GV include prostaglandin F receptor (Ptgfr), ATP synthase, H+ transporting (Atp5a1), adhesion G protein- coupled receptor V1 (Gpr98), DEAD (Asp-Glu-Ala-Asp) box polypeptide 60 (Ddx60), Dynein axonemal heavy chain (DNAhc7c), Zinc finger protein 846 (Zfp846), Neuronal tyrosine- phosphorylated phosphoinositide 3-kinase adaptor 2 (Nyap2), forkhead box P2 (Foxp2) (Figure 19), Chapter 1. DNA methylation in Young and Old GV oocytes 43 among others. Meanwhile, genes gaining DNAme in old GV include developmental pluripotency- associated 3 (Dppa3), Transcription factor 19 (Tcf19), X-linked lymphocyte-regulated 4B (Xlr4b), among others.

Among maternal imprinted DMRs, Gnas locus presented the biggest difference losing only 3% of DNAme in old group. Suggesting that gDMRs alterations are not the major feature driving the wide DNA demethylation observed in old group.

Genes with differential methylation were classified by the GO term finder into 7 groups according to the biological process they interact with; the main groups of genes were associated to developmental process (25%) and RNA metabolism (36%), followed by genes associated to stress response (Figure 22). However, most significative process (Fisher´s Exact corrected p-value <0.05) are DNA metabolism, cell-cell signalling and cell adhesion functions (Figure 22).

Some known genes that are important for early development include Runt related transcription factor 2 (Runx2), Frizzled class receptor 5 (Fzd5) and Guanine nucleotide binding protein, alpha q polypeptide (Gnaq). Surprisingly, genes with the biggest loss of DNAme are involved in early neuronal development, such as Membrane associated guanylate kinase (Magi2, 55% loss), Neuronal phosphoinositide 3-kinase adaptor 2 (Nyap2, 54% loss) and Serotonin Receptor 4 (Htr4, 50% loss) and midline 1 (Mid1 40% loss) (figure 23).

44 Female ageing alters oocyte DNA methylation, gene transcription and lysine methylation. A single-cell study

Cellular Component Biological Process

extracellular DNA metabolism matrix developmental cell-cell nucleus 7% (0.01) processes signaling

20% (0.008) 8% (0.01) cell adhesion 28% (0.14) 25% (0.09) 3% (0.02)

cytosol 7% (0.04) 10% (0.04) death

6% (0.04) cytoskeleton 7% (0.04) 22% (0.10) 22% (0.08) 25% (0.05) 10% (0.05) RNA mitochondrion metabolism plasma membrane stress response ER/Golgi

Figure 22. Gene ontology analysis for genes overlapping DMD probes. Values represent percentage of enrichment and Fisher's Exact corrected p-value - %(p-value).

In order to validate logistic regression results, a Bayesian hierarchical method (MEthyLation Inference for Single cell Analysis – MELISSA) was also run with the purpose to cluster cells based on local methylation patterns. For this approach, profiles of the 533 DMDs (identified with pooled data) were imputed to individual cells. The clustering was able to separate cells across HyperDs, HypoDs and InterDs, therefore recapitulating polled data results (Figure 23). Samples are show for Ddx60 and Mid1 genes, that are differentially methylated across gene bodies.

Overall DNA methylation changes along gene bodies is generally reflective of changes in transcription (Veselovska et al., 2015), and thus the differential DNAme observed in old GV oocytes may be attributable to changes in transcription. In the next chapter, I will evaluate the transcriptome in old oocytes and correlate these changes with the observed changes in DNAme. Chapter 1. DNA methylation in Young and Old GV oocytes 45

Figure 23. left panel: Genomic regions for DNAme Ddx60 and Mid1 genes showing separated groups and consistency. Abbreviations: CDS: Coding domain sequence; CGIs: CpG islands. Right panel: MELISSA clustering test for single-oocytes. Probes used are DMD (N=533) obtained from pooled data.

2.4 Discussion

Due to limited material, especially of old GV oocytes, DNAme in naturally-aged oocytes have been assessed mainly by immunofluorescence (IF), rather than at single-base resolution (Ge et al., 2015). Sequenced-based studies published so far used hormonal hyperstimulation, in vitro ageing simulations through chemical exposure or prolonged oocyte culture after ovulation (postovulatory ageing) to mimic natural ageing phenotypes. These artificial ageing protocols are likely to introduce confounding factors and thus not resembling natural in vivo ovarian ageing. Indeed, hormonal treatment and chemical compounds in culture media per se were shown to cause DNAme alterations and oocyte maturation defects in different species (Diederich et al., 2012; Lee et al., 46 Female ageing alters oocyte DNA methylation, gene transcription and lysine methylation. A single-cell study

2017). The present study, presents the first high-resolution BS-seq data of naturally aged GV oocytes obtained without hormonal super stimulation, demonstrating that ovarian ageing leads to loss of DNAme, with possible consequences for the oocyte´s developmental capacity.

Current findings show that in fully-grown GV oocytes, both HypoDs and HyperDs appear to be affected mostly losing DNAme during oocyte ageing. HyperDs significantly overlap with highly expressed genes, and moreover, transcription events account for the vast majority of DNAme establishment in the oocyte (Veselovska et al., 2015). However, it remains to be elucidated if persistent transcription may be related to maintenance of DNAme during prolonged oocyte quiescent states in adulthood.

On the other hand, although hypomethylated domains contain generally shorter and lower expressed genes, there are some crucial genes within these regions which are paradoxically highly expressed. In the current study, old oocytes present a subset of important developmental genes with altered DNAme patterns overlapping HypoDs, thus suggesting changes in possibly active regions within these domains. Differential transcription activity for genes found at HypoDs and HyperDs will be addressed in later sections.

DNA methylation levels are reasonably stable in most of the genome between young and old group, but there are very compelling exceptions where some genes loose or gain a substantial amount of DNAme (≥20%). The effects of these robust changes remain to be elucidated; however, these are unlikely to influence oogenesis itself, as oocytes depleted of DNA methylation can progress successfully through oogenesis(Kaneda et al., 2004). Rather these changes may have a substantial effects on embryo development post-fertilisation, as inherited patterns of DNA methylation from oocytes is essential for viable offspring in mice (Kaneda et al., 2004).

InterDs are generally much smaller than HyperDs and HypoDs (~2-50 kbp) and overlap gene bodies and inter-genic regions. Bigger interDs (>100 kbp) are found near telomeres and centromeric Chapter 1. DNA methylation in Young and Old GV oocytes 47 regions (observed from chromosome views); however, there is no obvious tendency for an association to heterochromatin or transcriptionally silent genes. InterDs appeared to be more susceptible to oocyte aging since they tend to either gain or lose DNAme (random probes). It remains to be elucidated why are less stable and if this may also be true in other instances, such as environmental stimuli like maternal diet or hormonal hyperstimulation.

Genome-wide loss of DNAme has been previously reported for aged blood cells (Bollati et al., 2009; Heyn et al., 2012) hinting that this wide DNAme loss may be associated to common human age- related diseases. However, this health-associated relationship that cannot yet be made in female germ cells since blood is a highly replicating tissue, while oocytes are quiescent and can be found at different stages during follicular growing phases.

Previous IF studies have shown that MII oocytes from female mice aged over 40 weeks have lower global DNAme levels that persists until morula stage (Yue et al., 2012). This suggests, that the differences observed in the current study are likely to prevail into the embryo as well, with possible consequences for embryo implantation and development. Interestingly, Qian Y. and co-workers showed that DNAme decreases in MII oocytes as early as at 18 weeks of age, when there is still normal reproduction and oocyte reservoir, suggesting that partial loss of DNAme is not necessarily detrimental for oocyte competence. Interestingly, at the same time they reported an active demethylation process occurring in the oocyte, which is probably mediated by a significant increase of the ten-eleven translocation (Tet) methyl-cytosine dioxygenase 3 (Tet3) (Qian et al., 2015).

Another possible mechanism is that there may be DNAme decline or lack of maintenance during DNA repair in the quiescent state of oocytes, may therefore involve de novo DNMT activity. DNMT3A and 3B are involved in de novo methylation of cytosine residues, whereas DNMT1 acts as maintenance DNMT. All are expressed in oocytes and provide as maternal stores in early preimplantation embryos until embryonic genome activation (EGA) (Kaneda et al., 2004; Kiefer and Simon, 2019; Uysal et al., 2015). DNMT3A requires specific co-factors to enable its activity and performs methylation at CpG and at non-CpG sites (Shirane et al., 2013). Of note, DNMT3A in mice 48 Female ageing alters oocyte DNA methylation, gene transcription and lysine methylation. A single-cell study is located in the nuclei of GV oocytes and is cytoplasmically localized in the MII oocytes before fertilization (Hirasawa et al., 2008). Furthermore, in the rhesus monkey, the DNMT3A mRNA is transcribed in all oocyte stages, but expression levels significantly decrease from GV oocytes to MII oocytes (Vassena et al., 2005). Together these findings suggest that DNMT3A may decrease its DNAme activity close to meiotic resumption. Consequently, it is possible that in aged oocytes, this decline in DNMT3A activity may be precocious, resulting in a failure to re-methylate domains actively targeted by TETs or lost through repair of DNA damage.

Previous studies have also looked at DNAme levels in candidate genes in aged mammalian oocytes without finding an effect of ageing on DNAme. Bovine MII oocytes coming from donors of three age categories showed no significant DNA methylation changes in genes that are critically involved in oocyte development, such as bTERF2, bBCL-XL, bPISD, bBUB1, and bSNRPN, suggesting that these genes could be more resistant to abnormal methylation in comparison with other genes less predominant (Mattern et al., 2016). Furthermore, maternally imprinted genes in mice (Snrpn, Kcnq1ot1, U2af1-rs1, Peg1, Igf2r) didn´t show methylation changes in embryos at 10.5 days post- coitum (dpc) coming from females at the end of their reproductive lifespan (>43 weeks) (Lopes et al., 2009). Consistently, we observed stable DNAme throughout most of the genome, which supports using genome-wide approaches to identify DNAme changes associated with ageing.

Current results showed that all maternal gDMR (30) are affected by oocyte ageing, even though changes are subtle (less than 3% change), these regions are highly relevant since are related to imprinted disorders. Thus, further studies are required to evaluate how tolerant are GV oocytes to gDMR alteration and the effects on early embryo. Importantly, in the present study we show that a subset of genes that are highly relevant for early embryogenesis show dramatic changes in DNAme. Many of those were also related to neuronal development and response to stimulus, which is notable, as delayed motherhood has been associated with neurocognitive and psychiatric disorders (Balasch and Gratacós, 2012).

Chapter 1. DNA methylation in Young and Old GV oocytes 49

Surprisingly, oocyte ageing process is causing DNAme alterations in hundreds of genes that play an active role in further early embryo development, so even when methylation seems to be remarkably stable during GV state, very specific regions, including gene bodies and regulatory regions as promoters, are greatly affected. Since most of DNAme is reset by blastocyst state, it remains to be assessed whether the aberrant methylation in these genes persists trough the global wave of epigenetic reprogramming in the preimplantation embryo, or if they play relevant biological functions previous to reprograming.

2.5 Conclusions

The present study is the first to assess the consequences of natural in vivo oocyte aging on DNAme at a single-cell level, single-base resolution. It revealed DNA methylation changes in fully- grown GV oocytes in mice at the end of their reproductive lifespan. Generally, loss of DNA methylation was observed in regions of the genome, with a subset of highly and intermediately methylated regions losing over 10% of their DNAme. Importantly, maternal gDMRs and specific genes critically involved in embryonic development were aberrantly methylated. The current study also demonstrates that fully methylated and unmethylated regions are more stable during aging than intermediate methylated regions. Further studies are required to investigate the role of intermediately methylated domains in early oogenesis and if the DNA methylation changes in aged GV oocytes persist into embryo development.

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Chapter 2. Gene Transcription in Young d Old GV oocytes

3.1 Introduction

The maternal transcriptome is uniquely assembled during the growth phase of oogenesis, in which it accumulates a vast amount of RNA in the oocyte cytoplasm. In this time, oocyte volume increases 200- to 300-fold, resulting in a fully-grown oocyte containing approximately 80pg of mRNA and 350-500pg of total RNA (Bachvarova et al. 1985). Importantly, a progressive decrease in transcription initiates around the time of antrum formation (Moore and Lintern-Moore, 1978) such that a fully-grown prophase I germinal vesicle (GV) oocyte is essentially transcriptionally quiescent throughout the remainder of oocyte maturation, fertilization and early embryo development (Svoboda et al., 2015).

In the final stages of follicular growth, oocytes acquire developmental competence to further embryo development (Eppig and Schroeder, 1989). Along with follicular growth, the oocyte acquires M-phase characteristics such as phosphorylation of centrosomal proteins (Wickramasinghe and Albertini, 1992) and it changes DNA configuration from the non-surrounded nucleolus (NSN) to surrounded nucleolus (SN) configuration, in which highly condensed DNA surrounds the nucleolus prior to resumption of meiosis (Ma et al., 2013b) (Figure 24).

54 Female ageing alters oocyte DNA methylation, gene transcription NS H3 lysine methylation. A single-cell study

Figure 24. Dynamic of oocyte DNA transcription activity through oogenesis and oocyte maturation process.

NSN-to-SN transition coincides with transcriptional silencing during oocyte growth and competence acquisition. Typically, ~70–80% of full-grown oocytes display the SN configuration, the others remain in the NSN configuration (Davidson, 1986; Pan et al., 2005). Although it is not apparent why transcription ceases prior to completion of growth, it is observed in oocytes of all species examined to date (reviewed by Svoboda et al., 2015). The developmental competence of full-grown NSN oocytes is markedly compromised, with most arresting at the two-cell stage (Monti et al., 2013; Zuccotti et al., 1995). This difference may be linked to an altered transcriptome (Ma et al., 2013a) or changes in translation (Chalupnikova et al., 2014) that interrupt adequate maturation process.

Transcriptional quiescent state is followed by a wave of RNA degradation where approximately 20% of total maternal RNA is actively degraded (figure 21). This results in relative changes to transcript dosage at the metaphase II (MII) state, where some transcripts are either stabilized, destabilized, or remain unchanged, thus generating a particular gene regulation for acquisition of oocyte competence (Li et al., 2010; Ma et al., 2013a; Su et al., 2007). Later on, these selectively accumulated transcripts serve as the maternal-effect that instructs the regulation of pre- implantation development until embryonic gene activation, that in mice occurs in waves starting at 2-cell stage (Aoki et al., 1997; Park et al., 2013; Schultz, 2002).

Error! Reference source not found.. Gene transcription in Young and Old GV 55 oocytes

Transcription will resume upon embryonic genome activation, that occurs at different time points depending on the species. So that in mice starts sequentially at 2-cell stage (Abe et al., 2018), whereas in other mammalian species like porcine and human, embryonic genome activation happens later at the 4- to 8-cell stage; in bovine at 8- to 16-cell stage or, in other cases, after 12 rounds of DNA replication at the mid-blastula stage in Xenopus (Newport and Kirschner, 1982; Telford et al., 1990). This means, that the first occurring processes in the preimplantation embryo are regulated by maternal transcripts and proteins stored in the oocyte. These unique maternal transcripts are derived from the so called “maternal-effect genes”, that are expressed in the oocyte but influence early embryo development (Li et al., 2010).

As mentioned in previous section, DNAme at cytosine residues increases gradually from non- growing to fully-grown GV stage, reaching ~40% of DNAme at CpG context and close to ~5% at non- CpG context (Shirane et al., 2013; Smallwood et al., 2011). DNAme appears to be driven by transcription activity and histone modification in the oocyte at some level (Gahurova et al., 2017). Of note, although influence of transcriptional activity on oocyte DNAme establishment has not been demonstrated as a general trend , it appears that transcriptional changes drive DNAme in specific imprinted loci (Bretz and Kim, 2017; Veselovska et al., 2015), suggesting that if not partially ruled by transcription activity, DNAme has an important interplay with gene expression regulation during oocyte maturation process.

Considering the crucial role of maternal RNAs for embryo preimplantation development, changes in maternal RNA dosage during oocyte ageing may contribute to age-associated decline in female fertility. Most oocyte ageing studies to date used super ovulated MII oocytes to assess transcription (Hamatani et al., 2004; Pan et al., 2008; Santonocito et al., 2013). Hybridization arrays in human and mice MII oocytes indeed demonstrated important age-associated transcription variations. Genes identified were involved in cell cycle regulation, DNA damage response and repair, energy pathways, cytoskeletal structure, and transcription regulation (Hamatani et al., 2004; Steuerwald et al., 2007). 56 Female ageing alters oocyte DNA methylation, gene transcription NS H3 lysine methylation. A single-cell study

With the recent development of low-input sequencing techniques, the first single-cell RNA sequencing (scRNA-seq) evaluating human GV oocytes and age-related changes showed differences in gene expression between young and old human GV oocytes from patients ongoing fertility treatment (Reyes et al., 2017). In this particular case, only one gene (rp11) showed significant difference in GV oocytes, however they reported divergent expression patterns emerged during oocyte maturation when examined MII oocytes, where the difference increased up to 522 differentially expressed genes, concluding that main differences caused by oocyte ageing may occur during final stages of maturation and not during meiotic arrest.

To date, scRNA-seq data hasn´t been generated in mouse models to evaluate age-associated oocyte changes in transcription. An RNA-seq study performed on individual growing follicles reported a marked age-related upregulation of three critical oocyte-specific genes (Fgf8, Gdf9, and Bmp15) encoding paracrine factors that stimulate metabolic cooperativity with the granulosa cells (Duncan et al., 2017). Interestingly, this study also reported increased ribosome number and expression levels of RPS2 protein, a 40S subunit component suggesting probable alterations in translational activity in old GV oocytes.

Due to mixed results in oocyte age-associated transcription and the lack of information in GV oocytes, there are still questions to solve about the ageing mechanisms leading to oocyte quality decline and the notable pregnancy outcomes of advanced motherhood. The present study used RNA-seq to evaluate the whole transcriptome at a single-cell level in fully-grown young and aged GV oocytes. Furthermore, this study was performed in parallel with methylome assessing from the same cells, thus enabling linking possible changes in gene transcription and DNA methylation.

3.2 Methods

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3.2.1 Animal handling and oocyte retrieval

As the same samples were used for DNA methylation and transcription analysis, oocyte collection was the preformed as section 2.2.1 in the previous chapter.

3.2.2 Single-cell RNA-seq library preparation

Parallel sequencing of single-cell methylome and transcriptome was conducted as previously reported by Clark SJ (Clark et al., 2017) in combination with single-cell genome and transcriptome sequencing – G&T-seq (Macaulay et al., 2016) to measure DNAme and full-length polyA(+) mRNA from the same single oocyte. This section focuses on scRNA-seq library preparation (Figure 25).

Lysed GV oocytes were incubated with magnetic beads coated with a modified version of the tailed oligo-dT primer (Biotinylated Oligo-dT30VN – Table 3) from the Smart-seq2 protocol (Picelli et al., 2014), with the purpose to capture the polyA(+) mRNA molecules from the cell lysate. After mixing and magnetic precipitation of the beads in the lysate, the supernatant containing the genomic DNA (gDNA) was collected in several washing steps and transferred to new PCR tubes, leaving only the polyA(+) mRNA– loaded beads to which the reverse transcription (RT) mastermix was then added. RT was performed as in Picelli S, et. 2014, using SuperScript II reverse transcriptase, first-strand buffer (Life Technologies) and template-switching oligo (TSO) (Pinto and Lindblad, 2010) (Table 5).

TSO has been proven to improve the yield of full-length cDNA because it allows template-switching of the reverse transcriptase -RT. The reaction relies on 2–5 untemplated nucleotides that are added to the cDNA 3ʹ-end when the RT reaches the 5ʹ-end of the RNA, enabling the RT to switch template and synthesize a complementary sequence to the TSO. This results in full-length cDNA carrying the entire 5ʹ-end of the transcript plus an additional artificial sequence, which in this case is the same 58 Female ageing alters oocyte DNA methylation, gene transcription NS H3 lysine methylation. A single-cell study as the one located at the 5ʹ-end of the oligo-dT primer (i.e., the 3ʹ-end of the mRNA). During RT reaction samples were regularly mixed to prevent sedimentation of the beads to which the polyA(+) mRNA molecules are bound. RT reaction was performed in 3 cycles as follows: 60min at 42oC for RT and template-switching, 30 min at 50oC to enable unfolding of RNA secondary structures and 10min at 60oC to inactivate the enzyme.

Figure 25. scRNA-seq library preparation overview. PolyA(+) mRNA is captured using magnetic oligo(dT)- biotin beads and reversed transcribed. Subsequently, cDNA is amplified, purified and tagmented (fragmented & tagged) for sequencing. Library quantity and quality is assessed using the Bioanalyzer 2100 (Agilent) prior to sequencing.

After RT reaction, cDNA was amplified using Kapa Hifi HotStart ReadyMix (Kapa) and ISPCR oligo (Table 5) that acts as PCR primer for cDNA amplification (Picelli et al., 2014).

Error! Reference source not found.. Gene transcription in Young and Old GV 59 oocytes

Table 5. Oligo sequences for single-cell RNA-seq library preparation

Step Sequence PolyA (+) mRNA 5-Biotin-TEG-AAGCAGTGGTATCAACG capture CAGAGTACTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTVN-3; IDT

Reverse 5-AAGCAGTGGTATCAACGCAGAGTACA Transcription (tso) TrGrG+G-3; Exiqon cDNA amplification 5-AAGCAGTGGTATCAACGCAGAGT-3; IDT (ispcr)

Purification of PCR-amplified cDNA molecules was carried out with a solid-phase reversible immobilization (SPRI) purification using AMPure XP beads (Agencourt) using 1:0.8 ratio to eliminate fragments <200bp, as described in detail in section 2.2.2. The quality of the cDNA was assessed using a high-sensitivity DNA analysis kit on an Agilent Bioanalyzer 2100. Average cDNA length ranged between 0.5 and 2 kb, reaching a maximum of ~1–1.5 kb.

For the preparation of the sequencing libraries, tagmentation (fragmentation and adaptor ligation) was performed using the Nextera XT DNA Library Prep Kit (Illumina) following the manufacturer’s protocol. Each sample was uniquely indexed in a final PCR amplification step. Finally, samples were pooled, cleaned (AMPure XP bead cleanup using 1:0.6 ratio) resulting in an average fragment length of 200-600bp. Libraries were quantified with the Kapa Library Quantification Kit for Illumina platforms according to the manufacturer’s instructions and checked again for length and quality on an Agilent Bioanalyzer 2100.

3.2.3 Sequencing and mapping

60 Female ageing alters oocyte DNA methylation, gene transcription NS H3 lysine methylation. A single-cell study

The scRNA-seq libraries were sequenced using the Nextera XT kit (Illumina) as per the manufacturer's instructions but using one-fifth volumes. Multiplexed library pools were sequenced on one lane of an Illumina NextSeq500 HighOutput generating 100-bp single-end reads.

For quality control evaluation, FastQC (v0.11.6) was run for raw sequence reads (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) with parameters: -f fastq -e 0.1 -q 20 -O 1 -a NNNNN…-adapter sequence- followed by trimming of adaptors and poor-quality calls taken for FastQC files by Trim Galore (v0.4.4) (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) with parameters: trim_galore --gzip --phred33 --fastqc_args "-q". All RNA-seq libraries were then aligned onto the mouse genome assembly GRCm38 using Hisat2 (v2.1.0) (https://ccb.jhu.edu/software/hisat2/index.shtml) with the use of splicing option (Hisat2 – align – s) and parameters: -O3 -m64 -msse2 -funroll-loops -g3 -DPOPCNT_CAPABILITY.

3.2.4 Data analysis

SeqMonk program (https://www.bioinformatics.babraham.ac.uk/projects/seqmonk/) was used for the analysis of mapped sequencing data. Aligned reads were mapped to genes when overlapping with any exonic region of that gene. Thus, probes were generated over mRNA features, split into exons and were quantitated with Seqmonk RNA-Seq pipeline quantitation on merged transcripts. This pipeline quantitates at the gene level by counting the number of reads which fall into exons of each gene correcting for the number of reads of the sample. For statistical analysis, DESeq method was performed for differential analysis using raw read counts over genic exons (Love et al., 2014).

Library size was estimated and read count was normalized to library size and presented as reads per kilobase million (RPKM). Significance was set with � <0.05 and applying Benjamimi and Hochberg correction to avoid erroneous significance increase with multiple comparisons. Only Error! Reference source not found.. Gene transcription in Young and Old GV 61 oocytes genes with at least 1 read in all cells were taken into the quantitation. Ensembl annotation was used whenever gene annotations were required. Gene ontology (GO) analysis was performed using mouse genome informatics (MGI) functional annotation project database, developed by The Jackson Laboratory (http://www.informatics.jax.org/function.shtml), together with Gene Set Enrichment Analysis Software (http://software.broadinstitute.org/gsea/index.jsp) based on a priori gene sets grouped for biological functions and processes.

3.3 Results

3.3.1 scRNA-seq libraries quality control

First, to look at the proportion of reads falling into genes and exons, an automated QC plot was generated for each sample, revealing that all cells behaved as expected. Namely, reads fall exclusively into exons and mitochondrial and ribosomal RNA was eliminated with oligo(dT) capture (Figure 26. Left panel). Since duplication can be confounded by high coverage in certain regions, a plot of duplication against read density over all exons was performed. Duplication plot showed no evidence of technical problems with the samples, but instead a clear relationship between read density and duplication, with low density exons presenting low duplication (Figure 26. Right panel).

62 Female ageing alters oocyte DNA methylation, gene transcription NS H3 lysine methylation. A single-cell study

Figure 26. Quality control evaluation of scRNA-seq libraries. Left panel: Percentage of reads falling into different genomic features. Right panel: Duplication plots for all samples.

According to FastQC report a total of 4,307,797 ± 1,434,763.264 reads were obtained on average per cell (read count ± SD), with an average alignment rate of 84%. The mean GC content per sequence in the reads was 46% for all cells, except for 2 cells that showed aberrant CG content when looking at the CG distribution by position in the reads (Figure 27a). Additional to these 2 cells, samples presenting over 2 standard deviations away from the mean of total read count (Figure 27b), were considered as technical outliers and were therefore excluded from the analysis. After QC evaluation and filter, a total of 84 fully-grown GV oocytes were used for further analysis; 44 old and 40 young. Negative control libraries were sequenced to screen for presence of DNA contamination. Sequencing results reported less than 2kb reads in all of them with less than 50 reads aligned.

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17/04/2018 a) Sequencelane5715_GGACTCCT_AAGGAGT contentA_E05_L001_R1_trimmed.fq.gz across FastQCall Report bases b) Read count distribution %T Technical outlier 90 %C Total Read Count Total count by gene 80 %A %G 8e+06 70 15000 60

50

40

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20 10000 10

17/04/20180 lane5715_CGAGGCTG_CGTCTAAT_G08_L001_R1_trimmed.fq.gz FastQC Report 4e+06

90 Normal cell

80 Per sequence GC content

70 5000

60 2e+06

50

40

30

20 2e+0 0 10 https://www.bioinformatics.babraham.ac.uk/sierra/sierra.pl/lane5715_GGACTCCT_AAGGAGTA_E05_L001_R1_trimmed_fastqc.html?action=send_file&lane_id= Young Old Young Old 0 Position in read (bp)

Per sequence GC content Figure 27. a) Sequence base composition for technical outliers (up) and normal cells (down). b) Read count distribution for all reads (left) and read count per gene feature (right) representing outlier cells.

Probe values histogram for both groups (Figure 28a) clearly represents the bimodal distribution of oocyte gene expression and shows that there is a significant number of genes with no expression https://www.bioinformatics.babraham.ac.uk/sierra/sierra.pl/lane5715_CGAGGCTG_CGTCTAAT_G08_L001_R1_trimmed_fastqc.html?action=send_file&lane_id= (RPMK<0). Furthermore, the cumulative distribution of detected genes in individual cells also reflects that the limit detection tends to homogenize for genes with values above 0 (Figure 28b). Therefore, before performing a differential expression analysis, a cut-off for detectable expression was set at 1 for raw counts (genes with at least 1 read) in all cells, so that further analysis was performed preferentially in detectable expressed genes.

64 Female ageing alters oocyte DNA methylation, gene transcription NS H3 lysine methylation. A single-cell study

Figure 28. Gene expression values distribution for young and old oocytes. a) probe values histograms and b) relative distribution of probe trend for young and old oocytes.

In order to look at the relatedness of the samples within the biological groups (young and old), cells were clustered according to their similarity across different subsets of genes performing Component Analysis (PCA) for 19,386 genes with detectable expression levels (genes with at least 1 read). PCA included experiments (replicates) to include other possible technical confounders involved in group separation. From this first comparison it comes out that young and old GV oocytes separate into 2 distinct groups under PC1, which explains11% of the variation (Figure 29). Of note, cell separation also seems to be affected by the experiment since they tend to group together (not independently) in some cases. In order to discard any batch effect that may influence and bias the analysis, for further analysis data was corrected by experiment.

Error! Reference source not found.. Gene transcription in Young and Old GV 65 oocytes

50 Replicate 1 2 25 3 4 5 6

PC2 (5%) 0 Status y o

-25 -20 0 20 40

PC1 (11%)

Figure 29. Technical confounders. PCA plot showing the relationship between cells regarding possible batch effects (experiment number). PC1 explains 11% of the variation but is also affected by experiment.

Following corrections, a correlation matrix for technical confounders was performed for all cells. Correlation factors included well and columns from the plate, experiment and age. Results show that there is not tendency for the cells to group together around the technical procedures, but they do group better by age (Figure 30). Therefore, following this correction, differential gene expression analysis was then assessed. 66 Female ageing alters oocyte DNA methylation, gene transcription NS H3 lysine methylation. A single-cell study

Experiment Row Column Age o y

1.0

0.99

0.98

0.97

0.96

0.95

0.94 All cells

Figure 30. Correlation matrix including technical confounders and biological groups; young and old (age).

3.3.2 scRNA-seq analysis for young and old GV oocytes

First, a standard quantitation was performed using read count over exons with SeqMonk RNA-Seq quantitation pipeline with the aim to observe general trends of the samples. A total of 25,965 probes were generated over transcript units (genes). Quantitation is presented as Log2RPKM showing a moderate increase in transcript abundance of lowly expressed genes in old group compared to young (Figure 31). Error! Reference source not found.. Gene transcription in Young and Old GV 67 oocytes

Figure 31. Reads over exons quantitation for young and old GV oocytes. Left Panel) All probes (25,965) distribution. Right Panel) Scatterplot representing all probes plotted young vs. old. Quantitation is corrected to gene length.

In order to evaluate the correlation between gene transcript abundance and cell separation, PCA was performed including read count per gene, experiments and cell type. Results showed that genes in PC1 are weakly associated with cell separation (R2 = 0.16, p<0.05) (Figure 32a), indicating that differences observed between young and old oocytes are subtle for gene expression level. However, looking separated experiments for both groups, there is a consistent tendency, that is significantly different (p<0.05), to lose transcript abundance in old GV compared to young (Figure 32b)

68 Female ageing alters oocyte DNA methylation, gene transcription NS H3 lysine methylation. A single-cell study

a) 16000

Replicate 1 15000 2 3 4

per gene per 5 6 count

Total 14000 Status y R2 = 0.16 o p-value = 0.00012

-20 0 20 40

PC1 (11%)

b) 16000 Kruskal-Wallis, p = 0.00012

15000 Status

y o

14000 Total count per gene

01 02 03 04 05 06 Experiment

Figure 32. a) Principal component analysis. Data store is clustered according to high and low rotation values and experiment, both generated for young and old GV oocytes. Clustered values around PC1 are genes with high read count consistencies among all cells. b) Gene expression level across experiments and cell type. Read count differences are significant for all experiments.

Error! Reference source not found.. Gene transcription in Young and Old GV 69 oocytes

DESeq analysis for raw counts was then performed for 19,386 genes with detectable in all cells. Multiple testing correction was applied to avoid artificial significance increase. DESeq resulted in 314 differentially expressed genes (DEG) (p<0.05) between old and young GV oocytes (Figure 33a). Total read counts over genes results to be significantly different (Figure 33b) revealing that genes tend to lose transcripts in old GV (p<0.05). This finding is corroborated when calculating fold- change from RPKM values where 70 annotated genes were under expressed in the old group compared to young (p<0.05, fold-change≥1.5), whereas only 8 genes presented significant increase in transcript abundance (p<0.05, fold-change≥ 1.5) (Supporting information. Annex 1). Taken together, these results show that old GV oocytes are either downregulating gene expression activity or failing to maintain mRNA storage.

Figure 33. Differential expression analysis for detectable expressed genes. a) Scatter plot for all DESeq genes. Red and green dots represent under- and over- expressed genes respectively that are at least 1 unit different between groups. b) Total features. Box plots of number of expressed features by age group. The means of the groups show a significant difference (p = 3.6x10-6)

From previous comparison it came out that lowly expressed genes seemed to be more affected by oocyte ageing (Fig. 33a). Therefore, genes were filtered by their position in the distribution (Fig 34) and results show that indeed, lowly expressed genes contain the majority of differentially 70 Female ageing alters oocyte DNA methylation, gene transcription NS H3 lysine methylation. A single-cell study expressed genes. Among DEGs, there are 72 annotated genes in percentile-25; 12 genes between percentiles 25-50 (fold-change 1.5<); 1 gene in percentile 50-75 (fold-change 1.5<); whereas for highly expressed genes there are not differentially expressed genes with more than 1.5fold-change, neither gaining or losing expression (Supporting information. Annex).

Figure 34. Differentially expressed genes filtered by position in distribution. Data presented as log2RPKM corrected for gene length. Horizontal black bars represent mean ± SD.

To check what genes are involved in cell separation, hierarchical clustering was applied for DEGs. As expected, young and old GV oocytes separate into two major clusters of under and over expressed genes (Figure 35). Interestingly, heatmap also uncovers that some cells from old group behave like those in the young group. These “young-like” cells appear to separate from old group under a gene set that is hieratically close to young group (young-like gene set). Furthermore, it also was uncovered a separated gene set that is more variable among old group (variable gene set).

Error! Reference source not found.. Gene transcription in Young and Old GV 71 oocytes

Experiment Row Column Age y o

Variable Gene Set

Young-Like gene set

6 5 Old-Like gene set 4 3 2 1 0 -1 -2 -3 -4 -5 -6

Figure 35. DEGs heatmap. Hierarchical clustering of samples based on the relatedness of gene expression levels of 314 DEGs. Data is presented in Log2 RPKM corrected for gene length obtained with the normalise function from the scatter package in R. DEGs clusters are shown by technical confounders and biological group; young and old (age).

A total of 18 old oocytes clustered together as young-like (yL) behaving cells. This subset of old cells presents variable transcript levels, with some genes behaving as in young cells and some others as in old. Comparing gene sets from previous DEGs clustering, it is evident that old-like gene generates the biggest separation between young and old cells being mostly under-expressed in old oocytes (Fig. 36). And as expected, genes in the young-like set appear to behave in a similar fashion between young and old GV, reflecting variability within old group. This suggests that oocyte ageing may not occur equally for all cell meiotically arrested during a certain period. 72 Female ageing alters oocyte DNA methylation, gene transcription NS H3 lysine methylation. A single-cell study

Figure 36. Differentially expressed genes hierarchically clustered for old GV oocytes behaving young-like (yL). Values are normalized log transformed read counts for young and old oocytes.

Similar transcript level for yL and young oocytes comprehend genes presumably more stable regarding transcription regulation during ageing process. Genes with greater change in this young- like gene set are Testis expressed gene 16 (Tex16, log fold-change = 1.34) involved in RNA processing, and FAT atypical cadherin 3 (Fat3, log fold-change = 0.73) that participates in neural and retinal development. On the other hand, genes that change with age regardless ageing variation include Tenacin C (Tnc, log fold-change = 3.91), Chordin-like 1 (Chrdl1, log fold-change = 3.66) and Interleukin 13 receptor alpha 2 (Il13ra2, log fold change =2.28) (Figure 37).

Error! Reference source not found.. Gene transcription in Young and Old GV 73 oocytes

Young-like genes Old-like genes 5 5

4 4

3 3 RPKM RPKM 2 2

1 1

0 0 Tex16 Fat3 Ppp6r1 Sema6a Robo3 Abhd2 Tnc Chrdl1 Il13ra2 Prl8a2 Tlr8 Olfr805 young old young old

Figure 37. Top genes changing (fold-change) in young like and old-like gene sets. Young like gene set is referred as genes that behave like young group in old oocytes. Old-like genes are genes that separate young from old oocytes. Prl8a2 presents 8.82 RPKM for young group.

As previously noticed, another important finding from DEGs hierarchical clustering is the appearance of a “variable gene set” that applies for old GV and that is missing from young GV group. This gene transcript variability, together with the occurrence of yL oocytes, suggest that old group differentiates from young, not just at transcript abundance level, but also at intrinsic homogeneity of their transcripts.

Dispersion analysis test was run for genes with similar mean fold-change. Results confirmed that old GV are highly more variable with 3,641 genes over-dispersed compared to 39 genes deviating from the mean in young group (Figure 38a). Plotting DEGs filtered by their variability around fold- change (over 1 Standard-Deviation) showed that variable genes are found more frequently in old group compared to young (61 and 48 respectively) (Fig. 38b).

74 Female ageing alters oocyte DNA methylation, gene transcription NS H3 lysine methylation. A single-cell study

a) Differential dispersion test b) FDR vs fold-change

Tex16 Tnc

Prl8a2 Abs log10 FDR log10 Abs Posterior probability

Ust Old – STDEV >1.0 Young – STDEV > 1.0

Cdh2

Grem1 Lipo1 Chrdl1

Log2 fold change Log2 fold change old vs young

Figure 38. a) Differential dispersion test based on fold-change calculating distance from gene mean in old and young group. b) Volcano plot highlighting DEGs (FDR>2%) showing high variability in young and old GV.

Gene ontology (GO) analysis for DESeq2 gene list was performed using MGI Gene Ontology GO_Slim chat tool (http://www.informatics.jax.org/gotools/MGI_GO_Slim_Chart.html). GO showed many genes affected by ageing are associated to cell organization and biogenesis, molecule transport and signal transduction, with several of these genes overlapping developmental process functions. All GO terms are significant (Fisher´s Exact corrected p- value) (Figure 39). Error! Reference source not found.. Gene transcription in Young and Old GV 75 oocytes

Cellular component Biological Process

respiratory system cellular cytoskeletal part component nuclear body development organelle part organization at microtubule 19% 10% cellular level organizing center 10% 56% 15% 39% metabolic centrosome 15% process 61%

67% cytoplasm 69% membrane-bounded 59% organelle cellular metabolic 43% process 73% cellular component organelle organization or biogenesis P< 0.001 P<0.001

Figure 39. Gene ontology analysis of genes differentially expressed in old compared to young GV oocytes. Results are shown for biological process ontology.

Importantly, none of top genes DEGs are oocyte specific, or oogenesis required according to AmigGO gene set enrichment analysis (Annex file). These is highly relevant since maternal transcripts go through a major wave of degradation upon EGA, so the probability of retaining embryo development transcripts decreases. The top 10 of over- and under-expressed genes showed that, the main genes losing transcript abundance are Tenascin C (Tnc; 15-fold change), a gene related to nervous system development; chordin-like 1 (Chrdl1; 10-fold change) which is fundamental in early embryonic cell differentiation, and interleukin 13 receptor alpha, also known as Neuroregulin-1 (Il13ra; 7-fold change) that is involved hematopoietic system development. This reveals that ageing of meiotically arrested oocyte affect transcripts that are crucial for early embryonic development. Weather this mRNAs are kept or play a role during oogenesis or oocyte competence acquisition requires further investigation.

Even though only a minority of genes showed substantial increased transcription (Figure 34b), some of them are highly relevant; Gremlin 1 (Grem1; 18-fold change) a BMP antagonist; uronyl-2- 76 Female ageing alters oocyte DNA methylation, gene transcription NS H3 lysine methylation. A single-cell study sulfotransferase (Ust; 2-fold change) that participates in limb growth and differentiation; and ATP binding cassette subfamily G member 3 (Abcg3; 2-fold change) an important in hydrolase expressed in early embryonic stages and several organs (EMBL-EBI; The Jackson Laboratory).

3.4 Relationship between transcription and DNA methylation

To assess a possible relationship between differentially expressed genes and DNA methylation, an annotation track was generated of differentially methylated probes from previous section overlapping differentially expressed genes. Even though for most differentially methylated probes were not found relation to gene expression between young and old oocytes, interestingly, 33 differentially expressed genes overlapped regions with altered methylation status in old oocytes (Figure 40).

In 30 of the 33 genes (90%) it was found a positive correlation between gene transcript abundance and DNAme, so these genes that loose or gain mRNA levels are also losing DNAme or gaining DNAme respectively. Only 1 gene (Tlrp) increased DNAme while decreased mRNA level, and three genes (Rims1, Gnaq and Rafgef4) lost DNAme and gained transcripts (Figure 41). Overall this suggests, that changes in transcription level during oocyte ageing can affect, or might be related, to the DNAme status of the oocyte.

To better illustrate DNAme and transcription correlation, DNAme values (%) were scaled to RPKM values using an arithmetic transformation ((young-old)/young *100). Probes were then plotted and resulted correlation is visible for all 33 genes (Fig 41a). An example for DNAme and transcription correlation is presented for Ddx60 gene (Fig 41b). Error! Reference source not found.. Gene transcription in Young and Old GV 77 oocytes

Figure 40. Left panel: Distribution of differentially methylated probes and DEGs. Red dots: all genes overlapping differentially methylated probes. Blue dots: DEGs that overlap differentially methylated regions

(33 genes). Right panel: Mean expression values (Log2 RPKM) for both, all genes and DEGs that overlap differentially methylated regions. Differentially expressed genes are taken from DESeq2 analysis.

78 Female ageing alters oocyte DNA methylation, gene transcription NS H3 lysine methylation. A single-cell study

a) b)

Ddx60

6

5

4

3

2 o

Expression (Log2 RPKM) (Log2 Expression y 1

0

0 20 40 60 80 Methylation (%)

Figure 41. a) Comparison of gene body DNAme and transcription for genes that identified to be both differentially methylated and expressed between young and old oocytes (N=33). Differences are standardized as: (young-old)/young *100.

3.5 Discussion

The present results demonstrated that oocyte ageing influences transcription levels of important developmental genes. The majority of differentially expressed genes in old oocytes showed a decline in gene expression compared to young oocytes. However, a few genes with a critical role in oocyte function and early embryo development were found that increased gene expression levels upon ageing, like Grem1 (>18 fold-change) and Ust (>15 fold-change).

Grem1 belongs to the DAN subfamily of bone morphogenic protein (BMP) antagonists. It has also been shown to transmit the sonic hedgehog (SHH) signal from the polarizing region to the apical ectodermal ridge during limb bud outgrowth, therefore playing a role in regulating organogenesis, body patterning, and tissue differentiation (reviewed by Brazil et al., 2015). Importantly, Grem1 has Error! Reference source not found.. Gene transcription in Young and Old GV 79 oocytes been shown to be a maternal effect gene in Xenopus; and reduced ovarian expression is associated with developmental defects and embryo lethality (Cw et al., 2018)

During oogenesis Grem1 is regulated by the oocyte secreted growth differentiation factor (GDF)-9 in granulosa cells. Downregulation of Grem1 is associated with declined ovarian reserve (DOV), demonstrating an important function in oocyte growth and maturation during folliculogenesis (Greene et al., 2014; Pangas et al., 2004). Further studies required to elucidate the consequences of Grem1 over expression in oocytes, and if it is involved in the molecular processes of oocyte quality decline during ageing process.

Unlike in the embryo, over expression of Grem1 not only results in increased limb growth and polydactyly, but has also been related to changes in cell differentiation, maintenance of undifferentiated states and several carcinogenesis processes in different cell types (Davis et al., 2015; Jaeger et al., 2012; Norrie et al., 2014). Grem1 important role in cell fate suggests an important and fine regulation during early embryogenesis.

UST protein is located in the Golgi membrane and catalyses the transfer of sulfate groups to several cell surface proteoglycans thus affecting cell-matrix, cell-cell and ligand-receptor interactions (Nikolovska et al., 2017). In the embryo, Ust is related to neuronal development, migration and polarization (Yu et al., 2018) and similar to Grem1, functions during limb growth in early embryonic stages as part of the SHH pathway (Lewandowski et al., 2015). However, Ust expression has not been directly related to oocyte molecular functions, however due its function and localization, it could play a role during zona pellucida (ZP) formation in the Golgi apparatus.

The oocyte´s Golgi apparatus represents an important site of processing secretory proteins like ZP components ZP1-3. ZP domains are composed by glycosylated proteins as betaglycan, glycoprotein- 2 and glycosyl phosphatidylinositol (GPI) (Wassarman and Litscher, 2018), which are eventually 80 Female ageing alters oocyte DNA methylation, gene transcription NS H3 lysine methylation. A single-cell study potential targets for Ust. Previous studies have shown that oocyte´s ZP is particularly affected in aged females due a to a phenomenon known as “zona hardening” (Nogués et al., 1988) which is thought to hinder sperm binding and posterior ZP-escape required for implantation. However, it has been suggested that zona hardening observed in old oocytes is not related to fertilization rates (Check et al., 2012). It remains to be explored whether Ust overexpression in old oocytes may affect the processing of ZP1-3 glycoproteins or if it has a different function.

On the other hand, the biggest loss in transcription levels was observed for Tnc (>15 fold-change), Chrdl1 (>10 fold-change) and Il13ra2 (>7 fold-change). Tenascins are extracellular matrix glycoproteins that act both as integrin ligands and as modifiers of fibronectin-integrin interactions to regulate cell adhesion, migration, proliferation and differentiation. Tnc is expressed in several cell types including ovaries and oocytes of primary and secondary follicles (McMahon et al., 2008). However, is better known for regulating the expression of key patterning genes during late embryonic spinal cord development (Karus et al., 2011) and the maintenance of undifferentiated stem cells (Hendaoui et al., 2014).

Different mutations of Tnc are related to variable behavioural and nervous system phenotypes including impaired synaptic plasticity and neurotransmission (Chiquet-Ehrismann and Tucker, 2011). A recent study showed that Tnc is expressed in the surface of trophoectoderm cells at the blastocyst stage in humans, and it has a critical role for embryo attachment to uterine cells during implantation (Aberkane et al., 2018). It is not yet clear if Tnc transcription level in meiotically arrested oocytes could affect later embryogenesis, especially because maternal RNA degradation after fertilisation. Nevertheless, this last finding is highly relevant since maternal mRNA status is decisive for embryo survival and Tnc downregulation may be reflected in further trophectodermal cell differentiation. More studies are needed to clarify Tnc function in the oocyte and the possible consequences of its downregulation in old GV oocytes.

Ilr13ra2 and Chrdl1 are extracellular components in different cell lineages and act throughout embryo development and in adult tissues as regulators of several signalling pathways. Il13ra2 Error! Reference source not found.. Gene transcription in Young and Old GV 81 oocytes responds to Interleukin-13 stimulus during inflammatory processes exhibiting diverse functions as cell differentiation factor (Tabata and Khurana Hershey, 2007), whereas Chrdl1, is involved in negative regulation of BMP signalling pathway present in embryo ectoderm and mesenchyme, where is thought to participate actively in dorsal-ventral axis definition (De Robertis and Moriyama, 2016).

Overall, misregulated genes in aged oocyte revealed a significant role for extracellular proteins and BMP signalling pathways. This may have implications for oocyte quality, since a recent study suggested that BMP signalling may participate in oocyte growth and maturation and specific BMP signalling factors were shown to be upregulated before maturation completion (Budna et al., 2017).

In oocytes, DNAme establishment is associated with active transcription activity during oocyte growth (Tomizawa et al., 2012; Veselovska et al., 2015). In agreement with this, gene expression changes in this study were found to correlate with altered gene body methylation in some genes. This is relevant since the DNA analysis in previous chapter showed important changes in DNAme as a consequence of female ageing. Generally, a correlation was found between deceasing transcription levels and loss in DNAme in old oocytes.

De novo DNAme occurs gradually but not synchronously during oocyte and follicular growth (Lucifero et al., 2004; Obata and Kono, 2002). The oocyte growth phase starts at the early secondary follicle stage. Folliculogenesis is a continuous process and in the adult ovary all stages from primordial to antral follicle can be found. It is possible that dynamic transcriptional changes affect the process on de novo DNAme during follicle growth and the active and synchronized communication between granulosa cells and the oocyte ((Svoboda et al., 2015).

Most of the differentially methylated regions in old GV are not associated with changes in gene expression, and only 27 of 472 (5.7%) differentially expressed genes appear to lose expression 82 Female ageing alters oocyte DNA methylation, gene transcription NS H3 lysine methylation. A single-cell study concomitant with DNAme at gene bodies. It is currently not known what this mechanism is and whether the DNAme changes observed in the present study result from aberrant de novo DNAme, from a loss of already set DNAme during aging, or from altered transcription machinery.

These results suggest that gene expression alterations during long periods of meiotic arrest do no account for overall changes in DNAme at gene bodies, since only a selected number of genes are correlated, however some affected genes might critical for oocyte competence acquisition.

3.6 Conclusion

Ovarian ageing resulted in significant changes in gene expression activity of old fully grown GV oocytes compared to young. Individual oocytes analysis revealed that oocyte ageing does not affect all oocytes at the same level thus generating a variable population of transcriptional states in old cells (yL). Furthermore, decreased transcription correlated with a decline in DNAme in a subset of differentially expressed genes, indicating that the interplay between transcription and DNAme deserves attention during oocyte ageing. Among the genes whose transcription was affected by aging, the greatest differences were found for genes related to BMP signalling pathway and extracellular proteins involved in cell migration and differentiation. With possible consequences for oocyte growth and maturation.

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Chapter 3. Histone 3 chemical modification in Young and Old GV oocytes

4.1 Introduction

Histone marks and gene regulation

DNA in eukaryotic cells is uniquely organized in multiple chromosomes, which are comprised of single linear DNA molecules wrapped around histone proteins. Together DNA and histones conform the chromatin fibres. There are five types of histones: H1, H2A, H2B, H3 and H4, which are highly conserved between eukaryotic species (Wu and Grunstein, 2000). Two copies of each of the four types of core histones (H2A, H2B, H3 and H4) form a histone octamer, which is wrapped by 145– 147bp of DNA (See figure 2 at introduction chapter). This complex conforms organized structures inside the nucleus that systematically packages DNA, and is termed a nucleosomes (Kornberg, 1974).

Different levels of DNA organization and condensation regulate the accessibility to the genome through the activation of chromatin modifiers interaction. This leads to silenced or active regions, which are repressive or permissive to binding by specific transcription factors and regulator proteins, respectively (Lorch and Kornberg, 2017). Chemical post-translational modifications (PTMs) of histone proteins has been revealed as one of the major driving forces in the complicated

90 Female ageing alters oocyte DNA methylation, gene transcription and H3 lysine methylation. A single-cell study system of epigenetic regulation, guiding fundamental biological processes, such as DNA replication, repair, and transcription (Lorch and Kornberg, 2017; Sueoka et al., 2018).

The histone 3 (H3) tail has been found to play a central role in epigenetic regulation primarily through alterations of lysine methylation. Tri-methylation of lysine 4 in histone 3 (H3K4me3) is one of the best-characterized histone modifications associated with active chromatin (Zhang et al., 2015). H3H4me3 is enriched at active promoters and correlates with transcriptional activity (Bannister and Kouzarides, 2011; Santos-Rosa et al., 2002).

Acetylation of lysine 27 in histone 3 (H3K27ac) is another histone mark that has been found to be associated with active chromatin, and is found to be localised at active promoters and enhancer regions (Creyghton et al., 2010). Contrastingly, di- and tri-methylation of lysine 9 in histone 3 (H3K9me2/3) are repressive modifications, which define silenced and repetitive heterochromatin by recruiting heterochromatin protein 1 (HP1) (Peters et al., 2001).

Oocyte development and histone modification

During mammalian oocyte growth and maturation, oocyte becomes arrested at prophase of meiosis I, where it remains quiescent up to years until the end of female reproductive life (see previous chapters). In this non-replicative state, epigenetic modifications and chromatin organization are actively dynamic and play important roles in the acquisition of oocyte competence (Gu et al., 2010; Nashun et al., 2015; Xu et al., 2009). A series of specific histone modifications present dynamic changes during oocyte transition from GV state to MII, including methylation, acetylation and phosphorylation in different amino acid residues, thus representing a necessary transition for oocyte competence (Gu et al., 2010).

Chapter 3. Histone 3 chemical modifications in Yong and Old GV oocytes 91

Recent studies have given insights into H3K4me3 dynamics through oogenesis. H3K4me3 is initially coupled to transcription in non-growing oocytes where it has a predominant enrichment at active promoters. Subsequently, in fully-grown transcriptionally silent GV state, this specific deposition changes to a broader domain occupancy and appears to be transcriptionally independent (Dahl et al., 2016; Hanna et al., 2018a; Zhang et al., 2016). Thus, resulting in acquisition of broad distal peaks at intergenic regions, putative enhancers and silent H3K27me3-marked promoters, resulting in canonically marked enhancers resetting (Hanna et al., 2018a). Notably, this last study, also shows that H3K4me3 loss presents limited direct consequences on transcriptional activity, thus opening again the question about the coupling of H3K4me3 and transcription.

H3K27me3 is a mark that is enriched at silent regulatory elements during primordial germ cell formation and remains pretty stable during oogenesis (Figure 1); however, after fertilization in the pre-implantation embryo H3K27me3 is remodelled at regulatory elements while remaining at distal and intergenic regions (Zheng et al., 2016). All this high dynamic of histones chemical marks deposition through active/inactive regions reveals widespread resetting of epigenetic memory and high plasticity of epigenome during gametogenesis and early development.

Primary oocyte Growing GV Fully-grown GV MII NSN SN

H3K4me3 Active promoters Active and inactive domains Resetting Active promoters C, Hanna 2018; Dahl, 2016 (Uli-Chip-seq)

Active enhancers ? Distal enhancers C,Hanna 2018; Dahl, 2016 H3K27ac (Uli-Chip-seq, STAR-Chip-seq)

H3K9me3 ? Heterochromatin Mareike Puschendorf , 2008 (Immunoassay)

Figure 42. Histone marks dynamics during oocyte growth. H3K4me3 accumulates during growth and it goes from being enriched at active promoters to be located also at inactive domains. H3K27ac is enriched at active enhancers/promoters co-existing with H3K4me3. Appears to have low presence at MII oocyte but it gains enrichment at distal enhancers after fertilization. There is no evidence from fully-grown GV state -MII transition. H3K9me3 is associated to heterochromatin in somatic tissue and is maternally inherited by the pre-implantation embryo. There is lack of dynamics information during oogenesis. 92 Female ageing alters oocyte DNA methylation, gene transcription and H3 lysine methylation. A single-cell study

In the case of repressive H3K9me3 deposition, there is currently no molecular data evaluating its dynamics in oocytes or early embryogenesis. Immunofluorescence experiments show that H3K9me3 typically associates with silenced repetitive DNA and is predominantly inherited at maternal peri-centromeres in the early embryo (Puschendorf et al., 2008). In post-implantation embryos, H3K9me3 has been defined as a key factor to maintain imprinting control regions (ICRs) by recruiting krüppel-associated box-containing zinc-finger protein (KRAB-ZFP) ZFP57 (Quenneville et al., 2011). Due to H3K9me3 relevant participation in early embryogenesis, it is important to understand the molecular mechanisms underlying gene silencing that might be associated with this Fully-grown GV MII repressive mark during gametogenesis. SN

The mature oocyte's epigenome also contains repressive H3K9me2/3 and H4K20me3 as well as active marks H3K4me1/3. During meiotic resumption and progression from an arrested germinal vesicle (GV)-stage oocyte to a metaphase-II (MII) oocyte, the histones undergo deacetylation and Active and inactive domains Resetting Active promoters C, Hanna 2018; Dahl, 2016 global gene transcription ceases (Reviwed by Marshall and Rivera, 2018). Histone marks and have (Uli-Chip-seq) been also proposed to be affected by oocyte ageing, thus altering their normal behaviour from GV to MII transition.

Distal, inter-genic regions Zheng et al., 2016 (STAR-Chip-seq) So far, ageing studies on oocyte histone marks have been performed using immunostaining assays. Lysine methylation analysis have shown that GV oocytes from old (10 months old) female mice present decreased H3K36me2, H3K79me2, H4K20me2 and H3K9me3 levels when compared to C,Hanna 2018; Dahl, 2016 Active enhancers ? Distal enhancers young (2 months old) (Manosalva and González, 2010). (Uli-Chip-seq, STAR-Chip-seq)

In case of histone acetylation, increase of acetylation of lysine 12 in histone 4 (H4K12ac) has been ? Mareike Puschendorf , 2008 strongly associated to aberrant chromosome disjunction in mature oocytes from women over 36 heterochromatin (Immunoassay) years old, revealing an important role of histone deacetylases during oocyte meiosis arrest. In old female mice, results are consistent with those in human for MII oocytes, however, interestingly, at GV state H4 acetylation shows an opposite behaviour, where actually acetylation at H4K12 residue Chapter 3. Histone 3 chemical modifications in Yong and Old GV oocytes 93

decreases significantly with age (Akiyama et al., 2006; Manosalva and González, 2009), a phenomenon that is shared with humans given that H4K12ac is retained in MII oocytes from women >31 years of age when compared to oocytes from women 21–30 years old (van den Berg et al., 2011).

GV oocytes represent a unique opportunity to evaluate histone marks dynamics, because oogenesis from oogonia formation to final follicular phases is a highly dynamic process that takes place in the absence of DNA replication and cell division. Furthermore, GV oocytes are significantly influenced by female ageing since they remain in a quiescent state until reproductive life ends. Having on account previous associations between biochemical state of oocyte histones and meiotic resumption, the research into histone modifications and modifiers represents an important next- step to understand possible chromatin alterations related to age-associate oocyte decline. Therefore, the aim of this study was to evaluate through immunostaining assay, specific histone modifications at H3 in young and old GV oocytes, and its correlation with gene expression of several chromatin modifiers.

4.2 Methods

4.2.1 Animal handling and oocyte retrieval

C57BL/6Babr mice were used throughout this study. Virgin females were housed in groups up to 5 until they had reached the desired age as indicated. All animal experiments were conducted in full compliance with UK Home Office regulations and with approval of the local animal welfare committee (AWERB) at The Babraham Institute. “Young” females were defined as 12 weeks old and “Old” females were between 44 - 54 weeks old. Ovaries were collected from both groups and transferred into M2 medium (M7167 Sigma-Aldrich) at room temperature. Oocyte retrieval was performed mechanically using needles to release them from the ovarian follicles. Fully grown 94 Female ageing alters oocyte DNA methylation, gene transcription and H3 lysine methylation. A single-cell study germinal vesicle (GV) oocytes were collected with glass mouth pipettes and freed from somatic cells by gently up-and-down pipetting and consecutive washes in M2 medium. Clean GV oocytes were washed twice in PBS and fixed with 4% paraformaldehyde (PFA) + 0.2% bovine serum albumin (BSA) for 20 min at RT. Oocytes were then washed in 0.2% BSA + 0.05% Tween-20 (in PBS), transferred into permeabilization buffer (0.7% Triton X-100 in PBS) and incubated protected from light in a humidity chamber at 4oC until immunostaining procedure. Oocyte collection was performed from 1-2 females at the time per group, thus samples were generated from mixed biological origin in each replicate.

4.2.2 Immunohistochemistry and immunofluorescence

Permeabilizated oocytes from young and old groups were incubated first with blocking solution (2% BSA in PBS) for 1h at RT. Next, oocytes from both groups were incubated overnight at 4oC with primary antibodies as follows: anti-H3K4me3/anti-Histone Pan, anti-H3K9me3/anti-Histone Pan or anti-H3K27ac/anti-Histone Pan (Table 6). All antibodies were used at 1:500 final dilution. Anti- Histone Pan was used as internal control for each histone mark staining. Following this, oocytes were washed twice with blocking solution and were incubated with secondary antibodies for 45min protected from light in humidity chamber at RT. Secondary antibodies were used as described in table 1.

Table 6. Primary antibodies used for histone immunostaining.

No. Oocytes Primary Antibody Secondary antibody young old H3K9me3 (Rabbit polyclonal). Alexa Fluor 568 (Goat Anti-Rabbit IgG 8 5 Abcam, ab8898 H&L). Abcam, ab175471 H3K27ac (Rabbit polyclonal). Alexa Fluor 568 (Goat Anti-Rabbit IgG 7 4 Abcam, ab4729 H&L). Abcam, ab175471 Chapter 3. Histone 3 chemical modifications in Yong and Old GV oocytes 95

H3K4me3 (Rabbit monoclonal). Alexa Fluor 568 (Goat Anti-Rabbit IgG 7 5 Millipore, 04-745 H&L). Abcam, ab175471 Anti-Histone Pan. Clone H11-4 Alexa Fluor 488 (Donkey Anti-Mouse IgG Used for Used for (Mouse monoclonal) Millipore – H&L). Abcam, ab150105 all all Merk – MAB3422

After incubation with secondary antibodies, oocytes were washed by incubating for several 10min periods in blocking solution. Groups of 2-3 oocytes were then mounted on glass slides using 1µl fibrinogen (Calbiochem) and thrombin (Sigma) from human plasma, and mounting media with DAPI (Sigma). Samples were stored protected from light at 4oC until laser-scanning confocal microscopy imaging.

Images of the mounted oocytes were taken using a Carl Zeiss LSM 780 Confocal with 40X objective. A total of 20 young and 16 old oocytes were immunolabelled divided into 3 groups/histone marks: H3K4me3 (7 young and 5 old); H3K9me3 (8 young and 5 old); and H3K27ac (7 young and 6 old).

4.2.3 Data analysis

Image analysis was performed using Fiji (ImageJ) v.2.0.0-rc-68/1.52e (http://imagej.net/) which is an open source Java image processing program that allows for quantitation of fluorescence intensity values across regions of interest. Cell and germinal vesicle areas were measured in microns2, and fluorescence intensity was measured using the Mean Gray Value, which is Integrated Density / Area. Quantitation for all channels was performed creating masks over DAPI stained regions and overlaying those selected regions with their corresponding images immunoassayed for the 3 histone marks and respective Histone Pan. Each histone mark florescence was divided by its equivalent Histone Pan intensity value, thus normalizing each value to its own internal control. Statistical analysis was performed in R software environment (https://www.r-project.org/). Welsh t test was performed to compare means of unpaired samples with different variances. When 96 Female ageing alters oocyte DNA methylation, gene transcription and H3 lysine methylation. A single-cell study normality was not supposed, unpaired two-samples Wilcoxon test was applied. Significance level was set with � value ≤ 0.05.

4.3 Results

No significant difference was obtained for any histone mark (p>0.05), maybe due to small sample size. However, old oocytes presented diminished intensity for H3K27ac and increased intensity for H3K4me3 (Figure 2a).

On the other hand, this assay also revealed that chromatin architecture is changing with age. Old oocytes presented less condensed, irregular and particularly expanded nucleus morphology when compared to young (Figure 43b and 44b). Furthermore, nucleus size is significantly bigger in old oocytes (p<0.05). Cell size also tends to be bigger in a proportion of old oocytes, despite of no statistical difference (Figure 3a).

Another important observation is that zona pellucida (ZP) retained histone marks antibodies in 87.5% (14/16) of old oocytes, whereas in young cells this unspecific staining was almost absent (0.15%) within the same replicate (same antibody batch). Thus, suggesting that ZP alterations might be also related to oocyte ageing process.

Together, these findings suggest that perhaps relevant organizational changes at cytoplasm, ZP and nucleus level are occurring during oocyte ageing. Bigger sample and more detailed protein assays will be more informative about histone and cell structure.

Chapter 3. Histone 3 chemical modifications in Yong and Old GV oocytes 97

Figure 43. a) Distribution of fluorescence intensity for H3K4me3, H3K9me3 and H3K27ac immunostainings in young and old oocytes. b) Imaging for DNA (DAPI – blue), all histones (PanH – green), specific histone marks (anti-H3K3me3, H3k9me3 and H3K27ac – red) and overlay, for young and old oocytes.

98 Female ageing alters oocyte DNA methylation, gene transcription and H3 lysine methylation. A single-cell study

Figure 44. a) Cell and nucleus areas for young and old oocytes. b) Chromatin distribution in old oocytes. Blue: DAPI staining for DNA. Green: All histones immunostaining.

Due to particular chromatin organization, a subset of oocyte specific chromatin modifiers was also checked from previous scRNA-seq analysis (previous chapter). Gene list was evaluated according * *

Chapter 3. Histone 3 chemical modifications in Yong and Old GV oocytes 99

to maternal chromatin remodelling factors obtained by Peter Nestorov et al. (Nestorov et al., 2015). From 17 genes evaluated, only 2 genes showed significantly lost in old oocytes (P<0.05): Developmental pluripotency associated 1 (Dppa1) and Deleted in azoospermia-like (Dazl) (figure 45).

Chromatin modifiers expression in young and old oocytes 1.30 * * 1.20

1.10

1.00

0.90 Fold Change (RPKM) Change Fold

0.80

0.70

Zp3 Dazl Brdt Pcgf1 Tet3 Tpmt Dppa1 Prdm6 Smyd3 Scml2 Phf19 Smyd4 Kdm1b Dclre1c Suv39h2 Jhdm1d Mecom

Figure 45. Gene expression of histone modifiers. Purple and orange bars indicate genes gaining and losing expression in old oocytes respectively. Statistical significance is highlighted by *

4.4 Discussion

In previous sections of the current study, it was shown that GV oocytes coming from female mice over 45 weeks old present a significant loss of DNAme at a subset of domains and altered gene expression. Additionally, as is presented in current section, old oocytes also appear to have differences in nucleus size and organisation.

Old oocytes tend gain H3K4me3 when compared to young, although not significantly. Active mark H3K4me3 presents a non-canonical broad deposition in fully-grown GV oocytes, characterized by 100 Female ageing alters oocyte DNA methylation, gene transcription and H3 lysine methylation. A single-cell study increased enrichment at distal regions and promoters of transcriptionally silent genes enriched in repressive H3K27me3, and importantly, at this quiescent state is also anti-correlated with DNA methylation (Dahl et al., 2016; Hanna et al., 2018a). In the oocyte, it appears that DNAme functions to prevent the deposition of H3K4me3 (Hanna et al., 2018b); therefore, increased H3K4me3 could be associated with DNA methylation loss during oocyte ageing. This could happen due to H3K4 methyltransferase MLL2 targeting H3K4me3 to unmethylated CpGs (Hanna et al., 2018a) and consequently increasing H3K4me3 deposition in DNA methylation-depleted regions.

H3K27 acetylation seem to remain stable in aged GV. H3K27ac is a mark of active cis-regulatory elements (cREs) reported to be highly cell- and tissue-specific. ChIP-seq analysis in immature GV oocytes shows that H3K27ac co-occupies distal enhancers together with H3K4me3 (Hanna et al., 2018a), suggesting enhancers are atypically marked similar to promoters. Interestingly, in mature MII oocytes H3K27ac has a low enrichment that increases again at distant enhancers in 2-cell embryos (Dahl et al., 2016), consistent with observations made by immunofluorescence immunofluorescence (Endo et al., 2005; Kim et al., 2003)

Aging changes in chromatin configuration and histone methylation of germinal vesicle stage (GV) oocytes was already reported by I. Monosalva and co-workers (Manosalva and González, 2010). In this immunofluorescence-based study they found that GV oocytes coming from females over 11 months of age presented a particular chromatin arrangement, which they called nNSN-nSN. These cells are in GV state, but they don´t fit with surrounded nucleolus (SN) nor non-surrounded nucleolus (NSN) chromatin configuration. In contrast with these results, in current immunostaining assay, all old oocytes presented SN. However, chromatin do presents similar clumps structures (Figure 3b) as the previous report. This phenomenon could be related to altered chromatin modifications action in old oocytes.

Old oocytes presented significant gain of Dppa1 and Dazl expression (Fold-change 1.22, p<0.05). Dppa1 is a downstream gene of Oct-4 (Bortvin et al., 2003). Oct-4 is an essential transcription factor for the formation and/or maintenance of the inner cell mas (ICM) and epiblast, and null embryos Chapter 3. Histone 3 chemical modifications in Yong and Old GV oocytes 101 are incapable of giving rise to ES cells (Avilion, 2003; Mulas et al., 2018). Dppa1 itself is also involved in neuronal differentiation process (Glazova et al., 2015) and has been recently found to be a lethal gene using knockout models (The International Mouse Phenotyping Consortium et al., 2016). There is no current evidence of Dppa1 activity in the oocyte, on the contrary, Dppa1 is first seeing at zygote stage, not related to the maternal gene load (Nestorov et al., 2015). Importantly, this last study reports metaphase II oocyte gene expression, at the onset of maternal RNA degradation/processing before fertilization, leaving still the interrogator open about Dppa1 function since upstream genes, as Oct-4 are highly active in the oocyte (Kehler et al., 2004; Zuccotti et al., 2011; Zuccotti, Maurizio et al., 2009).

On its part, Dazl is recognized as maternal gene associated to chromatin modeling (Nestorov et al., 2015). Dazl belongs to DAZ family of adaptors for targeting mRNA transport acting as activators of their translation. Dazl is expressed in primordial germ cells (PGCs) and/or pre-meiotic and meiotic germ cells of both sexes, and interestingly, in its absence the meiotic spindle fails to form due to disorganization of meiotic microtubules during oocyte maturation (Chen et al., 2011; Fu et al., 2015). Altered expression of Dazl in GV oocytes during ageing process could be related to downstream expression profile changes or chromatin architecture variations. More studies need to be performed to elucidate its role on oocyte chromatin stability during prolonged meiosis arrest.

Increased cell size was also reported previously in old oocytes, together with altered nucleolus architecture and changed translational activity (Duncan et al., 2017). These findings are highly relevant, being that cytoplasm volume is thought to increase due to disrupted proteostasis that leads to accumulation of deteriorated proteins (Duncan et al., 2017). The current study approach doesn´t give insight into protein homeostasis, however old oocytes aren´t just changing cytoplasm size, but also germinal vesicle area (p<0.05). Thus, suggesting important changes at protein/structural level.

Another important observation from current results, is that ZP staining looked different between young and old oocytes. ZPs from old GV retained immunostaining in contrast to young oocytes. 102 Female ageing alters oocyte DNA methylation, gene transcription and H3 lysine methylation. A single-cell study

Gene expression analysis showed altered Ust and Tnc genes in old GV, both associated to extracellular matrix compounds, that could explain the differences. Further work needs to be done in this regard.

Results shown in this section reflect that there is not only changes at histone methylation, DNA methylation and gene transcript abundance (see previous sections), but also, there are significant alterations at cellular level, such as increased cellular size, ZP characteristics and altered chromatin distribution. Each one of these could be related to several mechanisms and may involve different molecular actors, indicating that oocyte ageing is a complex phenomenon that effects varied biological and cellular processes. More robust protein analysis would be interesting to explore with the aim to dig more into chromatin modifiers and protein accumulation mechanisms during oocyte ageing process.

4.5 Conclusions

The current study shows that histone H3 modifications may altered in oocytes coming from females at the end of their reproductive life. Despite of no statistical significance, old oocytes tend to gain H3K4me3 and lose H3K27ac, both related to active transcription units. These findings, together with DNAme loss, suggest that ageing process could be generating chromatin regions with lower expression activity in fully-grown GV oocytes. Old oocytes also present a bigger cytoplasm and altered ZP. Small sample was a limiting factor that could be the cause of insufficient statistical power. More detailed studies need to be done for the sake of a better understanding of age- associated histone marks alterations and chromatin configuration maintenance.

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Concluding remarks and future directions

Female germ cells remain as quiescent cells within the ovary from the early oogenesis at embryonic stages until ovarian reserve exhaustion at the end of reproductive life span, which in women case, last decades. This implies that the oocyte competence to resume meiosis and effectively maturate, should remain stable trough a significant number of oestrous cycles if a healthy pregnancy is to be achieved.

Oocyte maturation requires crucial molecular and cellular changes, including maternal mRNA accumulation, gene silencing, selective DNA methylation establishment, chromatin configuration rearrangements and meiotic spindle assembly. These represent enormous challenges for a cell that can remain viable for several years inside ovarian follicles.

Female reproductive ageing brings with it the decline in the optimal fertility period prior to the end of the oocyte reservoir. This conveys, among other several physiological alterations, a failure in oocyte maturation mechanisms. Alternations in the oocyte molecular machinery are still under study and key mechanisms remain to be uncovered.

Since reproductive life is generally shorter than overall lifespan, and some studies suggest that long reproductive life is directly associated to extended longevity (Scott et al., 2014; Wainer-Katsir et al., 2015), leading to the idea that, in some contexts, prolonged reproductive capability might be associated to a better health. This hypothesis is supported by the finding that oophorectomy in women accelerates ageing and the appearance of age- associated neurological disease (Scott et al., 2014). Together, these findings highlight the

108 Female ageing alters DNA methylation, gene transcription H3 lysine methylation. A single-cell study importance of reproduction in health quality, and vice versa, the effect of prolonged health span over reproduction capacity.

According to this, reproduction and longevity studies represent a big challenge because they link parameters of good quality of life. Therefore, more advantages and informative approaches are required.

The current results allowed a deeper look into individual variability in single aged oocytes, revealing that not all cells behaved the same manner, suggesting that female germ cells ageing is not an overall synchronous process. This last finding is highly concordant to female reproductive biology as embryo development is not always affected.

This study at single-cell and single-base resolution reveals that female ageing alters transcriptome and methylome of meiotically-arrested GV oocytes. These findings are highly relevant since DNA methylation and gene transcription changes were observed at several important developmental genes in old oocytes.

Further studies about age-associated oocyte changes at DNA methylation and mRNA stability through female reproductive lifespan are required to understand the downstream effects on embryo development. It is not known yet how much of chromatin and mRNA state can prevail in the embryo and what are the consequences of an altered state. It would be interesting to dig more into the reprogramming capability of the zygote and the maternal influenced cell lineages to understand how much of old oocyte alterations really affect future generations.

Annex 1: Annex 1. Differentially methylated gene list

Feature Young Old young-old old-young Smpd3 26.235327 62.163746 -35.928419 35.928419 Cpeb3 63.739143 98.16117 -34.422027 34.422027 F8 45.626823 79.906784 -34.279961 34.279961 Gck 25.880651 59.60843 -33.727779 33.727779 Mtmr12 44.559143 76.11633 -31.557187 31.557187 Maml3 22.995886 54.22944 -31.233554 31.233554 Il1rapl2 17.510256 48.43651 -30.926254 30.926254 Clip4 52.28305 82.20374 -29.92069 29.92069 Adcyap1 6.7855997 36.06238 -29.27678 29.2767803 Plcxd1 45.934837 74.68134 -28.746503 28.746503 Kcnab2 34.789936 63.091553 -28.301617 28.301617 Tram1 52.15116 80.15399 -28.00283 28.00283 Mtap7d2 33.287254 61.227425 -27.940171 27.940171 Atp10a 53.084675 80.76425 -27.679575 27.679575 Cbs 16.443594 44.052547 -27.608953 27.608953 Morn4 22.366554 49.70672 -27.340166 27.340166 Ptprm 40.58537 67.850235 -27.264865 27.264865 Methig1 41.5233 68.677216 -27.153916 27.153916 Tmprss2 34.467445 61.61911 -27.151665 27.151665 Bnip3 41.43625 68.51171 -27.07546 27.07546 Enox1 36.2147 63.145805 -26.931105 26.931105 Zfp960 43.527405 70.43518 -26.907775 26.907775 Ccdc141 23.137371 49.802277 -26.664906 26.664906 Syt1 53.487537 80.1009 -26.613363 26.613363 Tril 51.73656 78.2275 -26.49094 26.49094 Pdgfb 68.0276 94.344315 -26.316715 26.316715 Zbtb20 52.224556 78.30476 -26.080204 26.080204

112 Título de la tesis o trabajo de investigación

Srgap2 49.66709 75.63511 -25.96802 25.96802 Smurf1 43.113934 69.06256 -25.948626 25.948626 Hlcs 41.93992 67.80943 -25.86951 25.86951 Rps6ka4 46.1214 71.721436 -25.600036 25.600036 Foxp1 34.854156 60.250225 -25.396069 25.396069 Fxyd6 68.886055 94.1882 -25.302145 25.302145 Mtap7d1 41.81727 67.053665 -25.236395 25.236395 Slmap 58.379402 83.48505 -25.105648 25.105648 Sh3rf2 15.840741 40.60639 -24.765649 24.765649 Fbxl3 8.58266 32.992676 -24.410016 24.410016 Eapp 28.069473 52.42868 -24.359207 24.359207 Nck1 47.5786 71.93349 -24.35489 24.35489 Gli3 38.557873 62.905334 -24.347461 24.347461 Bhlha15 25.213879 49.513054 -24.299175 24.299175 Acvr2b 28.73666 52.95039 -24.21373 24.21373 Ext1 56.359276 80.250656 -23.89138 23.89138 Tut1 30.970325 54.78771 -23.817385 23.817385 Mbd5 53.25877 76.961105 -23.702335 23.702335 Myl4 32.44399 56.03718 -23.59319 23.59319 Atp5o 48.416267 71.98149 -23.565223 23.565223 Pkd2 48.778095 72.33492 -23.556825 23.556825 Il27ra 40.73283 64.23985 -23.50702 23.50702 Pcyox1l 65.752884 89.199196 -23.446312 23.446312 Wwox 48.729874 72.01471 -23.284836 23.284836 Sep11 58.490185 81.67427 -23.184085 23.184085 Msrb3 16.724493 39.862286 -23.137793 23.137793 Adh7 18.143248 41.219917 -23.076669 23.076669 Rbpms 45.735043 68.76971 -23.034667 23.034667 Ppil3 48.8937 71.71601 -22.82231 22.82231 Igsf9 43.141975 65.93954 -22.797565 22.797565 Bnc2 69.43273 92.18943 -22.7567 22.7567 U3 36.105415 58.81549 -22.710075 22.710075 C4b 62.106457 84.77328 -22.666823 22.666823 Ethe1 59.63572 82.243225 -22.607505 22.607505 Fam151b 57.164665 79.69441 -22.529745 22.529745 Fam65a 44.36324 66.81651 -22.45327 22.45327 Gbx2 53.81966 76.271385 -22.451725 22.451725 Map3k1 21.996283 44.446 -22.449717 22.449717 Agfg2 50.03389 72.414856 -22.380966 22.380966 Anexo A. Nombrar el anexo A de acuerdo con su contenido 113

Rabgap1l 62.54762 84.90163 -22.35401 22.35401 Mcph1 65.87291 88.20423 -22.33132 22.33132 Gldc 65.171974 87.48839 -22.316416 22.316416 Frmd4a 50.682426 72.83107 -22.148644 22.148644 Tns3 53.64925 75.77551 -22.12626 22.12626 Ypel1 32.618782 54.648293 -22.029511 22.029511 Il1rapl1 14.211662 36.236904 -22.025242 22.025242 Arhgap10 34.84858 56.743248 -21.894668 21.894668 Casc4 55.76653 77.59522 -21.82869 21.82869 Sntg1 42.832188 64.43612 -21.603932 21.603932 Ust 49.311077 70.86765 -21.556573 21.556573 Zfp128 72.55719 94.02574 -21.46855 21.46855 Gab2 49.851818 71.27552 -21.423702 21.423702 Gpr44 3.5076904 24.904285 -21.396595 21.3965946 Abcb11 29.77381 51.149895 -21.376085 21.376085 Lamc2 57.830788 79.15549 -21.324702 21.324702 Actl6a 31.095509 52.245552 -21.150043 21.150043 Nxph4 21.499207 42.640026 -21.140819 21.140819 Fbxo11 48.21911 69.30433 -21.08522 21.08522 Acaca 64.29448 85.36447 -21.06999 21.06999 Shisa7 67.75258 88.805 -21.05242 21.05242 Mzf1 20.193846 41.18647 -20.992624 20.992624 Nrip3 43.175716 64.16188 -20.986164 20.986164 Smg6 32.488243 53.467865 -20.979622 20.979622 Rras 26.94102 47.89612 -20.9551 20.9551 Wdr65 45.68741 66.600464 -20.913054 20.913054 Slc35f3 41.28801 62.141705 -20.853695 20.853695 Abhd8 53.63418 74.48488 -20.8507 20.8507 Chrm2 30.68629 51.52544 -20.83915 20.83915 Asap1 37.471 58.303894 -20.832894 20.832894 Tlr9 75.931854 96.65142 -20.719566 20.719566 Ccdc113 22.77382 43.492954 -20.719134 20.719134 Sox5 74.39683 95.066666 -20.669836 20.669836 Casz1 58.835564 79.49771 -20.662146 20.662146 Asph 56.207756 76.84492 -20.637164 20.637164 Elfn1 21.22194 41.85665 -20.63471 20.63471 Zranb3 55.066875 75.62007 -20.553195 20.553195 Ttc28 24.34497 44.810913 -20.465943 20.465943 Arhgef10 34.590687 54.95857 -20.367883 20.367883 114 Título de la tesis o trabajo de investigación

Hhatl 35.20701 55.466125 -20.259115 20.259115 Ahdc1 36.229282 56.387245 -20.157963 20.157963 Sipa1l1 60.975563 81.11524 -20.139677 20.139677 Nxn 10.713954 30.83081 -20.116856 20.116856 Aff3 26.762737 46.876167 -20.11343 20.11343 Bcl2l1 36.270885 56.364624 -20.093739 20.093739 Prok2 28.965338 49.057972 -20.092634 20.092634 Lima1 60.111164 80.16811 -20.056946 20.056946 Capn9 7.157008 27.161585 -20.004577 20.004577 Il3ra 78.04941 58.042053 20.007357 -20.007357 Them7 61.516388 41.49394 20.022448 -20.022448 Mgme1 62.860962 42.80535 20.055612 -20.055612 Cntn6 68.38179 48.312958 20.068832 -20.068832 Wdr81 30.75879 10.67525 20.08354 -20.08354 Sytl3 48.05741 27.973455 20.083955 -20.083955 Aebp2 56.886364 36.78527 20.101094 -20.101094 Lct 90.86555 70.76439 20.10116 -20.10116 Dnaic1 55.851326 35.741074 20.110252 -20.110252 Pard3b 62.696606 42.585823 20.110783 -20.110783 Txnrd2 47.26638 27.148031 20.118349 -20.118349 Iqcm 60.398396 40.267914 20.130482 -20.130482 H2-M3 26.183222 6.0108886 20.1723334 -20.172333 Hmg20b 58.9546 38.727287 20.227313 -20.227313 Agtr1a 29.085361 8.846518 20.238843 -20.238843 5S_rRNA 52.490074 32.217155 20.272919 -20.272919 Ap1g2 51.835167 31.55419 20.280977 -20.280977 Rtn1 37.343014 17.010437 20.332577 -20.332577 Npnt 31.127117 10.791776 20.335341 -20.335341 Itpr2 37.095795 16.720158 20.375637 -20.375637 Mxra7 40.30935 19.92738 20.38197 -20.38197 Ptprn2 82.30205 61.901794 20.400256 -20.400256 Dscaml1 54.682686 34.24726 20.435426 -20.435426 Gnaq 55.862904 35.384865 20.478039 -20.478039 B4galnt4 30.865551 10.35215 20.513401 -20.513401 Gpr3 76.46916 55.898304 20.570856 -20.570856 Btbd9 73.842285 53.260345 20.58194 -20.58194 Pzp 28.906734 8.266245 20.640489 -20.640489 Tmem233 36.740433 16.076197 20.664236 -20.664236 Peli2 32.580383 11.904687 20.675696 -20.675696 Anexo A. Nombrar el anexo A de acuerdo con su contenido 115

Arid5b 32.522743 11.808767 20.713976 -20.713976 Slc20a2 70.369774 49.65213 20.717644 -20.717644 Slc39a14 55.01952 34.257713 20.761807 -20.761807 Arhgap20 35.087566 14.308536 20.77903 -20.77903 Asb3 37.03129 16.227287 20.804003 -20.804003 Rap1gap 51.62265 30.815313 20.807337 -20.807337 Rims2 40.737576 19.930162 20.807414 -20.807414 Ikzf1 64.1817 43.36668 20.81502 -20.81502 Ccbl2 52.64161 31.777012 20.864598 -20.864598 Plaur 59.44029 38.575535 20.864755 -20.864755 Nox3 68.526306 47.658012 20.868294 -20.868294 Ctif 65.79794 44.922283 20.875657 -20.875657 Itln1 84.8246 63.941917 20.882683 -20.882683 Slc29a3 36.37916 15.494669 20.884491 -20.884491 Rxfp1 26.03515 5.1238637 20.9112863 -20.911286 Arhgap4 90.12437 69.15878 20.96559 -20.96559 Ccdc149 67.76842 46.79859 20.96983 -20.96983 Eif4ebp1 57.071472 36.09831 20.973162 -20.973162 Bahcc1 36.771206 15.771393 20.999813 -20.999813 Ssr1 51.70208 30.701199 21.000881 -21.000881 Eif2ak1 45.868397 24.787699 21.080698 -21.080698 Nuak1 41.46157 20.280544 21.181026 -21.181026 Smarca2 74.157715 52.97059 21.187125 -21.187125 Pigk 74.73438 53.54416 21.19022 -21.19022 Aldh3a1 47.517204 26.271076 21.246128 -21.246128 Pgls 44.965305 23.674412 21.290893 -21.290893 Dusp27 72.589096 51.247833 21.341263 -21.341263 Rai1 47.280365 25.885391 21.394974 -21.394974 Kcnq2 82.36421 60.91112 21.45309 -21.45309 L1cam 60.379932 38.815754 21.564178 -21.564178 Prkce 60.30433 38.6989 21.60543 -21.60543 Cmya5 36.355717 14.709697 21.64602 -21.64602 Tgfbr3 51.820923 30.13823 21.682693 -21.682693 Aen 27.633656 5.9354234 21.6982326 -21.698233 Abca12 39.27718 17.557709 21.719471 -21.719471 Isg20 57.69964 35.965843 21.733797 -21.733797 Spata13 67.30277 45.493366 21.809404 -21.809404 Slc38a5 38.07987 16.25762 21.82225 -21.82225 Plch2 38.56791 16.71292 21.85499 -21.85499 116 Título de la tesis o trabajo de investigación

Golim4 54.00305 32.094105 21.908945 -21.908945 Atp2b2 71.06311 49.15034 21.91277 -21.91277 Cep164 82.17018 60.24418 21.926 -21.926 F5 42.546722 20.577108 21.969614 -21.969614 Slit3 54.846344 32.82943 22.016914 -22.016914 Spats2l 82.817825 60.783222 22.034603 -22.034603 Arhgef3 69.69737 47.633804 22.063566 -22.063566 Adam23 58.554333 36.45682 22.097513 -22.097513 Pappa2 35.914528 13.788963 22.125565 -22.125565 Sash1 66.221054 44.09342 22.127634 -22.127634 Eya2 47.529716 25.401888 22.127828 -22.127828 Nphp4 65.963585 43.807636 22.155949 -22.155949 Epb4.1l4a 27.692957 5.516402 22.176555 -22.176555 Zdhhc15 62.338276 40.15292 22.185356 -22.185356 Gabrb3 85.850204 63.660896 22.189308 -22.189308 Ccbe1 94.58981 72.392586 22.197224 -22.197224 Cyp2c40 57.81256 35.585857 22.226703 -22.226703 Atp1b1 56.93418 34.63798 22.2962 -22.2962 Pcdhac1 43.56309 21.24228 22.32081 -22.32081 Dact3 75.03797 52.68394 22.35403 -22.35403 Zfhx4 43.782085 21.379915 22.40217 -22.40217 Inf2 52.67467 30.198303 22.476367 -22.476367 Ralyl 81.90319 59.38259 22.5206 -22.5206 Cadps2 89.51904 66.982735 22.536305 -22.536305 Reep6 65.811935 43.268414 22.543521 -22.543521 Zfp35 53.55948 30.980946 22.578534 -22.578534 Fam19a5 65.0123 42.42121 22.59109 -22.59109 Fam83b 69.22297 46.589893 22.633077 -22.633077 Slc26a5 48.25951 25.609634 22.649876 -22.649876 Pfkfb4 48.53363 25.851923 22.681707 -22.681707 Sema3f 65.15435 42.460754 22.693596 -22.693596 Erbb4 39.519836 16.820988 22.698848 -22.698848 Adm 88.11222 65.36299 22.74923 -22.74923 Grhl2 45.513973 22.760712 22.753261 -22.753261 Brd3 72.01924 49.260185 22.759055 -22.759055 Myo16 72.30427 49.503345 22.800925 -22.800925 Arhgef17 38.100815 15.275968 22.824847 -22.824847 Rnf152 58.37218 35.538994 22.833186 -22.833186 Susd4 42.06179 19.167404 22.894386 -22.894386 Anexo A. Nombrar el anexo A de acuerdo con su contenido 117

Nol3 58.496906 35.593224 22.903682 -22.903682 Bsn 39.845364 16.9202 22.925164 -22.925164 Shank3 26.008753 3.0795877 22.9291653 -22.929165 Cmss1 49.053116 26.059368 22.993748 -22.993748 Bcl9l 74.245865 51.246082 22.999783 -22.999783 Dnmbp 44.86257 21.765194 23.097376 -23.097376 Gria1 86.98134 63.839775 23.141565 -23.141565 Sgcd 48.477364 25.327974 23.14939 -23.14939 Plcb4 72.79271 49.616516 23.176194 -23.176194 Tmtc2 89.18605 66.00304 23.18301 -23.18301 Rassf4 79.30277 56.079918 23.222852 -23.222852 Pde1a 73.72078 50.489784 23.230996 -23.230996 Atn1 49.88083 26.649775 23.231055 -23.231055 Rbfox3 57.246826 33.83189 23.414936 -23.414936 Adam22 50.2333 26.814777 23.418523 -23.418523 Triml1 57.759357 34.31138 23.447977 -23.447977 Basp1 41.545776 18.071087 23.474689 -23.474689 Thbs4 36.576912 13.0998535 23.4770585 -23.477059 Astn1 65.32521 41.80655 23.51866 -23.51866 Nebl 49.373814 25.831583 23.542231 -23.542231 Armc2 34.909016 11.335097 23.573919 -23.573919 Nedd4 51.213158 27.619457 23.593701 -23.593701 Fzd5 64.27022 40.666065 23.604155 -23.604155 Spsb1 65.471664 41.852608 23.619056 -23.619056 Pcsk6 39.48001 15.848343 23.631667 -23.631667 Nav2 93.17171 69.50936 23.66235 -23.66235 Astn2 65.40613 41.740707 23.665423 -23.665423 Tmx2 77.42901 53.719948 23.709062 -23.709062 Phldb1 56.599022 32.842903 23.756119 -23.756119 Srebf2 90.70325 66.92263 23.78062 -23.78062 Clstn2 78.416885 54.51659 23.900295 -23.900295 Olfr134 67.980125 44.062782 23.917343 -23.917343 Fras1 67.29383 43.335365 23.958465 -23.958465 Mid1ip1 46.84865 22.822063 24.026587 -24.026587 Dab1 69.951096 45.851612 24.099484 -24.099484 Traf6 50.797268 26.519321 24.277947 -24.277947 Unc79 47.88639 23.606676 24.279714 -24.279714 Slc47a1 60.952656 36.672497 24.280159 -24.280159 Map2k6 50.402107 26.034826 24.367281 -24.367281 118 Título de la tesis o trabajo de investigación

Pqlc1 84.96816 60.58894 24.37922 -24.37922 Pde2a 93.75246 69.36536 24.3871 -24.3871 Tnxb 45.517693 21.089764 24.427929 -24.427929 Bre 92.7724 68.319916 24.452484 -24.452484 Tbc1d24 41.79841 17.335922 24.462488 -24.462488 Rerg 61.765663 37.267387 24.498276 -24.498276 Ifltd1 78.590675 54.018036 24.572639 -24.572639 Rgs20 77.38673 52.769615 24.617115 -24.617115 Itm2c 69.71066 45.084816 24.625844 -24.625844 Prelid2 83.10679 58.47489 24.6319 -24.6319 Kirrel3 53.723675 29.09029 24.633385 -24.633385 Zc3h12d 46.102913 21.46896 24.633953 -24.633953 Unc119 55.759644 31.098047 24.661597 -24.661597 Trpv4 84.4807 59.80719 24.67351 -24.67351 Elmod3 75.39762 50.635735 24.761885 -24.761885 Snora17 75.151665 50.353607 24.798058 -24.798058 St8sia1 54.42179 29.555841 24.865949 -24.865949 Rgs22 54.24926 29.35964 24.88962 -24.88962 Orm3 51.210407 26.312342 24.898065 -24.898065 Prkag2 38.25539 13.336493 24.918897 -24.918897 Cbln4 60.397278 35.394737 25.002541 -25.002541 Mapk10 85.102 60.052147 25.049853 -25.049853 Osbpl7 29.502804 4.3953876 25.1074164 -25.107416 Rapgef4 59.082733 33.971718 25.111015 -25.111015 Sdk1 62.551094 37.36928 25.181814 -25.181814 Cdkl1 53.753235 28.547892 25.205343 -25.205343 Txlnb 91.57879 66.339584 25.239206 -25.239206 Tnfrsf21 64.22502 38.923115 25.301905 -25.301905 Corin 67.38681 42.034565 25.352245 -25.352245 Igsf5 33.31715 7.876063 25.441087 -25.441087 Plppr5 52.35982 26.913387 25.446433 -25.446433 Rnh1 40.781193 15.306906 25.474287 -25.474287 Scarb2 61.78191 36.243385 25.538525 -25.538525 Stox2 66.92543 41.382347 25.543083 -25.543083 Fap 40.90676 15.227531 25.679229 -25.679229 Armc1 64.26097 38.552174 25.708796 -25.708796 Nbeal2 58.323437 32.56325 25.760187 -25.760187 H2afy2 54.597626 28.790298 25.807328 -25.807328 Heg1 50.73954 24.859085 25.880455 -25.880455 Anexo A. Nombrar el anexo A de acuerdo con su contenido 119

Fbn2 56.47246 30.4797 25.99276 -25.99276 Arhgap33 78.23003 52.2266 26.00343 -26.00343 Apba1 50.478756 24.437708 26.041048 -26.041048 Kdsr 61.746254 35.53413 26.212124 -26.212124 Sorcs3 36.732666 10.511948 26.220718 -26.220718 Smad9 61.45542 35.19474 26.26068 -26.26068 Sgms2 54.81681 28.408869 26.407941 -26.407941 Gadl1 39.914345 13.503558 26.410787 -26.410787 Evc 80.9729 54.41732 26.55558 -26.55558 Prkca 65.74668 39.09245 26.65423 -26.65423 ccdc198 58.127068 31.471981 26.655087 -26.655087 Ldlrad3 90.572525 63.867767 26.704758 -26.704758 Vwf 79.99132 53.260803 26.730517 -26.730517 Dlgap2 88.39146 61.627056 26.764404 -26.764404 Ccdc109a 48.20344 21.432943 26.770497 -26.770497 Ppp1r9a 56.35032 29.44899 26.90133 -26.90133 Dnahc6 52.53496 25.601444 26.933516 -26.933516 Dysf 44.301765 17.318813 26.982952 -26.982952 Edar 55.033295 28.01994 27.013355 -27.013355 Arap1 69.20114 42.117363 27.083777 -27.083777 Dnahc7c 63.633068 36.524727 27.108341 -27.108341 Ids 52.606365 25.434998 27.171367 -27.171367 U6 55.390038 28.179682 27.210356 -27.210356 U7 81.32486 54.08482 27.24004 -27.24004 Helz 62.98222 35.732674 27.249546 -27.249546 Etl4 86.99852 59.694256 27.304264 -27.304264 Slc27a6 38.41195 11.05052 27.36143 -27.36143 Enox2 65.47774 38.107796 27.369944 -27.369944 Kcne2 84.19872 56.78411 27.41461 -27.41461 Lonrf1 76.0381 48.485554 27.552546 -27.552546 Pik3c3 87.15874 59.41039 27.74835 -27.74835 Cyp4f17 64.37544 36.62351 27.75193 -27.75193 Mrps6 73.07036 45.16848 27.90188 -27.90188 Gpr101 67.46492 39.403664 28.061256 -28.061256 Trmt2b 58.013275 29.94959 28.063685 -28.063685 Epb4.1l4b 66.344696 38.25376 28.090936 -28.090936 Fmr1nb 86.58735 58.494232 28.093118 -28.093118 Galnt13 79.89007 51.74947 28.1406 -28.1406 Sema3a 47.16432 18.938894 28.225426 -28.225426 120 Título de la tesis o trabajo de investigación

Rims1 74.751434 46.414154 28.33728 -28.33728 Fgf12 65.074844 36.697258 28.377586 -28.377586 Ppp3ca 52.03374 23.641869 28.391871 -28.391871 Vmn2r26 43.59944 15.146929 28.452511 -28.452511 Glrb 46.756813 17.90069 28.856123 -28.856123 Serpinb2 87.84456 58.93984 28.90472 -28.90472 Cntn4 48.658527 19.640982 29.017545 -29.017545 Osbpl6 37.966534 8.909898 29.056636 -29.056636 Sgcz 53.042076 23.970058 29.072018 -29.072018 Mex3b 54.03559 24.954493 29.081097 -29.081097 Immp2l 44.70683 15.607701 29.099129 -29.099129 Rbms1 56.604267 27.396235 29.208032 -29.208032 Nfix 58.780407 29.47634 29.304067 -29.304067 Serpinb3b 71.67839 42.303032 29.375358 -29.375358 Ctnnd2 57.26194 27.85146 29.41048 -29.41048 Arhgef2 39.06117 9.621214 29.439956 -29.439956 Kcnn2 75.537 45.80538 29.73162 -29.73162 Grid2 56.269264 26.489594 29.77967 -29.77967 Fam129c 45.42669 15.407582 30.019108 -30.019108 Akna 83.24473 53.127636 30.117094 -30.117094 Aff2 82.720856 52.477783 30.243073 -30.243073 Ncor1 81.39579 51.06298 30.33281 -30.33281 Sorl1 70.9856 40.548195 30.437405 -30.437405 Lipo1 58.2619 27.765451 30.496449 -30.496449 Cntnap5c 60.595947 30.027035 30.568912 -30.568912 Csnka2ip 91.17833 60.572865 30.605465 -30.605465 Hs3st1 74.09633 43.385864 30.710466 -30.710466 Kif26b 62.59203 31.706894 30.885136 -30.885136 Mdga2 84.95886 53.97357 30.98529 -30.98529 Cdh8 56.09847 24.781172 31.317298 -31.317298 Zfpm2 72.111824 40.629814 31.48201 -31.48201 Fat3 81.627396 50.07987 31.547526 -31.547526 Olfr132 86.44284 54.883575 31.559265 -31.559265 Gca 53.370403 21.764013 31.60639 -31.60639 Cbx4 66.19249 34.5819 31.61059 -31.61059 Firre 72.02194 40.311176 31.710764 -31.710764 St3gal4 52.038887 20.29115 31.747737 -31.747737 Ltbp3 57.582367 25.807886 31.774481 -31.774481 Trim34a 53.683983 21.682447 32.001536 -32.001536 Anexo A. Nombrar el anexo A de acuerdo con su contenido 121

Sema5b 42.79315 10.714118 32.079032 -32.079032 Lrrc52 52.978577 20.862444 32.116133 -32.116133 Plec 91.01145 58.758636 32.252814 -32.252814 Dgki 62.099663 29.75438 32.345283 -32.345283 Atxn7l1 80.01241 47.42219 32.59022 -32.59022 Bbx 61.563637 28.889496 32.674141 -32.674141 Gm8817 62.213326 29.512478 32.700848 -32.700848 Nmnat3 53.278847 20.185963 33.092884 -33.092884 Fat4 73.270775 39.94088 33.329895 -33.329895 Hecw2 72.50787 39.08953 33.41834 -33.41834 Epb4.1l3 87.08014 53.472145 33.607995 -33.607995 Trpc5 66.91706 33.228184 33.688876 -33.688876 Odz3 47.512264 13.7518215 33.7604425 -33.760443 Gpd2 52.46929 18.60325 33.86604 -33.86604 Insr 76.13262 42.183495 33.949125 -33.949125 Mphosph10 73.63705 39.646004 33.991046 -33.991046 Dmd 41.046787 6.606276 34.440511 -34.440511 Dnahc14 85.06536 50.414745 34.650615 -34.650615 Npas3 54.12925 19.460363 34.668887 -34.668887 Foxp2 66.86081 32.070297 34.790513 -34.790513 Irak3 61.390938 26.482883 34.908055 -34.908055 Csmd1 78.64691 43.3534 35.29351 -35.29351 Tmeff2 49.54487 14.1890335 35.3558365 -35.355837 Tnc 55.121967 19.740305 35.381662 -35.381662 Krtap20-2 73.99411 37.82206 36.17205 -36.17205 Frmpd4 70.28233 33.741245 36.541085 -36.541085 Zfp385b 72.35873 35.520504 36.838226 -36.838226 Atp8b2 61.858673 25.017197 36.841476 -36.841476 Dnph1 60.15135 23.22435 36.927 -36.927 St6galnac3 62.07777 24.955656 37.122114 -37.122114 Ptpn3 94.48595 57.15885 37.3271 -37.3271 Camk4 67.96862 29.754047 38.214573 -38.214573 Tcf4 50.800396 12.442578 38.357818 -38.357818 Asb15 87.56206 48.974022 38.588038 -38.588038 Tlr8 60.639366 21.903622 38.735744 -38.735744 Gnat3 65.81462 26.767935 39.046685 -39.046685 U1 82.2284 43.04421 39.18419 -39.18419 Ank2 49.882202 10.66699 39.215212 -39.215212 Arhgap15 62.833733 23.456306 39.377427 -39.377427 122 Título de la tesis o trabajo de investigación

Ptprq 61.822186 22.42206 39.400126 -39.400126 Dync1i1 82.87976 43.187874 39.691886 -39.691886 Ptgfr 82.439255 42.741047 39.698208 -39.698208 Tspan12 58.20941 18.260311 39.949099 -39.949099 Cdh13 92.47076 52.309227 40.161533 -40.161533 Vit 89.566925 48.84851 40.718415 -40.718415 Ddx60 73.95606 32.88706 41.069 -41.069 Runx2 53.021935 11.643395 41.37854 -41.37854 Mid1 92.154526 50.59986 41.554666 -41.554666 Olfr721-ps1 82.0056 38.341534 43.664066 -43.664066 Amph 95.8403 51.588074 44.252226 -44.252226 Grik4 73.84713 29.298626 44.548504 -44.548504 Gpr98 79.325005 31.88355 47.441455 -47.441455 Fam190a 77.91483 28.208828 49.706002 -49.706002 Htr4 79.218 29.333185 49.884815 -49.884815 Nyap2 78.993 24.833065 54.159935 -54.159935 Magi2 82.5959 27.512365 55.083535 -55.083535

Annex 2. Differentially expressed gene list

Feature P-value FDR Log2 Fold young_SC old_SC Change Cops5 1.55E-05 0.00334239 -0.2229819 45.49782 52.421265 Tceb1 1.44E-04 0.01406006 -0.2718487 601.3372 716.8035 Tmem70 0.00118698 0.04885528 -0.1799773 75.21413 84.27882 Rims1 5.16E-04 0.02975986 -0.2131505 12.2879305 14.125654 Eif5b 9.33E-04 0.04179294 -0.1793367 29.85513 33.484596 Pdcl3 2.36E-04 0.01915555 -0.1873643 53.338844 60.28634 Il1r2 4.11E-04 0.02605363 -0.3834774 19.037952 24.692614 Il1r1 9.38E-04 0.04191124 0.2474235 12.778282 10.702827 Mrps9 1.02E-04 0.01133396 -0.284293 11.871186 14.234996 Anexo A. Nombrar el anexo A de acuerdo con su contenido 123

Osgepl1 9.96E-04 0.04319927 -0.1862367 8.222059 9.246054 Coq10b 7.76E-06 0.00203188 -0.2910264 47.66465 57.66542 Plcl1 3.50E-04 0.024668 0.3145255 6.028924 4.8270845 Ppil3 6.46E-06 0.00178284 -0.3634331 44.875763 56.928898 2810408I11Rik 1.51E-05 0.00332902 -0.2497772 48.1903 56.871136 Fn1 9.03E-04 0.04110332 -0.2668845 1.50134 1.8015053 Pecr 0.00117249 0.0484646 -0.107515 577.137 616.0135 Xrcc5 9.46E-04 0.04203907 -0.1360599 48.26106 52.5075 Gm16582 3.45E-04 0.02437896 1.7783542 0.22235163 0.06399233 9430031J16Rik 1.73E-07 1.16E-04 1.5793124 0.7281165 0.2437296 Pde6d 8.29E-04 0.03870694 -0.1728404 80.85088 90.25033 Serpinb12 5.07E-04 0.02941709 -0.28594 14.931913 18.001253 Ddx18 6.24E-04 0.03367334 -0.1757339 29.523123 33.07333 Ubxn4 7.63E-05 0.00954336 -0.1367682 48.88625 53.21305 Cxcr4 1.31E-05 0.00307211 1.594698 0.63352275 0.21051222 Kif21b 2.22E-05 0.00444678 0.552061 0.94704336 0.64652723 Dhx9 6.49E-04 0.03404373 0.42994955 3.181527 2.350493 Pigc 8.89E-04 0.0406599 -0.1892399 2.890774 3.2729545 Myoc 9.20E-04 0.04160949 -0.1521799 24.016842 26.390287 Prrc2c 7.44E-04 0.0366851 0.24659304 22.02189 18.466404 Tiprl 7.95E-04 0.03759207 -0.1713631 25.369417 28.224894 Uck2 5.14E-05 0.00756799 -0.2243638 69.187965 79.73772 Ddr2 2.85E-04 0.02135958 0.23157632 4.6820226 3.9474573 Exo1 1.12E-04 0.01199983 0.14363652 24.37149 21.988205 Kif26b 1.29E-04 0.01298341 0.9248227 0.5351606 0.29083568 Bpnt1 1.53E-04 0.01469711 0.62211096 1.6475084 1.0588129 Angel2 2.62E-04 0.02057331 -0.1485444 64.53073 70.84599 Rpp38 6.47E-04 0.03404373 -0.2128289 38.878952 44.72656 Gm13211 3.99E-04 0.02567281 0.8489297 3.3008776 1.6861436 Gata3 1.13E-04 0.0120458 -0.3072602 4.999172 6.09829 Entpd2 7.75E-04 0.03754466 0.5077906 1.1429106 0.7931944 Sec16a 3.65E-04 0.02494994 0.2280552 24.689426 20.946636 Gm13398 5.22E-06 0.00153233 0.5993201 3.6525087 2.3547332 Rab14 9.96E-08 7.15E-05 -0.1843413 72.20946 81.22414 Lrp1b 1.37E-05 0.00312516 0.39677072 2.1314266 1.5943986 Gm13481 6.03E-10 2.22E-06 1.1478708 1.8730596 0.80343646 Galnt13 4.18E-04 0.0262542 1.6808265 0.06521785 0.01871154 Gca 2.67E-05 0.00493214 1.3626053 0.8209195 0.3143526 Rapgef4 5.95E-04 0.03274053 -0.2545601 2.363648 2.8093417 124 Título de la tesis o trabajo de investigación

B230120H23Rik 2.20E-05 0.0044433 0.39272684 1.8379062 1.399025 Gm13752 1.36E-04 0.01342574 -0.2463364 39.315353 46.405422 Dusp19 1.23E-04 0.01264694 -0.1502028 93.25025 102.48481 Gm13713 2.71E-04 0.02084407 0.89087623 0.6466437 0.33484536 Gm13716 3.79E-04 0.02517325 1.5525055 0.6905689 0.2004939 Olfr1164 1.24E-05 0.00297886 1.0173732 2.433133 1.157607 Fnbp4 4.75E-06 0.00146123 -0.2221328 9.762713 11.276756 Gm10801 6.64E-07 3.22E-04 3.3635082 1.7165146 0.06018308 Gm10800 2.01E-11 1.95E-07 3.737456 12.622439 0.9889537 B230118H07Rik 6.85E-04 0.03540439 -0.338994 3.0442364 3.8342438 Ehf 3.25E-05 0.00551682 -0.1294619 65.71236 71.229355 2700007P21Rik 2.11E-04 0.01805841 -0.2155255 9.340801 10.759686 Slc5a12 1.16E-08 1.50E-05 0.7438967 3.6441708 2.163517 Aven 6.51E-05 0.0087397 -0.1556663 45.577774 50.26415 Grem1 2.55E-05 0.00476088 -2.969452 0.12622812 2.310017 Atpbd4 3.24E-06 0.00118117 -0.1706808 27.677652 30.902737 Meis2 1.46E-06 6.14E-04 -0.2521885 43.73592 51.525597 Rpap1 7.90E-04 0.03755615 0.2595492 3.228404 2.6963627 Vps39 4.96E-04 0.02936114 0.21554135 10.823463 9.250069 Serinc4 3.05E-04 0.02253676 -0.1791507 15.076726 16.884657 Serinc4 2.34E-05 0.00448648 -0.2093306 13.733205 15.704584 Wdr76 2.74E-04 0.02084963 -0.1813463 37.848503 42.622066 Dut 1.29E-04 0.01298341 -0.2082996 55.708584 63.830704 Macrod2 2.25E-04 0.01860396 -0.2327763 13.14593 15.231936 Snrpb2 7.50E-05 0.00944489 -0.2428041 36.40435 42.56691 Crnkl1 2.76E-04 0.02084963 -0.1346954 127.57345 138.91357 Gins1 5.15E-05 0.00756799 -0.2024452 32.721596 37.30398 Dynlrb1 2.25E-04 0.01860396 -0.1819265 97.16064 109.19981 Srsf6 4.67E-04 0.02836657 0.356321 4.329987 3.3423223 Gtsf1l 5.87E-04 0.0324294 1.1558069 1.0883573 0.44357416 3230401D17Rik 1.04E-04 0.0114376 -0.1249893 177.78867 191.82472 Znfx1 4.18E-04 0.0262542 0.20727228 14.842046 12.788667 Sall4 1.12E-04 0.01199983 0.2965086 14.774483 11.895318 Lama5 5.94E-05 0.00834584 0.3318521 4.765183 3.7651088 2700069I18Rik 6.62E-07 3.22E-04 1.6925209 1.6923323 0.5000232 Gm9733 3.11E-04 0.0226789 1.1488457 2.9284477 1.2090667 Sirpb1b 4.72E-05 0.00706069 0.83017015 1.8342965 0.99344784 Prkci 0.00105974 0.04505282 -0.297356 4.5007787 5.479175 Actl6a 1.81E-04 0.01636723 -0.1800133 23.562742 26.37856 Anexo A. Nombrar el anexo A de acuerdo con su contenido 125

Cetn4 5.37E-05 0.00782172 -0.5193752 1.0389051 1.4602216 Aadac 1.83E-05 0.00381126 0.28688326 59.954033 48.349026 E130311K13Rik 0.00102507 0.04397591 -0.3177316 3.2243388 3.9472976 Ccnl1 1.23E-04 0.01264694 -0.2166834 13.152841 15.0853615 Gm17402 3.11E-04 0.0226789 -0.5563071 1.9483103 2.7904441 Dap3 7.53E-04 0.03702858 -0.125497 43.268795 46.747818 Creb3l4 1.39E-04 0.01369922 -0.1841134 38.918 43.807518 Mov10 3.88E-05 0.00632311 0.54388136 1.5378304 1.0278121 Clcc1 1.68E-04 0.01546444 -0.1214902 69.89 75.253006 Gm9857 9.22E-04 0.04160949 -0.1954554 67.07916 76.18291 Cnn3 4.04E-06 0.00130545 -0.1648957 131.26456 145.72215 Prss12 6.36E-04 0.03398647 0.23722878 15.7182865 13.2647 Sec24b 1.57E-04 0.0149108 0.27406394 9.63915 7.9358706 Gstcd 7.88E-05 0.00959036 -0.1442909 20.454336 22.406584 Ppp3ca 7.46E-05 0.00944489 0.5285054 1.842753 1.2521067 Pdha2 0.00115511 0.04805351 0.8406988 1.04555 0.56837684 15-Sep 7.40E-04 0.0366851 -0.1802564 83.55123 93.75112 Lphn2 0.00108003 0.04551612 0.43469524 1.1733513 0.8685069 St6galnac3 2.43E-04 0.01958158 0.61211514 5.7897797 3.7486084 Tmem68 4.97E-04 0.02936114 -0.2913902 5.4476557 6.6106615 Gm11827 1.86E-07 1.17E-04 -0.3101485 83.4719 102.47254 Calb1 2.92E-05 0.00519037 0.61968213 4.526822 2.9139185 Ndufaf4 3.08E-04 0.0226789 -0.2241369 84.63812 97.65748 Pigo 2.33E-04 0.0189852 0.2643484 3.998128 3.3411384 Polr1e 1.25E-04 0.01270522 0.26603764 13.50597 11.178735 Rg9mtd3 4.29E-04 0.02664208 -0.1696304 28.319147 31.54132 Musk 0.00107726 0.04549844 -0.1639851 46.590824 51.63818 Dnajc25 9.65E-04 0.04227734 -0.6329368 0.2734486 0.4175053 Akna 7.88E-04 0.03755615 0.57392466 1.1385441 0.7548837 Tnc 5.94E-09 8.86E-06 3.9106736 0.48216328 0.03150562 C87499 6.39E-04 0.03399156 -0.186257 247.60457 279.10663 Dnajc6 4.52E-04 0.02763612 1.6961852 0.11479364 0.03566434 Btf3l4 2.13E-04 0.0181743 -0.1889328 28.306692 31.93733 Nrd1 3.72E-04 0.02502922 0.2148978 11.721621 10.033355 Nsun4 9.93E-04 0.04315488 -0.1038033 52.833466 56.372936 Dnajc8 1.94E-06 7.98E-04 -0.2242305 60.16708 69.59084 Fuca1 7.43E-04 0.0366851 -0.2223257 13.668614 15.721304 Sh2d5 2.17E-04 0.01831503 -0.1843075 39.300102 44.24456 C230096C10Rik 4.04E-04 0.02568613 0.30976662 4.241221 3.3989766 126 Título de la tesis o trabajo de investigación

Dhrs3 6.38E-06 0.00178284 0.61574477 5.6627097 3.6698072 Gm13145 9.24E-05 0.01061766 0.5055553 26.858347 18.63592 Wdr8 5.02E-04 0.02941709 0.2755178 3.9681811 3.2503693 Ski 3.15E-05 0.00545454 0.20072666 64.761055 55.97082 Sema3a 6.32E-04 0.0339161 0.8428295 0.5131001 0.27258766 Rheb 5.06E-04 0.02941709 -0.1872022 16.453379 18.574516 Lmbr1 3.69E-06 0.0012129 -0.1845418 19.716965 22.204893 Gckr 2.05E-04 0.017984 -0.2296888 19.76589 22.965853 Slbp 2.47E-06 9.59E-04 -0.2747651 226.0488 270.44888 Ube2k 7.56E-04 0.03711783 -0.1074959 63.263596 67.68642 Corin 1.57E-04 0.0149108 1.7488053 0.16953601 0.0485126 1700112J05Rik 6.23E-05 0.00850124 -0.4673444 5.316759 7.2941246 Shroom3 1.11E-04 0.01199983 0.24464983 5.014393 4.194564 AA792892 8.29E-04 0.03870694 -0.1984344 111.02044 125.705055 Gm7919 1.33E-05 0.00307211 -0.2373824 41.765392 48.984863 Gm8258 6.27E-04 0.03375753 -0.6927143 5.5927315 9.080949 Abcg3 5.98E-04 0.03274053 -1.0054595 0.23820141 0.47275662 Acacb 5.04E-04 0.02941709 0.3250509 5.119773 4.048009 Fbxw8 6.54E-05 0.0087397 0.35808247 7.5225487 5.829231 Oas1f 2.46E-05 0.0046311 -0.294062 26.946777 32.923904 Gm15800 0.00105464 0.04499689 0.29341426 2.78176 2.2552307 Arpc3 3.29E-06 0.00118117 -0.2332519 118.05269 137.30365 Denr 7.65E-04 0.03730889 -0.2268799 7.641202 8.796273 Vps37b 4.58E-05 0.00699289 -0.2373301 18.543095 21.754814 Eif2b1 4.77E-04 0.02880029 -0.164925 196.25502 217.68106 Zfp664 0.00121253 0.04959074 0.17737636 41.04115 36.05466 Piwil1 1.41E-05 0.0031734 -0.2551217 8.68842 10.267082 Tsc22d4 5.09E-04 0.02945226 0.19660977 5.421093 4.696396 Mtus2 2.88E-05 0.0051633 -0.2159201 10.768498 12.392087 4930588N13Rik 0.00114185 0.04770658 -0.2012932 69.171715 78.56251 Dync1i1 3.61E-06 0.00120777 0.9223845 2.43651 1.2563266 Thsd7a 1.60E-04 0.01508497 0.1745307 5.6932535 5.009695 Cttnbp2 6.07E-08 4.70E-05 1.2254537 0.79998934 0.3395538 Chchd3 9.58E-04 0.04227734 -0.1502549 19.425247 21.277477 Fam115a 1.44E-05 0.00321794 1.2687683 0.2577907 0.10293514 Arhgef5 6.18E-05 0.00849467 1.7624047 0.14216341 0.04547649 Ggct 7.26E-04 0.0361986 -0.1588497 38.71001 42.6968 Tnip3 3.67E-04 0.02494994 -0.3022254 3.3696394 4.054904 Vmn1r35 8.14E-04 0.03831844 1.3823069 0.75513744 0.27582258 Anexo A. Nombrar el anexo A de acuerdo con su contenido 127

Mrpl35 4.22E-04 0.02637471 -0.1676532 32.45493 36.095314 St3gal5 0.00106874 0.04533628 -0.2822833 13.982201 16.93427 Vamp8 2.69E-04 0.02079022 -0.1736962 31.059872 34.630516 Dctn1 9.86E-04 0.04303286 0.25470617 2.5619557 2.1262956 Snrnp27 4.88E-06 0.00147893 -0.230821 46.009834 53.507687 Lsm3 3.02E-05 0.00531627 -0.2704508 71.618 85.578026 C130022K22Rik 1.75E-04 0.01589283 -0.1979684 4.0960526 4.640617 Eogt 6.45E-04 0.03404373 0.3711214 1.938281 1.4788208 Setmar 8.61E-06 0.00222618 -0.4267796 8.043429 10.777772 Atg7 5.21E-06 0.00153233 -0.3170852 3.6732233 4.5411987 1500001M20Rik 4.29E-05 0.00670343 -0.3129141 3.9272585 4.809294 H1foo 9.58E-04 0.04227734 -0.1542113 2324.4675 2563.265 Ninj2 7.08E-04 0.0361522 -0.1440755 429.4344 469.94446 Atp6v1e1 3.11E-06 0.00118065 -0.1783117 168.96695 188.92638 M6pr 5.62E-04 0.03146055 0.21231714 6.3366394 5.447728 AC122511.1 8.39E-04 0.03882384 -1.6730328 0.2462942 0.79129905 Lrmp 0.00113104 0.04735695 -0.4764293 0.62828076 0.8698513 Sspn 7.26E-04 0.0361986 -0.162416 13.694005 15.252258 Tmtc1 5.47E-05 0.00790867 -0.2599781 47.353603 56.23115 Dennd5b 2.65E-04 0.02061545 0.6558784 0.3916666 0.24652548 AU018091 2.96E-10 1.44E-06 0.86978996 4.401931 2.3839197 Myadm 1.23E-05 0.00297886 0.49125978 5.6730943 3.9986377 Lair1 3.40E-05 0.00562552 -0.4368833 1.7960173 2.3916085 Ppp6r1 5.33E-04 0.03014809 0.56852 1.6192821 1.1082438 Zfp418 3.43E-08 3.33E-05 0.89149356 1.9868538 1.0256964 Trim28 0.00102411 0.04397591 0.25092456 15.839008 13.215387 Gpr77 5.22E-04 0.02985072 1.3468183 0.59944165 0.23324268 Prr24 0.00101371 0.04376798 0.6630039 1.2013999 0.7187031 Psg17 1.98E-08 2.27E-05 0.74884784 8.124136 4.8315077 Sympk 0.0011451 0.04773954 0.27195203 4.75044 3.919806 Tdrd12 1.13E-09 2.44E-06 -0.2575459 180.11311 213.0952 Pdcd5 8.33E-04 0.03874488 -0.223749 24.419413 28.105848 Mtag2 3.59E-04 0.02494994 0.8638777 1.148166 0.6259557 Abhd2 4.33E-08 3.65E-05 0.42527696 4.398036 3.26317 Furin 4.18E-04 0.0262542 0.25970823 6.804713 5.6507754 Fam103a1 3.79E-04 0.02517325 -0.2068566 24.334768 27.781908 Rab38 8.90E-05 0.01032785 -0.1037717 202.09322 215.23631 Spcs2 3.45E-07 2.03E-04 -0.2129424 43.03057 49.370068 Pold3 8.33E-05 0.00972347 -0.1161603 96.70743 103.87996 128 Título de la tesis o trabajo de investigación

Numa1 6.63E-05 0.00880656 0.2507517 13.366574 11.14267 9030624J02Rik 6.07E-04 0.03296492 -0.1618045 22.417885 24.803082 Vwa3a 0.00102779 0.04398389 0.26887557 5.773144 4.7518826 Gtf3c1 6.53E-06 0.00178284 0.36865097 16.720333 12.878907 Taok2 9.90E-04 0.04314937 0.18388143 8.535623 7.469376 Fgfr2 9.64E-04 0.04227734 0.23412468 29.515364 24.946718 Ptdss2 1.47E-04 0.01421166 -0.3130895 4.963836 6.0888605 Osbpl5 9.30E-04 0.04174851 0.77251375 0.64862937 0.37123016 Ing1 2.80E-04 0.02106646 -0.1491831 25.710611 28.314724 Fgl1 8.98E-04 0.04094425 -0.7658379 0.31104288 0.5214661 Ufsp2 7.11E-04 0.0361522 -0.1820121 22.643164 25.34268 2700029M09Rik 2.14E-05 0.0043674 -0.2473221 201.37704 236.76648 Ddx60 2.11E-07 1.28E-04 1.531813 1.5956032 0.54843 Tktl2 1.95E-05 0.00401282 0.38628858 8.203822 6.262604 AC169518.1 1.50E-04 0.014469 0.3857271 13.24959 10.1278 Anapc10 7.66E-04 0.03730889 -0.3214899 15.056252 18.530428 Usp38 2.09E-04 0.01805841 0.32223603 28.660366 22.83037 Syce2 7.68E-05 0.00954452 -0.1666065 210.49663 233.82214 4921524J17Rik 8.36E-04 0.03875049 -0.1892063 69.53272 78.810745 Gm15679 3.35E-06 0.00118117 1.6869057 0.5291774 0.16285762 Gm15680 3.68E-04 0.02494994 1.3018007 0.25450256 0.09734999 Gm16156 4.28E-04 0.02664208 1.7603725 0.1349591 0.03398743 Cfdp1 3.67E-04 0.02494994 -0.1999334 87.52494 99.75671 Nudt7 3.31E-05 0.00552535 -0.2182232 127.881905 147.0824 Cenpn 2.39E-08 2.44E-05 -0.2230504 103.129234 119.33319 Mphosph6 1.55E-09 3.00E-06 -0.2137526 753.6948 865.5488 Pgbd5 7.26E-04 0.0361986 0.21016061 9.038485 7.8686676 Pcnxl2 3.56E-04 0.02488532 -0.5620735 0.42393884 0.61542004 8430410K20Rik 3.84E-04 0.02530449 -0.2406554 14.477939 16.922625 Cwc15 8.33E-04 0.03874488 -0.1746713 61.35566 68.74963 Fat3 2.86E-13 5.54E-09 0.73771423 0.93121463 0.5584426 Mbd3l2 2.45E-06 9.59E-04 -0.2881128 62.088154 75.027664 Keap1 2.27E-05 0.00448162 0.35450062 3.5287344 2.730915 Zfp653 1.65E-04 0.01534682 -0.2373757 16.835114 19.72475 Srpr 1.22E-04 0.01264694 -0.2395041 11.151006 13.0084 Robo3 1.02E-04 0.01133396 0.44204906 1.7586317 1.2786793 Olfr877 6.01E-07 3.15E-04 1.3163022 3.5611496 1.3920945 Olfr878 8.20E-07 3.79E-04 0.88691074 10.762117 5.787781 Fxyd6 6.36E-04 0.03398647 -0.5173336 10.76463 15.272647 Anexo A. Nombrar el anexo A de acuerdo con su contenido 129

Btg4 7.72E-04 0.03752458 -0.157334 1010.74023 1117.2795 Fbxo22 3.98E-04 0.02567281 -0.161914 19.880424 22.005861 1700017B05Rik 8.04E-05 0.00959036 0.51020116 12.27667 8.519905 Thsd4 2.18E-04 0.01831719 0.28128782 1.4164674 1.1632496 Pias1 1.05E-04 0.0114845 -0.1385162 74.72384 81.593895 Tln2 3.86E-04 0.02534077 0.3880327 1.286718 0.97774976 Nedd4 8.11E-09 1.12E-05 0.68741536 4.3788705 2.723833 Omt2a 1.70E-04 0.01553011 -0.1696724 258.5204 288.12027 Omt2b 6.02E-04 0.03286057 -0.1654704 872.30383 969.789 Topbp1 2.33E-04 0.0189852 0.20825502 18.491535 15.875037 Tlr9 7.33E-05 0.00935193 0.45866975 2.2689366 1.6656463 Rad54l2 4.31E-07 2.32E-04 0.32976148 4.8345437 3.8490374 Fbxw14 2.36E-05 0.00448648 -0.186832 292.06833 328.59915 Fbxw28 2.60E-04 0.02052599 -0.170084 420.4943 468.4118 Nme6 3.44E-04 0.02437896 -0.2687922 7.1992507 8.552993 Azi2 3.11E-04 0.0226789 -0.2855092 45.096443 54.327663 Rpsa 6.16E-04 0.0333629 0.21729243 36.858616 31.625515 Myrip 6.22E-06 0.00177244 0.2631539 17.710749 14.657916 Ust 6.33E-07 3.22E-04 -1.1653887 4.6143255 10.24626 Aig1 6.05E-04 0.03293434 -0.1763316 10.322264 11.534791 H60b 4.33E-06 0.00137189 -0.4778772 5.8499517 8.108185 Raet1c 1.01E-04 0.01130685 -0.3919354 12.432625 16.156399 Hddc2 6.00E-06 0.00173531 -0.3436534 3.5488129 4.5006366 Clvs2 3.75E-08 3.47E-05 0.28515744 13.360216 10.91757 Rpf2 3.21E-04 0.02319441 -0.2718296 19.16882 22.806097 Cep57l1 1.82E-04 0.01642943 -0.1398358 50.62977 55.43313 Mcm9 6.68E-04 0.03478552 -0.1891583 94.32469 106.233215 Ipmk 5.25E-04 0.02985072 -0.1267533 26.76495 28.974556 Cabin1 1.67E-04 0.01543634 0.27372915 17.965776 14.7583885 Trappc10 5.46E-04 0.03068866 0.19352798 8.962326 7.786889 Cirbp 0.00101109 0.04375227 -0.1653821 23.521358 26.062147 Hcfc2 3.44E-06 0.00119021 -0.4668054 1.2304758 1.6896088 Nfyb 6.80E-04 0.03526221 -0.2720062 3.0556335 3.634976 1700028I16Rik 5.56E-05 0.00798715 -0.4940198 3.4496987 4.808673 D10Wsu102e 9.42E-04 0.04199285 -0.1221522 401.70865 433.89804 Gm16176 7.08E-08 5.28E-05 0.9787232 7.1584 3.6018322 Actr6 6.77E-05 0.00892645 -0.6086072 1.9846213 2.9768412 Ndufa12 7.15E-06 0.00192556 -0.3866974 7.2610955 9.34558 Krr1 6.94E-04 0.03557638 -0.1839029 16.244839 18.294397 130 Título de la tesis o trabajo de investigación

Ptprr 7.80E-04 0.03754466 -0.1520994 12.431913 13.724809 Olfr805 4.97E-04 0.02936114 1.5983427 0.7696672 0.22528575 Mtmr3 3.65E-04 0.02494994 0.32782662 4.386792 3.4811509 Grb10 6.40E-04 0.03399156 0.2644194 3.8231258 3.1902115 Sertad2 0.00120515 0.0494979 -0.2939066 2.1152644 2.5927541 Mat2b 3.34E-04 0.0239688 -0.1390232 292.1754 318.22113 Dppa1 3.55E-04 0.02488532 -0.2948699 9.89196 12.049086 Sar1b 5.01E-04 0.02941709 0.34030324 77.8766 61.000668 Ube2b 2.59E-04 0.02052599 -0.2218953 47.95683 55.33071 Zcchc10 7.68E-06 0.00203188 -0.24916 160.1856 188.5337 Tnip1 7.97E-05 0.00959036 0.24996006 9.230049 7.6867423 Slc5a10 3.22E-06 0.00118117 0.22280689 36.029167 30.633495 Ubb 1.83E-05 0.00381126 -0.2033694 561.30884 640.08154 Dnahc9 7.29E-05 0.00935193 0.29572254 2.019909 1.6319872 Alox12 0.00116654 0.04836193 -0.2864527 2.1683233 2.6182783 Pelp1 0.00116751 0.04836193 0.18036331 22.508047 19.763103 Slc25a11 6.08E-05 0.00841432 -0.2290306 8.816877 10.261392 Glod4 2.09E-04 0.01805841 -0.2387022 5.568121 6.5153017 9130204K15Rik 2.03E-04 0.01792427 -0.1630056 95.706795 106.176674 Dusp14 3.65E-04 0.02494994 -0.1575719 103.77302 114.62569 4632419I22Rik 4.04E-04 0.02568613 0.31414062 2.0197682 1.602785 Utp18 8.81E-04 0.04047184 -0.2149242 34.523617 39.500755 Skap1 2.06E-04 0.01802921 -0.1374594 15.460512 16.79618 Psmd12 9.26E-05 0.01061766 -0.1501483 55.112923 60.548798 Cd300c 1.20E-04 0.01259503 0.34524614 11.55016 9.058445 Nup85 1.04E-04 0.0114376 -0.1745847 63.744663 71.180504 2310067B10Rik 8.29E-05 0.00972347 0.33722484 4.0379486 3.2079246 Fasn 5.27E-04 0.02985072 0.37223312 1.0441153 0.79954255 E2f6 3.61E-04 0.02494994 1.044609 0.629181 0.30355698 Asap2 4.73E-05 0.00706069 -0.1944978 32.774982 37.238777 Heatr5a 4.38E-04 0.02704937 0.24049267 6.356782 5.348005 Mdga2 4.02E-04 0.02568613 0.6463994 0.40693125 0.25519657 Tomm20l 1.20E-04 0.01259503 -0.2086556 325.91617 372.72415 Snapc1 2.37E-04 0.01915825 -0.1890185 20.772827 23.50767 Plekhg3 4.01E-05 0.0064165 0.24744447 25.632357 21.495214 Atp6v1d 3.44E-04 0.02437896 -0.1352258 87.67343 95.40391 Zfyve26 2.99E-04 0.0222331 0.29923287 4.581041 3.6995263 Med6 7.84E-04 0.03755615 -0.2211107 83.63621 96.25958 Npc2 3.27E-05 0.00551682 -0.2199607 19.109951 21.978344 Anexo A. Nombrar el anexo A de acuerdo con su contenido 131

2700073G19Rik 3.18E-04 0.02306609 -0.1909673 17.50119 19.66974 Psmc1 2.84E-05 0.00514045 -0.2155474 77.01446 88.35587 AK010878 1.61E-04 0.01508497 0.19213781 23.409328 20.361185 Tcl1b2 3.81E-05 0.0062515 -0.2547791 279.7308 330.80466 Kif26a 2.34E-06 9.45E-04 0.96424127 0.97643816 0.4992752 Jag2 1.10E-05 0.00277708 0.26977414 18.142187 14.931786 Prl8a2 4.61E-08 3.72E-05 1.9876556 8.82204 2.2230048 Cage1 7.66E-04 0.03730889 -0.1661102 27.523237 30.526928 Cage1 1.00E-06 4.40E-04 -0.1839302 163.09792 183.84863 A730081D07Rik 1.25E-04 0.01270522 -0.1899911 56.615013 64.09697 Ccdc90a 7.24E-04 0.0361986 -0.1682331 17.856842 19.871557 Gmpr 2.60E-04 0.02052599 -0.191761 59.72061 67.67263 Zfp169 2.91E-04 0.02173176 0.24641125 3.5072722 2.933571 Fgd3 2.66E-04 0.02061545 1.1513544 0.40014094 0.17937577 Trip13 8.28E-05 0.00972347 -0.3846163 3.6710925 4.7018137 Glrx 2.15E-04 0.0181849 -0.1239537 455.87402 491.8891 Gpr98 8.86E-07 3.99E-04 0.8967381 0.20989965 0.11105418 Fam169a 4.38E-04 0.02704937 -0.3046647 6.254522 7.561911 Cwc27 4.70E-04 0.0284518 -0.194083 18.113968 20.550957 Gm15286 4.04E-05 0.0064165 -0.244988 39.311253 45.978905 Isl1 8.08E-04 0.03812552 0.8691305 0.7800994 0.40965486 Zfp131 7.18E-05 0.00933951 -0.2422757 24.31637 28.485476 Flnb 9.66E-04 0.04227734 0.27520293 17.765873 14.586308 Abhd6 5.02E-04 0.02941709 -0.177943 25.548683 28.715807 3830406C13Rik 4.56E-04 0.02778113 -0.2609277 8.980132 10.631644 Nek10 3.56E-06 0.00120777 0.33624494 5.977619 4.723853 Adk 5.97E-04 0.03274053 -0.1428689 52.065277 57.012207 Nisch 8.23E-04 0.03865431 0.49203554 0.3705686 0.25996593 Phf7 7.83E-05 0.00959036 -0.2226665 15.521251 17.939117 Dph3 8.86E-04 0.04062415 0.18087429 4.9775634 4.2964864 Sh2d4b 6.00E-05 0.00836726 0.6535523 2.6535885 1.6756198 Gnpnat1 1.44E-04 0.01406006 -0.2357788 22.178385 25.863226 Apex1 1.32E-04 0.01316868 -0.1959999 24.460867 27.637098 Slc7a8 9.60E-05 0.01094756 0.4665813 3.3949785 2.4460516 D14Ertd668e 4.81E-04 0.02896423 -0.5457973 1.0562708 1.5296714 Cab39l 3.71E-04 0.02502922 -0.148578 32.401497 35.57491 Pinx1 7.79E-04 0.03754466 -0.2293605 17.451828 20.54098 Cpb2 5.45E-04 0.03068866 -0.2469502 17.944437 21.076698 Tsc22d1 5.19E-04 0.02975986 -0.1072539 67.896324 72.43634 132 Título de la tesis o trabajo de investigación

6720463M24Rik 1.87E-07 1.17E-04 -0.2333633 31.884329 37.314 Abcc4 2.35E-05 0.00448648 0.34460497 11.54406 9.0525675 Clybl 1.22E-05 0.00297886 -0.2226659 50.477497 58.180824 Brix1 9.64E-04 0.04227734 -0.2137979 101.20741 115.7711 Ctnnd2 4.04E-05 0.0064165 1.4428203 0.1865665 0.06166599 Zfp706 7.77E-04 0.03754466 -0.1321447 51.950806 56.358692 Zfpm2 1.63E-04 0.01521579 1.0773969 0.36936536 0.16672204 Nudcd1 3.24E-05 0.00551682 -0.2800975 61.627594 73.96199 Samd12 7.22E-04 0.0361986 -0.6686286 2.5955422 4.127843 Enpp2 9.99E-05 0.01125866 0.5198296 6.6005917 4.5632186 Plec 7.56E-07 3.57E-04 1.1837212 0.13291766 0.05874956 Foxred2 1.94E-04 0.01730136 0.2769355 19.596352 16.026056 Zc3h7b 3.88E-04 0.02539725 0.47604662 5.4038615 3.8808026 Poldip3 4.24E-05 0.00668765 0.2782455 9.559921 7.812313 A4galt 2.73E-04 0.02084963 0.38284233 8.360541 6.3349648 Ttc38 4.94E-04 0.02936114 0.5613344 1.162707 0.78907394 Plxnb2 4.94E-04 0.02936114 0.34699535 17.761202 13.838233 Sbf1 7.29E-04 0.0362326 0.34828496 4.3873816 3.4185226 Kif21a 1.33E-05 0.00307211 -0.6415999 0.7028403 1.08657 Senp1 1.15E-05 0.00286275 -0.2315357 21.441801 25.028309 Slc11a2 5.18E-04 0.02975986 -0.2564079 4.4075556 5.2206907 Map3k12 7.90E-04 0.03755615 0.7846436 8.108739 4.675095 Rsl1d1 0.00117759 0.04857175 -0.1500178 339.6084 372.91153 Mkl2 6.50E-04 0.03404373 0.19878794 11.154524 9.653952 0610037P05Rik 2.75E-05 0.00502388 -0.238894 17.951363 21.10289 Mzt2 4.49E-04 0.027547 -0.2250076 16.153122 18.621367 Pi4ka 7.14E-04 0.0361986 -0.2579757 12.696879 15.114527 Parl 5.76E-09 8.86E-06 -0.183639 100.668945 113.339485 Dppa4 6.93E-04 0.03557638 0.44366357 6.9038544 4.942279 Nfkbiz 2.01E-08 2.27E-05 0.73662937 12.412642 7.3707023 Gm813 5.92E-05 0.00834584 -0.2552768 1412.0164 1663.3586 E330017A01Rik 4.40E-04 0.02709389 -0.2961839 946.2314 1143.9125 Gabrr3 0.00107311 0.04542192 -0.2390256 18.232422 21.261656 ORF63 4.25E-07 2.32E-04 1.3868145 0.8839539 0.32233208 Sod1 3.78E-04 0.02517325 -0.1663463 946.1943 1052.7924 Dopey2 7.85E-05 0.00959036 0.33301136 5.700186 4.498779 C2cd2 5.72E-04 0.03195709 0.3337421 4.468639 3.5094535 Igf2r 3.73E-04 0.02503886 0.37295902 5.846322 4.4922566 Tbp 2.22E-04 0.01854395 -0.151275 10.171205 11.203786 Anexo A. Nombrar el anexo A de acuerdo con su contenido 133

Dcpp3 7.08E-05 0.00927527 0.6618377 11.3654585 6.91102 Pgp 8.51E-04 0.03929354 0.32521704 12.421439 9.845925 Hmga1 4.88E-04 0.02931602 0.2041196 16.10372 13.783991 Uhrf1bp1 3.90E-04 0.02544442 0.3712345 4.9184318 3.7750642 Srpk1 7.85E-04 0.03755615 0.12365793 47.662647 43.371204 BC004004 3.40E-04 0.02431703 -0.1663716 9.973909 11.061862 Dnahc8 9.48E-04 0.04203907 -0.1195037 6.5293264 7.0110188 Hsf2bp 3.63E-04 0.02494994 -0.1483532 30.009007 32.859512 Dhx16 5.82E-04 0.03224281 0.27235612 5.219955 4.3161044 A930015D03Rik 8.65E-04 0.03985102 -0.1751937 65.468506 73.10047 Znrd1as 1.23E-04 0.01264694 -0.2490472 20.666897 24.273119 Cyp39a1 5.26E-04 0.02985072 -0.2292694 10.793342 12.485219 Runx2 4.39E-06 0.00137189 1.0119128 0.4886698 0.23979114 AI314976 1.59E-05 0.00339125 -0.205447 37.06286 42.369633 Dazl 7.44E-10 2.22E-06 -0.3001243 307.00662 375.04636 2610034M16Rik 3.13E-05 0.00545454 0.41465378 21.073019 15.657716 Txndc2 2.10E-04 0.01805841 -0.1748386 80.774765 90.12175 Lpin2 1.95E-04 0.01730136 0.19949435 38.777008 33.519188 Lclat1 1.44E-10 9.30E-07 -0.2670813 14.928886 17.80418 Ehd3 1.07E-06 4.62E-04 0.62228143 3.1492026 2.0389366 Galm 2.76E-04 0.02084963 -0.1654757 66.40793 73.80813 Dhx57 7.09E-04 0.0361522 -0.1358033 14.657283 15.941032 Ston1 8.06E-05 0.00959036 -0.3328527 4.211339 5.261809 Dsg1a 2.56E-04 0.02043345 0.55350727 1.139116 0.75767666 2700062C07Rik 2.15E-04 0.0181849 -0.18838 135.7684 153.13762 Rit2 1.99E-04 0.01759428 0.4138638 1.8465184 1.3715866 Zmat2 5.75E-04 0.0320484 -0.1247166 25.583548 27.561502 Cdo1 2.66E-04 0.02061545 0.33294997 184.27893 145.00272 Sema6a 3.95E-04 0.02561929 0.48605645 1.2921808 0.92383933 Snx2 0.00110977 0.04656707 -0.1945957 24.786095 28.014505 C330018D20Rik 6.44E-05 0.00873183 -0.2576154 16.858402 19.915365 Ptpn2 9.14E-04 0.04151707 -0.1906184 72.26156 81.54304 Cep192 2.76E-04 0.02084963 0.29714054 8.224025 6.6588445 Rnmt 1.60E-04 0.01508497 -0.1333 12.5234375 13.640977 Ska1 9.52E-06 0.00242766 -0.2183233 52.166737 60.051365 BC031181 1.85E-04 0.01660911 -0.1705241 93.07147 103.61955 Rttn 5.82E-04 0.03224281 0.16840439 30.93007 27.241983 Syvn1 7.16E-04 0.0361986 0.28985772 14.051369 11.418368 Slc22a30 9.18E-10 2.22E-06 1.1062655 1.5463705 0.6954344 134 Título de la tesis o trabajo de investigación

Gnaq 7.20E-04 0.0361986 -0.2911477 3.2416148 3.929909 Lipo4 2.30E-05 0.00448648 1.6652778 0.5615626 0.17471848 Gm14446 6.54E-04 0.03419053 0.7679638 1.2581885 0.73344564 Dpcd 6.73E-04 0.03497174 -0.1928789 161.39226 182.87686 Arl3 0.00102533 0.04397591 -0.21426 30.80162 35.191364 Pcgf6 1.19E-04 0.01259503 -0.1489881 136.83437 150.32469 Sfr1 3.94E-07 2.25E-04 -0.2460087 326.19434 382.21933 Wdr96 0.00120924 0.04956082 0.29676872 12.118837 9.779429 Pdcd4 4.43E-05 0.0068666 -0.2500533 13.720318 16.098587 Vwa2 6.42E-04 0.03399156 0.26596826 13.752414 11.36073 Shroom4 4.63E-05 0.00700637 0.26866618 9.608724 7.9354215 Dgkk 3.24E-04 0.02331804 0.3319713 7.052587 5.5675387 Efhc2 4.29E-08 3.65E-05 0.4367057 27.264683 19.824276 Klhl13 1.50E-07 1.04E-04 0.2696193 30.161531 24.838772 Slc25a5 1.34E-04 0.01332138 -0.2118384 93.588066 107.26864 Ocrl 0.0010561 0.04499689 0.45623475 3.0733294 2.2246172 Hprt 3.93E-04 0.02557118 -0.2088168 58.492718 66.80605 4930550L24Rik 0.00109357 0.04598679 0.8211737 1.6711447 0.9194609 Xlr4a 2.26E-04 0.01860396 -0.6460695 10.416822 15.980607 Dkc1 2.45E-04 0.01965495 -0.197611 42.70832 48.58567 Tmem47 1.55E-05 0.00334239 0.45786473 7.440388 5.299706 Dmd 9.23E-04 0.04160949 0.31911907 0.769245 0.6078572 Ercc6l 6.91E-04 0.03557638 0.19612303 23.535793 20.402763 Tex16 4.57E-05 0.00699289 1.3498499 3.969699 1.5607468 Tex16 3.35E-09 5.90E-06 1.2463665 1.6462088 0.6922274 BC065397 4.00E-04 0.02567281 0.80353755 1.2690078 0.7271894 Chrdl1 7.23E-05 0.00934576 3.6638005 0.678617 0.0644281 Trpc5 5.77E-05 0.00822966 1.1896043 0.51475257 0.2216807 Il13ra2 9.75E-05 0.01105266 2.2823026 0.22107932 0.03006268 Tmem29 9.27E-04 0.04170361 -0.2103764 9.869166 11.256321 Huwe1 7.92E-04 0.03756201 0.2719655 6.2457557 5.150816 Mospd2 3.83E-04 0.02530449 0.32722136 5.455903 4.275379 Rab9 2.31E-04 0.0189852 -0.286226 14.695723 17.829931 Gm8817 2.10E-08 2.27E-05 2.6054485 10.336363 1.6744884 Tlr8 7.93E-05 0.00959036 1.9675465 1.2291485 0.31856287 Mid1 8.32E-10 2.22E-06 0.9587817 3.1049583 1.580906

Anexo A. Nombrar el anexo A de acuerdo con su contenido 135

Annex 3. Differentially expressed genes with significant methylation changes

gene young old Low_meth Hi_meth Tnc 3.75 0.24 15.3451311 0.06516725 Tlr8 4.41 1.15 3.82791393 0.26123889 Corin 0.92 0.26 3.49499985 0.28612305 U6 0.92 0.26 3.49499985 0.28612305 Galnt13 0.51 0.15 3.49291082 0.28629417 Snora17 0.51 0.15 3.49291082 0.28629417 Cdh8 4.11 1.26 3.26822892 0.30597612 Lipo1 1.93 0.60 3.21059946 0.31146831 Lipo2 1.93 0.60 3.21059946 0.31146831 Lipo4 1.93 0.60 3.21059946 0.31146831 5S_rRNA 1.11 0.37 3.03351472 0.32965062 Ctnnd2 1.11 0.37 3.03351472 0.32965062 U7 1.11 0.37 3.03351472 0.32965062 Nyap2 9.11 3.04 2.99567352 0.33381475 Ddx60 11.05 3.81 2.8998634 0.34484383 Gca 2.79 1.07 2.59970691 0.38465875 Kcnh7 2.79 1.07 2.59970691 0.38465875 Cttnbp2 7.15 3.04 2.3570623 0.42425692 U6atac 3.24 1.38 2.34594206 0.42626799 Trpc5 2.58 1.10 2.34232919 0.42692547 Mir1942 2.42 1.07 2.26851602 0.44081681 Parp10 2.42 1.07 2.26851602 0.44081681 Plec 2.42 1.07 2.26851602 0.44081681 Zfpm2 1.84 0.83 2.2202068 0.45040849 n-R5s27 4.31 2.12 2.02777192 0.49315211 136 Título de la tesis o trabajo de investigación

Runx2 4.31 2.12 2.02777192 0.49315211 Supt3h 4.31 2.12 2.02777192 0.49315211 Igf1 18.69 9.39 1.99017377 0.50246869 Mid1 20.85 10.63 1.96120101 0.50989164 Snora51 20.85 10.63 1.96120101 0.50989164 Dync1i1 7.24 3.72 1.94572519 0.51394719 Gpr98 5.21 2.76 1.88721424 0.52988155 Sema3a 3.32 1.77 1.87402727 0.53361016 Kif26b 4.19 2.28 1.83784614 0.54411519 U1 4.19 2.28 1.83784614 0.54411519 Fat3 17.22 10.32 1.66819107 0.59945172 Nedd4 23.10 14.38 1.60631823 0.62254165 Mdga2 5.64 3.52 1.60425712 0.62334147 7SK 5.31 3.46 1.53540965 0.65129199 St6galnac3 5.31 3.46 1.53540965 0.65129199 Akna 6.55 4.35 1.5062644 0.66389407 Ppp3ca 10.16 6.91 1.47036275 0.68010428 Tlr9 7.88 5.78 1.36322923 0.73355235 Dmd 18.34 14.46 1.26806438 0.7886035 n-R5s9 18.34 14.46 1.26806438 0.7886035 Tsga8 18.34 14.46 1.26806438 0.7886035 Rims1 84.34 96.91 0.87034553 1.14896897 Rapgef4 15.79 18.78 0.84104417 1.1889982 Gnaq 20.06 24.29 0.82579334 1.21095673 Nif3l1 95.66 121.37 0.78813517 1.26881788 Ppil3 95.66 121.37 0.78813517 1.26881788 Fxyd6 19.27 27.40 0.70325586 1.42195758 Ust 19.77 43.92 0.45024556 2.22101023

References

6.1 Go to the end of each chapter