Implantation Period Using the Affymetrix Genechip Mirna 4.0 Array

Characterization of miRNAs in the spent media throughout the pre- implantation period using the Affymetrix Genechip miRNA 4.0 array

Paul Del Rio

A Thesis presented to The University of Guelph

In partial fulfilment of requirements for the degree of Master of Science in Biomedical Science

Guelph, Ontario, Canada

ABSTRACT

CHARACTERIZATION OF MIRNAS IN THE SPENT MEDIA THROUGHOUT THE PRE-

IMPLANTATION PERIOD USING THE AFFYMETRIX GENECHIP MIRNA 4.0 ARRAY

Paul Del Rio Advisor(s): University of Guelph, 2021 Dr. Pavneesh Madan

Distinct miRNA populations have only been detected in spent media (SM) cultured with blastocyst-stage embryos. Therefore, the aim of the study was to globally profile the extracellular microRNA (miRNA) population throughout the pre-implantation period in bovine embryos. Briefly, embryos were in-vitro produced in group culture and SM was collected at the

2-cell, 8-cell, and blastocyst stage of development. Total RNA was extracted from SM and hybridized onto a miRNA microarray. Results show that distinct miRNA populations can be detected in the SM at the 2-cell, 8-cell, and blastocyst stage of development with miRNAs showing stage-dependant and stage-independent expression in SM. In addition, distinct miRNA populations can be identified in the SM cultured with slow-growing and fast-growing embryos at the 2-cell, 8-cell, and blastocyst stage of development. Overall, the findings contribute to the growing body of evidence supporting the use of miRNAs in SM as non-invasive biomarkers of early embryo development.

DECLARATION OF WORK PERFORMED

I declare that all work presented in this thesis was performed by me with the exception of the procedures mentioned below:

Stephan Botha, Saeid Ghiasi, and Stephanie Hookey collected cattle ovaries from a government inspected commercial abattoir (Cargill Meat Solutions, Guelph, Ontario, Canada).

Elizabeth St. John prepared media used for in vitro production of bovine embryos. Edgardo

Reyes and Allison MacKay were responsible for all the ordering of materials. Valérie Catudal from Genome Quebec (Montreal, Quebec) conducted all of the hybridization of total RNA samples on the Affymetrix Genechip miRNA 4.0 array.

iii ACKNOWLEDGEMENTS

First and foremost, I would like to express my deepest gratitude to my advisor, Dr.

Pavneesh Madan, for giving me an opportunity to work in the field of reproductive biology.

Your mentorship, guidance, and constructive feedback has allowed me to grow as an individual, student, and researcher. Giving me an opportunity to explore and choose the project of my choice has allowed me to solidify my interest and passion in this field. I will always cherish your expertise and knowledge, which has helped me through the highs and lows of my graduate school journey. I would also like to thank Dr. Jonathan Lamarre and Dr. Julang Li for sitting on my advisory committee and providing support and assistance throughout my project.

I would also like to thank Dr. Monica Antenos for all her helpful advice and assistance during my project. Her unrivalled understanding of laboratory techniques allowed me to form a deeper appreciation for the process of scientific research. I would like to express my deepest appreciation for Elizabeth St. John who helped train and develop my skills in IVF. Thank you for always being there for advice and support during the beginning of my project. I would also like to thank Ed Reyes and Allison Mackay for ordering all the materials and handling the logistics involved with sending my samples to Genome Quebec. Your efficiency and organization were instrumental to the completion of my project.

I would also like to thank the past and current members of the Madan lab for all your friendship and support. Thank you so much to Jyoti and Saba who were always there to lend a helping hand. I would also like to thank all the other past and present members and students in the RHBL who made my graduate school journey an enjoyable and memorable experience.

iv Lastly, I would like to express my deepest appreciation to my parents and my girlfriend,

Mimi, who have stood by me throughout the challenges and successes I encountered throughout my time as a graduate student. Your support, advice, and words of encouragement is source of inspiration that lifted my spirits and pushed me through difficult times. Although my dad will not be able to see me graduate, I know that he is watching from above and is proud of the person that

I have become.

v TABLE OF CONTENTS

ABSTRACT…………………………………………………………………………………...…ii

DECLARATION OF WORK PERFORMED……………………………………………...…iii

ACKNOWLEDGEMENT………………………………………………………………...……iv

LIST OF TABLES……………………………………………………………………………..viii

LIST OF FIGURES……………………………………………………………………………..ix

LIST OF ABBREVIATIONS…………………………………………………………………...x

INTRODUCTION……………………………………………………………………………….1

REVIEW OF LITERATURE…………………………………………………………….……..3

Historical perspective on assisted reproductive technologies…………………………....3 Limitation of embryo transfer………………………………………………..…...………5 Embryo assessment methods………………………………………………….…..……...6 miRNAs in the spent in-vitro culture media...…..…………………………..………….14

RATIONALE……………………………………………………………………………...……25

HYPOTHESIS AND OBJECTIVES…………………………………………………….……26

CHAPTER ONE………………………………………………………………….…………….27

INTRODUCTION………………………………………………………………….…..28

MATERIALS AND METHODS………………………………………………………31

Oocyte collection and in-vitro production of bovine embryos………………….31 Collection of spent in-vitro culture media………………………………………33 miRNA extraction……………………………………………………………….33 miRNA microarray hybridization……………………….………………………34 Statistical Analysis………………………………………………………………34 Target pathway prediction of differentially expressed miRNAs…….…………35

RESULTS……………………………………………………………………………….35

Differentially expressed miRNAs in 2-cell, 8-cell and blastocyst SM………….35 Differentially expressed miRNAs shared between 2 or SM conditions……...... 37

vi Prediction of miRNA-mRNA targets for stage-specific and shared differentially expressed miRNAs.………………………………………………………………39 Annotated roles of differentially expressed miRNAs in literature………….….41

DISCUSSION…………………………………………………………………………...43

CHAPTER TWO……………………………………………………………………………….50

INTRODUCTION……………………………………………………………………...51

MATERIALS AND METHODS………………………………………………………54

Oocyte collection and in-vitro production of bovine embryos………………….54 Collection of spent in-vitro culture media conditioned with SG and FG embryos…………………………………………………………………………..55 miRNA extraction…………………………………………...…………….…….57 miRNA microarray hybridization……………………………………………….57 Statistical analysis……………………………………………………………….58 Target pathway prediction of differentially expressed miRNAs..………………58

RESULTS ………………………………………………………………………………59

Differentially expressed miRNAs between 2-cell SG vs. 2-cell FG, 8-cell SG vs. 8-cell FG, and blastocyst SG vs. blastocyst FG SM………………………….....59 Predictions of miRNA-mRNA targets for differentially expressed miRNAs detected between 2-cell SG vs. 2-cell FG, 8-cell SG vs. 8-cell FG, and blastocyst SG vs. blastocyst FG SM……………………………………………………..….61

DISCUSSION…………………………………………………………………………...63

SUMMARY AND FUTURE DIRECTIONS………………………………………………….68

LITERATURE CITED………………………………………………………………………...71

APPENDIX……………………………………………………………………………………...82

vii LIST OF TABLES

Table 1: Spent IVC media collection schedule at various stages (adapted from Van Soom et al., 1997; Perkel and Madan, 2017).

Table 2. miRNA-mRNA targets predicted to have roles in the top 3 biological pathways represented in GSEA analysis.

Table 3. List of miRNAs detected in all 3 SM conditions previously annotated in literature. The majority (18 miRNAs) have been profiled in embryo-based studies, while 14 miRNAs were explored in cancer-related studies. Five miRNAs were cited in both embryo and cancer related studies.

Table 4. DEM between 2-Cell SG SM vs. 2-Cell FG SM. The majority of miRNAs were upregulated in 2-cell SG SM in comparison to 2-Cell FG SM.

Table 5. DEM between 8-Cell SG SM vs. 8-Cell FG SM. The majority of miRNAs were upregulated in 8-cell SG SM in comparison to 8-Cell FG SM.

Table 6. DEM between blastocyst SG SM vs. blastocyst FG SM. The majority of miRNAs were upregulated in SG SM in comparison to FG SM.

Table 7. Top 5 enriched biological pathways of predicted genes regulated by miRNAs differentially expressed in 2-cell SG SM vs. 2-Cell FG SM

Table 8. Top 5 enriched biological pathways of predicted genes regulated by miRNAs differentially expressed in 8-cell SG SM vs. 8-Cell FG SM.

Table 9. Top 5 enriched biological pathways of predicted genes regulated by miRNAs differentially expressed in blastocyst SG SM vs. blastocyst FG SM.

viii LIST OF FIGURES

Figure 1. Total transferable IVP and IVD embryos from the year 2000-2018. ET have been steadily increasing throughout the 18-year period, with IVP embryo surpassing IVD in 2015 as the dominate source of embryos transferred.

Figure 2. A total of 111 miRNAs were differentially expressed in 2-cell, 8-cell, and blastocyst SM. Expression of miRNAs in SM increased throughout the 3 conditions examined, with 13, 21, and 77 miRNAs being detected in 2-cell, 8-cell, and blastocyst SM, respectively. The majority of miRNAs detected in SM were blastocyst derived.

Figure 3. Venn diagram of DEM from 2-cell (yellow), 8-cell (green), and blastocyst SM (blue). Overlapping results showed DEM shared between 2 or more groups (14 miRNAs between 8-cell and blastocyst SM; 7 miRNAs between all 3 SM conditions).

Figure 4. A total of 6 miRNAs were significantly expressed in 2-cell SM. Two miRNAs were up-regulated, while 4 miRNAs were down-regulated in 2-cell SM.

Figure 5. The top 20 of 53 significantly up-regulated miRNAs are graphed with the 3 significantly down-regulated miRNAs exclusive to blastocyst SM.

Figure 6. Fourteen miRNAs were significantly shared between 8-cell and blastocyst SM. All miRNAs increased in levels of up-regulation from the 8-cell to the blastocyst SM.

Figure 7. Seven miRNAs were significantly shared between all three SM conditions. 6 miRNAs increased in levels of up-regulation, while 1 miRNA remained consistently down-regulated (bta- miR-450b).

Figure 8. Results of GSEA of mRNA targets predicted for DEM in 2-cell SM, blastocyst SM, shared in 8-cell and blastocyst SM, and common to all 3 SM conditions. The majority of predicted genes clustered under biological processes involved in cellular process, biological regulation, and metabolic process.

ix ABBREVIATIONS

ART Assisted reproductive technologies AI Artificial insemination AIn Artificial intelligence ApoA1 Apolipoprotein A1 COCs Cumulus oocyte complexes DABG Detectable above background DEM Differentially expressed miRNAs DGCR8 DiGeorge Syndrome Critical Region 8 EGA Embryonic genome activation ET Embryo transfer FDR False detection rate FG Fast growing FTIR Fourier transform infrared GSEA Gene set enrichment analysis HCG Human chorionic gonadotropin HLA-G Human leukocyte antigen G HPF Hours post fertilization MOET Multiple ovulation embryo transfer NIR Near-infrared NMR Nuclear magnetic resonance IETS International Embryo Technology Society ICM Inner cell mass IVD In-vivo derived IVF In-vitro fertilization IVP In-vitro produced JARID2 Jumongi protein miRNA MicroRNA mRNA Mature RNA Pre-miRNA Precursor microRNA Pri-miRNA Primary microRNA PZ Presumptive zygote qPCR Quantitative polymerase chain reaction SG Slow growing S-IVM Synthetic in-vitro maturation SM Spent media

Introduction

Declining cattle fertility is a globally observed phenomenon that plagues farmers and economies alike (Khatib et al., 2009). In-vitro production of embryos has been a methodology used to overcome the issue, but it has seen only limited success. Due to low cost, morphological assessment of cleavage and/or blastocyst stage embryos remains the key determinant of embryo viability (Wrenzycki, 2018). However, multiple studies have concluded that physical appearances do not always correlate well with implantation and live birth outcomes. In response, alternative non-invasive assessment methods are being explored.

One such method is the profiling of miRNAs in the spent culture media. Since miRNAs are involved in various biological processes, SM characterization may provide information on intrinsic embryo health. Recent studies have shown that distinct miRNA populations in SM can be identified between embryos differing in morphological quality, implantation outcome, chromosomal status, and sex. This suggest that extracellular miRNAs may be a viable non- invasive biomarker for embryo assessment. However, profiling of miRNAs has been limited to the media conditioned with blastocyst staged embryos, and/or few candidate miRNAs have been explored throughout the preimplantation period.

Therefore, the aim of this study was to globally profile miRNAs in the SM throughout the pre-implantation period using the Affymetrix genechip miRNA 4.0 array. Firstly, SM conditioned with 2-cell, 8-cell and blastocyst staged embryos were profiled for miRNA expression. This was conducted to determine if the intracellular changes in miRNA expression in embryos are detectable in the SM. In addition, profiling in the early, mid and late stages of preimplantation development would allow for the identification of stage-specific miRNAs and miRNAs shared throughout pre-implantation development.

The second part of this study was to determine if distinct miRNA populations can be detected in SM conditioned with embryos growing at different developmental rates. The aim was to profile the global miRNA population in SM conditioned with SG and FG embryos at the 2- cell, 8-cell and blastocyst stage of development. This was conducted to elucidate potential miRNA biomarkers between embryos of differing quality throughout development.

REVIEW OF LITERATURE

Historical perspective on assisted reproductive technologies

Although considered a modern technology, assisted reproductive technologies (ART) in cattle have been used since the early 20th century with the implementation of artificial insemination (AI) in Denmark and Russia (Ivanoff, 1922; Moore & Hasler, 2017). The aim was to propagate the genetics of genetically superior sires within the herd (Moore & Hasler, 2017).

With continual refinement in collection, processing, evaluation and preservation of sperm, AI has become standard practise in many breeding programs worldwide. Although relatively efficient with a 60.1% pregnancy rate (Moore & Hasler, 2017), AI technologies alone cannot keep pace with the growing demand for beef and dairy products. Therefore, advanced breeding techniques, such as multiple ovulation embryo transfer (MOET), were developed in the 1970s and 1980s, as a means of increasing the number of offspring produced per cow (Mapletoft &

Hasler, 2005; Smith, 1988a, 1988b).

MOET involves the use of hormones to induce superovulation in donor cows to produce several eggs, as oppose to one during a natural heat (Moore & Hasler, 2017). Once ovulation has been detected, the donor cow is inseminated, and the embryos were surgically collected after 7 days (Moore & Hasler, 2017). Due to the invasive nature and cost of the procedure, a non- invasive procedure involving flushing of the reproductive tract, was developed (Drost, Brand, &

Aarts, 1976; Dziuk, Donker, Nichols, & Petersen, 1958; Elsden, Hasler, & Seidel, 1976; Rowe et al., 1976; Rowson & Dowling, 1949). With such advancements, MOET became commercially available in the 1970s (Hasler, 2014), drastically improving the reproductive efficiency in cattle.

Valuable cows that would normally produce a single calf a year can now have at least 5 calves a year with the use of surrogate recipients (Moore & Hasler, 2017).

During the same time, rapid research was being conducted in the field of in-vitro production systems. With refinement in ovum pick up methods, sperm capacitation protocols, and culture conditions, the first calf to be born strictly from in-vitro techniques was reported in

1987 (Lu, Gordon, Chen, & McGovern, 1987) . Innovations within the industry continued with the development of two step culture media, which serves to closely mimic the changes occurring in the fallopian tube. Maturation media was developed to circumvent the need of having to correctly time a dam in-heat. In-vitro maturation also allowed for the salvaging of genetic material from culling due to old age or infectious disease (Hasler, 2003; Mapletoft & Hasler,

2005; Wrathall, Simmons, Bowles, & Jones, 2004). This drastically increased the types of donors that could be used for embryo transfer (ET).

With the development and improvement of cryopreservation techniques, large scale ET programs became possible. Embryos produced in-vivo or in-vitro can be harvested and/or produced without the immediate need of transfer (Mapletoft & Hasler, 2005). This was beneficial to the international trade of cattle as transportation of livestock across vast distances was costly and carried higher risk of disease transmission. Entire herds, in the form of frozen embryos, can be transported with fewer logistical barriers and little to no risk of disease transmission (Mapletoft & Hasler, 2005). With these economic advantages, large scale ET programs were implemented in North America, Europe, South America, and Asia in the 1970s,

1980s, 1990s, and 2000s, respectively (Mapletoft & Hasler, 2005).

In its infancy, ET programs relied on in-vivo derived (IVD) embryos due to their superior quality and less cost in comparison to in-vitro produced (IVP) embryos. IVD embryos had thicker zona pellucida, better resistance to freezing, and did not require extensive laboratory space. However, as demand grew in the industry and more laboratories were integrated in farms,

more IVP embryos were produced. 2015 was the first year whereby more IVP embryos were produced than IVD embryos (Figure 1.). Although production of embryos is at an all-time high, various factor remain preventing farmers from using ET technologies. One such factor is that pregnancy rates remain similar to traditional breeding programs, such as AI and natural breeding

(Calhaz–Jorge et al., 2017). Since ET is the only advance breeding program capable of examining embryos prior to transfer, improvements in embryo quality assessment must be made for ET programs to become a more attractive option across the industry.

Limitation of Embryo Transfer

Although vast improvements have been made in ET programs in both humans and bovine, some historical inefficiencies remain unsolved. One example being the lack of certainty

on an embryo’s implantation potential (Kropp, Salih, & Khatib, 2014). In humans, this challenge leaves clinics to practise multiple embryo transfers. Although not ideal due to the potential side effects, such as low birth weight, prematurity, risk to maternal health, multiple embryo transfers is commonly practised as a means of improving success rates for a costly procedure (Klitzman,

2016). In bovine, implantation uncertainty leaves farmers the challenge of having a cow out of service for more time spent rebreeding (Perkel et al., 2015). This has profound impacts on overall milk production yields, which can push a farmer to cull members of the herd if economically necessary (Perkel et al., 2015). Therefore, it is imperative that knowledge on embryonic implantation potential is explored and understood, as it has profound economical and clinical repercussions.

Embryo Assessment Methods

A classic parameter used as a marker for implantation potential is embryo quality

(Lindner & Wright, 1983). To date, the only method used widely in the field is morphological assessment. This method involves the examination of an embryo’s physical features at different stages of development under stereomicroscope. The most common features assessed may include cytoplasm homogeneity, number of blastomeres, blastomere symmetry, and degree of fragmentation (Gandolfi et al., 1997; Perkel et al., 2015). This method of assessment has been in practise for as long as in-vitro production systems have been used to achieve pregnancy in humans and cattle. Research shows that morphology correlates with the developmental/implantation potential of an embryo (Singh & Sinclair, 2007; Van Soom et al.,

1997), however, this method is not without its limitations. The greatest challenge limiting morphological assessment is the variability that exist in the personnel and tools required to carry out observations (Rocha et al., 2016). An embryologist is task with grading individual or groups

of embryos on morphology. Correct assessment and use of established guidelines, such as those published by the International Embryo Technology Society (IETS) (Bó & Mapletoft, 2013) or

Gardner and Schoolcraft (Gardner & Schoolcraft, 1999), are dependent on an embryologist’s experience and attention to detail (Rocha et al., 2016). Thus, the guidelines are subject to a user’s interpretation, allowing for variations in grading. Studies have shown that morphological assessment vary greatly within users of the same facility (Farin et al., 1995; Van Loendersloot et al., 2014) and between facilities using the same guidelines . These differences are most pronounce when comparing embryos of similar morphology, such as those graded as excellent or good (Farin et al., 1995). User consistency appears to occur between embryos presenting drastically different morphology, such as good and poor (Rocha et al., 2016). In addition to user variation, optical tools used for assessment are not standardized across the industry. Therefore, grading of an embryo may differ based on technical and personnel variations, thereby reducing its accuracy and reproducibility in the field.

The low accuracy of morphological assessments has been well documented (Khatib et al.,

2009). In one study, a cohort of embryos were morphologically evaluated prior to genetic screening through embryo biopsy. Researchers showed that embryos may possess aneuploidy, despite appearing morphologically normal (Perkel et al., 2015). This highlights the greatest drawback of morphological assessment: the inability to provide intrinsic information on embryo health, which translates to developmental and implantation potential. Therefore, there is a need for an assessment method that is accurate and reproducible, while still possessing the non- invasive, cost-effectiveness, and the user-friendly nature of morphological evaluation.

One method that has seen limited clinical implementation is morpho-kinetics analysis

(Payne, Flaherty, Barry, & Matthews, 1997). This evaluation method grades an embryo based on

the timings in which it reaches certain morphological stages, such as timing to cleavage, compaction, and blastulation, while still examining morphology. There are studies to suggest that the timing of development, in addition to morphology, correlate with embryo quality (Aparicio,

Cruz, & Meseguer, 2013; Edwards et al., 1984; Van Montfoort, Dumoulin, Kester, & Evers,

2004). Literature suggest that embryos that do not cleave too early or late are those of the highest quality (Van Soom et al., 1997). The main driver to the development of morpho-kinetics analysis is the advancements in time-laspe imaging technologies. With current technologies, cameras can be placed within the incubator and images can be taken and recorded at any time interval

(Aparicio et al., 2013). This drastically increases the qualitative and quantitative ability of assessment as numerous observations can be made without the direct presence of an embryologist (Aparicio et al., 2013). This eliminates the need for an embryologist to make timely observations, and more importantly, drastically limits an embryo’s exposure to sub- optimal conditions as observations are made in the incubator. Once images are processed, the information can be examined by an embryologist in a less time-constraint fashion, which can enable stricter adherence to morphological assessment guidelines. Despite these advancements from traditional morphological evaluation, morpho-kinetics has seen limited successes in clinical practise. Pregnancy rates are similar to clinics using traditional methods, and the costly equipment deters it uses in industrial applications (Rocha et al., 2016). One method that is currently being examined, as a means of improving predictive value of morpho-kinetics, is the use of artificial intelligence (AIn) software for grading of embryos.

In this regard, embryo images from time-lapse software can be inputted into a computational program whereby the image features are translated into an algorithm. The aim of

AIn is to automatically classify embryos to increase the reproducibility and objectivity of

morpho-kinetic analysis (Rocha et al., 2016). One of the first studies to integrate AIn into embryo assessment was conducted by Chen and colleagues, whereby a computer assisted scoring system was developed (Chen et al., 2016). Using a multivariate logistic regression system with multivariate adaptive regression spine, the system did have higher discriminatory power for embryo selection in comparison to an embryologist (Chen et al., 2016). The first study to use an all AIn system for embryo selection was developed by Rocha and colleagues. In their study, they process 482 images of day 7 blastocyst-staged bovine embryos using genetic algorithms and artificial neural network. The images were reduced to 24 assessment variables, which formed the

AIn structure for embryo assessment (Rocha et al., 2017). In comparison to the benchmark assessment scores provided by embryologists in the study, the AIn system was able to classify

76% of embryos correctly, while showing greater consistency in grading (Rocha et al., 2017). It is important to note that no AIn grading system has been trialed in a clinical or industrial setting, however early research shows promise in the technology. Despite greater consistency and reproducibility in results, AIn systems do no completely remove the human aspect in assessment.

The computer algorithms are still based on the learnings and experiences of an embryologist.

Therefore, the standards of assessment are limited to the embryologist who trained the system

(Rocha et al., 2017).

Aside from morphology, the study of an embryo’s micro-environment through SM analysis has been explored as a non-invasive method of embryo quality. It is well-established that a developing embryo continuously interacts with its surrounding by up-taking and releasing a wide variety of macromolecules (Rødgaard, Heegaard, & Callesen, 2015). It is believed that embryos of differing quality have different proteomic, metabolomics and genomics profiles,

detectable in the SM. With this hypothesis, a once discarded fluid, may potentially be used in conjunction with morphology evaluations for the assessment of embryo quality.

Proteomics is the study of all the protein products that are translated in a cell and/or tissue at a specific time or condition (Rødgaard et al., 2015). Since protein changes for the maintenance of homeostasis, the proteome is a valuable tool for identifying normal and pathological conditions. In in-vitro production systems, the proteome can be indirectly studied through secretomic analysis of SM (Katz-Jaffe & McReynolds, 2013). The secretome are all the protein products that are produced and released by a cell and/or tissue. It is expected that embryos differing in quality, will have distinguishable gene expression, which will be reflected and detected in the proteome and secretome, respectively (Katz-Jaffe & McReynolds, 2013).

Knowledge about an embryos proteonome and secretome have been limited due to sensitivity issues with assays and the reagents used for in-vitro systems (Rødgaard et al., 2015). Currently, media used by many laboratories contain serum albumin, which is a naturally occurring blood protein, to improve embryo development. The challenge is that protein products with a similar molecular weight to albumin (60-70kDA) can be missed during profiling (Mains et al., 2011).

Nonetheless, research have attempted to profile the secretome of SM, highlighting human leukocyte antigen G (HLA-G) ( Katz-Jaffe et al., 2009), jumongi protein (JARID2) (Cortezzi et al., 2011), human chorionic gonadotropin (HCG) (Butler et al., 2013), and apolipoprotein A1

(apoA1) (Mains et al., 2011; Nyalwidhe et al., 2013) as potential protein biomarkers of embryo quality. ApoA1, HLA-G, JARID2, HCG have conflicting results or were inconsistent in clinical settings, while ApoA1 is the most extensively studied protein biomarker in SM.

ApoA1 is a major protein component of high-density lipoproteins with a specific role in lipid metabolism and cholesterol transport (Brunham et al., 2006). In embryonic SM, apoA1

levels appears to have dynamic expression in media. Researchers saw that embryos that scored higher in morphology had decreased levels of apoA1 from the oocyte to the 4-cell stage embryo

(Nyalwidhe et al., 2013), followed by a resurgence in expression in the blastocyst stage (Deutsch et al., 2014; Mains et al., 2011). It is postulated that apoA1 plays a role in regulating cell membrane synthesis in embryos. Cell divisions start out slow in early cleavage embryos before rapidly increasing proliferation towards the morula and blastocyst stage; coinciding with apoA1 expression. Indeed, apoA1 transcripts were detected in late stage embryos and not early embryos, supporting its potential roles as a biomarker of cell proliferation (Rødgaard et al., 2015). A limitation of the studies conducted thus far on apoA1 is that no correlations to pregnancy rates were done.

Overall, the use of the secretome as an indicator of the proteome may be a valuable tool for discerning embryo quality. Early studies have confirmed that embryos of differing quality have distinct secretomic profiles, detectable in the SM. Certain proteins have been identified as candidate biomarkers. However, proteomic data acquisition often requires the use of expensive machinery, such as a mass spectrophotometer, for array-based detection of proteins (Rødgaard et al., 2015). Thus, implantation to current laboratories would be costly and time-consuming as samples would need to be sent to third party facilities. In addition, current assay protocols require a large sample volume to meet sensitivity cut-offs. Therefore, protocols are limited to using pooled SM samples which takes away the ability to distinguish between embryos. This may be suitable for industrial uses where group culture is practised, however, implementation into human laboratories would be difficult.

The secreted metabolome is by far the most extensively studied non-invasive biomarker of embryo quality, outside of morphology analysis. The first studies to analyze SM metabolites

measured the concentrations of glucose, pyruvate, and certain amino acids in media. Researchers saw that glucose uptake was higher in morphologically good quality embryos (Gardner et al.,

2001). In addition, viability and pyruvate uptake had an inverse relationship (Conaghan et al.,

1993), and certain amino acids, such as glycine, leucine, and asparagine, were correlated with successful pregnancies (Brison et al., 2004). The main limitation to using the aforementioned molecules as biomarkers is due to the cost required to implement the technologies required to detect such molecules in an in-vitro fertilization (IVF) laboratory setting. In addition, biological variation between embryos often presented conflicting results, making identification of a metabolite marker difficult. Nevertheless, early research has shown that the metabolic content of

SM may correlate with embryo quality.

Resurgence in the study of metabolites in SM came in the development of spectral technologies capable of detecting a wide array of metabolites. This gave rise to the metabolomics analysis of SM, which involved a comprehensive analysis of all small-molecule metabolites found within the media (Nagy, Sakkas, & Behr, 2008). The aim of these array-based studies was to identify molecular biomarkers that provides a holistic view of normal and pathological embryo development. Evidence suggest that viable pre-implantation embryo have lower nutrient turnover rates and are metabolically “quieter” than their non-viable counterparts (Leese, 2002).

Thus, spectral technologies, such as near-infrared (NIR), fourier-transform infrared (FTIR), and nuclear magnetic resonance (NMR) spectroscopy, may be used to distinguish between viable and non-viable embryos.

NIR illuminates unknown samples with a broad spectrum of near-infrared light, giving broad information on the chemical make-up of a SM sample. A number of researchers (Botros,

Sakkas, & Seli, 2008; Seli et al., 2011; C. G. Vergouw et al., 2014; Carlijn G. Vergouw et al.,

2011) have shown that the chemical composition of SM do differ between viable and non-viable embryos. With support across multiple studies, coupled with short turnover time from product analysis to results and low input volume, NIR analysis of SM has seen clinical trials. Although a promising technology, no improvements in pregnancy rates were observed between samples analyze through morphology versus morphology and NIR analysis (C. G. Vergouw et al., 2014).

In addition, further studies have seen a substantial degree of intergroup differences, making results prone to false positives (Rødgaard et al., 2015). A method similar to NIR, called FTIR, has also been used to analyze metabolites in SM (Muñoz et al., 2014). Similar to NIR, research using FTIR has conflicting reports showing little to no improvements in pregnancy outcomes, when compared to morphology assessment (Rødgaard et al., 2015).

Recently, NMR spectroscopy have also been used to study metabolic profiles of embryonic SM. One advantage of NMR over NIR and FTIR is that it has the ability to distinguish between individual metabolites (Perkel & Madan, 2017). Research in this regard is concerned with finding unique metabolic markers, rather than general metabolic signatures.

Although identification of unique metabolites in SM between morphologically and morpho- kinetically different embryos (Kirkegaard, Svane, Hindkjær, Nielsen, & Ingerslev, 2013; Perkel

& Madan, 2017; Pudakalakatti et al., 2013; Wallace et al., 2014) has been established, clinical application of NMR remains elusive. The biological variation between embryos, IVF culturing techniques, and reagents used make the data between studies ambiguous and sometimes contradictory (Rødgaard et al., 2015). For example, one study found metabolic difference in early embryos that implanted versus ones that did not, while other researchers have failed to detect metabolic differences in media cultured in late stage embryos (Rinaudo et al., 2012). In addition, research have shown that different media have the ability to alter the NMR results. The

use of media and reagents across IVF clinics are not standardized, therefore, NMR results may not be reproducible and have large inter-clinic variations (Rinaudo et al., 2012).

In general, metabolomics analysis of embryonic SM has been comprehensively explored identifying numerous candidate biomarkers of embryo quality. However, biological variation between embryos makes finding a consistent, reliable, and sensitive metabolic biomarker challenging. With further increases in assay sensitivity and development of protein-free media, methods such as NMR, which are fast and requires low input sample volume, may see clinical integration in the future. microRNAs in the spent in-vitro culture media

Profiling of miRNAs in the SM is the most recently proposed method for the assessment of embryo quality. miRNAs are short, non-coding RNA molecules capable of post- transcriptional regulation of gene expression (Wahid et al., 2010). Researchers have demonstrated that a single miRNA can have multiple mature RNA (mRNA) targets, thereby having the ability to influence a wide array of genes and molecular pathways (Wahid et al.,

2010). miRNAs are highly conserved across species and are believed to regulate at least a third of the mammalian genome (Friedman et al., 2009). Targets of miRNAs can range from genes governing proliferation, differentiation, development, and apoptosis (Lewis, Burge, & Bartel,

2005) with expression being detected in a wide-array of tissues, such as kidney, liver, heart, testis, ovaries (Ludwig et al., 2016). In addition, miRNAs have been identified in various biological fluids, such as tears, blood, urine, semen, and oviductal fluid (Valadi et al., 2007; Z.

Wang et al., 2013; Weber et al., 2010). It has been demonstrated that miRNAs are encapsulated in exosomes and/or bound to proteins (Valadi et al., 2007), making them resistant to temperature changes, pH flux, and harsh detergents (Mitchell et al., 2008). With such stable characteristics,

heterologous tissue expression, and wide-genomic influence, miRNAs promise to be a good candidate biomarker for various physiological and pathological conditions, such as cancer

(Mitchell et al., 2008), diabetes (Zampetaki et al., 2010), and infertility. miRNA biogenesis

miRNA biogenesis begins with the transcription of primary miRNA fragments (pri- miRNA) from the genome by RNA polymerase II and RNA polymerase III enzymes in the nucleus (Wahid et al., 2010). The pri-miRNA is cleaved by a RNA microprocessor complex, comprising of a ribonuclease-III-like enzyme, drosha, and an RNA binding protein, DiGeorge

Syndrome Critical Region 8 (DGCR8), to form a double stranded hairpin-shape molecule called precursor miRNA (pre-miRNA) (Wahid et al., 2010). The pre-miRNA is recognized by an exportin 5/RanGTP complex and is transported out to the cytoplasm, whereby a ribonuclease-III- like enzyme, dicer, further processes it into a double stranded miRNA-miRNA duplex (Wahid et al., 2010). The guide strand is removed, and the other strand integrates into the RNA-induced silencing complex to form a mature miRNA (Bartel, 2004). Afterwards, miRNAs have the ability to bind imperfectly to the seed region (located within the 3’ untranslated region) of a mRNA target, resulting in the complete degradation or a partial translational repression of target mRNA

(Bartel, 2004). miRNAs may also target the mRNA promoter region and increase its translation

(Galliano & Pellicer, 2014).

The roles of miRNAs in gametogenesis

The relationship between miRNAs and reproductive development is well-established. In fact, the first miRNA to be discovered, lin-4, regulated the gene, LIN-14, which had implications in larvae development in the nematode Caenorhabditis elegan (R. C. Lee, Feinbaum, & Ambros,

1993). From the findings of Lee and collegues in 1993, several studies have explored the roles of

miRNAs in reproduction. The first studies to come forward were mice knockdown studies of the genes involved in miRNA biogenesis, DICER, DROSHA, and DGCR8, to examine the effects of miRNA depletion in the female and male reproductive systems.

In the female model, deletion of DICER has profound negative effects on oogenesis.

Specifically, mouse studies have shown that DICER depleted ovaries had oocytes presenting abnormal spindle formation, chromosomal alignment, polar body formation, and lower maturation rates (Liu et al., 2010; Murchison et al., 2007; Tang et al., 2007). Poor oocyte development was also associated with poor granulosa cell proliferation and steroidogenesis, resulting in lower recruitment of immature oocytes and higher rates of follicular atresia (H. C.

Liu et al., 2010; Murchison et al., 2007). Aside from poor oocyte development, DICER depleted ovaries also have compromised ability in maintaining pregnancy (Luense et al., 2011). When

DICER depleted ovaries were surgically implanted with wild-type mice embryos, all embryos failed to develop (Otsuka et al., 2008). Post implantation development was primarily affected, suggesting that miRNA depletion impacted proper corpus luteus formation (Otsuka et al., 2008).

Interestingly, wildtype mice with DICER depleted ovaries did not present any idiopathy during fertilization or pre-implantation period (Otsuka et al., 2008). In terms of the associated structures of the female reproductive tract, DICER deletion led to substantial morphological changes within the fallopian tube and uterus (Gonzalez & Behringer, 2009). There was observed disintegration of the smooth muscle layer of the tract, disorganized epithelium, and a dysregulation in the complex chemical milieu of the uterine and follicular fluid (Gonzalez & Behringer, 2009).

Since DICER a gene involved in the biosynthesis of other small non-coding RNAs, such as small interfering RNAs (siRNAs), the effects observed in DICER studies is not truly associated with miRNA depletion (Kim, Han, & Siomi, 2009). DGCR8, on the other hand, is a

gene solely involved in the biosynthesis of canonical miRNAs. Therefore, DGCR8 knockout studies were performed on mouse oocytes (Suh et al., 2010). With the use of a ZPR promoter-

Cre recombinase system, Suh and colleagues eliminated DGCR8 expression in mouse oocytes and saw a reduction in miRNA expression and overall fecundity (Suh et al., 2010). Contrasting

DICER knockouts, DGCR8 knockouts did not present abnormal spindle formation and oocytes were able to mature appropriately (Suh et al., 2010). In addition, there was no difference in mRNA expression between control oocytes and DGCR8 knockouts (Suh et al., 2010). It is important to note that miRNA biosynthesis can still occur in the absence of DGCR8 through the non-canonical pathway, therefore researchers conclude that non-canonical mIRNAs may still be expressed and influencing gene expression in DGCR8 knockout oocytes (Gross, Kropp, &

Khatib, 2017a). Overall, miRNAs seem to play a crucial role in regulating gene expression during oogenesis.

In the male model, similar knockout studies have been conducted to examine the roles of miRNAs in spermatogenesis. Deletion of DICER and DROSHA in male primordial germ cells resulted in abnormal development of germ cells, spermatozoa, and infertility (Hayashi et al.,

2008). Deletion of DICER in spermatogonia results in altered genetic segregation in meiosis causing abnormal chromatin compaction, acrosome formation, and increased apoptosis in spermatids (Romero et al., 2011). Removal of DICER in sertoli cells, which are cells responsible for sperm production in the testis, resulted in deformed testis formation, abnormal spermatogenesis, and infertility (Chang et al., 2012). Moving downstream of the male reproductive tract, deletion of DICER in the epididymis negatively affected sperm maturation.

Researchers observed a disruption in lipid homeostasis, weakened sperm membrane, and an alteration in the structural organization of the epididymis with signs of a thickened blood-testis

barrier (Björkgren et al., 2015; Korhonen et al., 2015). Overall, miRNA processing machinery are essential in the development of primary and secondary male sex organs, as well as in the propagation, differentiation, and maturation of spermatocytes into mature sperm cells.

The roles of miRNAs in embryogenesis

Fertilization is the process of fusing two haploid gametes, a sperm and an oocyte, to form a diploid cell called a zygote (Wassarman, 1999). Fusion requires a sperm cell to penetrate the zone pellucida, whereby paternal genetic material is introduced into the oocyte (Wassarman,

1999). Aside from physical DNA being exchange in this process, recent research has detected sperm-borne miRNAs in zygotes. Genomic characterization of mice zygotes has shown that both the oocyte and zygote have similar profile, suggesting that most miRNAs are maternally inherited (Tang et al., 2007). However, research have shown that a small population of miRNAs are paternally inherited. Cross referencing the profiles of mice sperm, oocyte, and zygotic cells, revealed 14 miRNAs shared between sperm and zygotic cells (Yuan et al., 2016). These paternally inherited miRNAs are thought to influence cleavage as fertilization of oocytes with miRNA-depleted sperm failed to progress to 2-cell stage embryos (Liu et al., 2012). When the same oocytes were injected with wild type sperm miRNAs, the zygotes were rescued and cleaved. Further analysis of sperm-borne miRNAs revealed that miR-34c-5p may have a role in initiating the first cleavage division in mice (Liu et al., 2012). In addition, miR-34c-5p have been detected in high quality spermatozoa, successful intra-cytoplasmic injection treatment, good quality embryos, implantation, and pregnancy (Abu-Halima et al., 2014; Cui et al., 2015; Liu et al., 2012). miRNAs appear to play a moderator role during fertilization, whereby it aids to maintain paternal and maternal communication.

Embryonic genome activation (EGA) is defined as the period in which development become fully controlled by the embryonic genome (Jukam, Shariati, & Skotheim, 2017). Prior to

EGA, the embryo relies on maternally inherited transcripts and proteins, such as mRNA and miRNA, in addition to paternally inherited transcripts, to carry out its first cleavage divisions

(Jukam et al., 2017). From fertilization to EGA, transcription is kept at a minimum and resumption of transcription happens at different stages across species. In mice, human, and bovine, EGA occurs at the 2-cell (Schultz, 1993), 4-cell (Braude, Bolton, & Moore, 1988), and

8-cell stage of embryo development (Memili & First, 2000). Regardless of differences in timing,

EGA is marked by maternal mRNA degradation and the transcription and expression of the zygotic genome (Mondou et al., 2012). It is thought that zygotic miRNAs play a key role in the targeted degradation of maternal transcripts and modulation of gene expression from EGA until the blastocyst stage of development (Mondou et al., 2012). In a microarray screening study,

Mondou and colleagues identified two miRNAs, miR-21 and miR-130a, as possible candidates for influencing EGA (Mondou et al., 2012). Quantitative polymerase chain reaction (qPCR) analysis of the two miRNAs revealed a linear increase of the precursor and mature forms of the miRNAs in early embryos, but not in blastocyst-staged embryos (Mondou et al., 2012). Mondou and colleagues concluded that certain miRNAs are upregulated to activate certain genes involved in the initiation of maternal mRNA degradation and activation of the embryonic genome. In addition, other studies have shown that embryonic miRNA expression occurs in a stage specific manner. Tesfaye and colleague saw that miR-125a, miR-496, miR-127, miR-145, and miR-496 were highly expressed in early stages embryos, while expression decreases in late staged embryos (Tesfaye et al., 2009). Therefore, researchers suggest that miRNAs may play a role in the genetic and molecular rewiring that occurs during EGA. EGA across species is often seen as

a key developmental milestone, whereby any issues in the embryonic genome will likely result in embryonic arrest or senescence. Thus, examination of miRNAs during this stage of embryo development is paramount in determining embryo quality.

Blastulation is the stage in pre-implantation development that marks the formation of the blastocyst from a morula, which is collection of integrated blastomeres formed after the events of

EGA. The blastocyst contains three distinct structures: inner cell mass (ICM), trophoblast, and the blastocoel. The ICM is a collection of cells that will eventually form a fetus, while the trophoblast are the cells in the periphery of the blastocyst responsible for providing nutrients to the embryo and forming the placenta. The blastocoel cavity is a fluid filled space containing nutrients for the development of the ICM. Blastulation is also the first differentiation event whereby totipotent blastomeres, which are cells capable of differentiating into any cell type, become pluripotent. As dynamic gene expression is necessary to achieve these morphological, cellular, and molecular changes, researchers postulate that miRNAs play a role in blastulation.

Studies have shown that both the trophectoderm and ICM display distinct miRNA profiles

(Berardi et al., 2012; Lee et al., 2016; Reza et al., 2019). The miRNAs detected within the ICM have roles in germ line differentiation, whereby distinct miRNAs can be distinguished between the endoderm, ectoderm, and mesoderm (Reza et al., 2019). In comparison, trophectoderm derived miRNAs are believed to have roles in embryo-maternal cross talk (Reza et al., 2019).

Overall, miRNAs play a substantial role in the development of the pre-implantation embryo. In fertilization, paternally inherited miRNA transcripts influence an embryo’s ability to initiate cleavage. During EGA, zygotic miRNA expression triggers maternal mRNA degradation.

From EGA to blastocyst formation, miRNAs regulate gene expression to complete the molecular rewiring an embryo must undergo prior to its interaction with the maternal endometrium. Due to

their widespread effects, it has prompt researchers to explore miRNAs as biomarkers of embryo quality, with the potential to serve as an indicator of implantation potential.

The first studies supporting the use of miRNAs as biomarkers of embryo quality examined intra-cellular miRNA expression of blastocyst-stage embryos derived from either healthy human patients or those presenting some form of infertility (McCallie, Schoolcraft, &

Katz-Jaffe, 2010). It was determined that the intra-cellular miRNA expression between the two groups of embryos were significantly different, even when the embryos in the two groups were matched according to morphological score (McCallie et al., 2010). Other studies in bovine examined blastocyst embryos and also saw significant differences in miRNA expression between embryos of different morphology (Tesfaye et al., 2009). Therefore, these findings suggest that miRNAs can be a tool to distinguish between normal and aberrant embryos. Since invasive miRNA analysis is costly and potentially damaging to an embryo, profiling extra-cellular miRNAs found in the SM can be a viable alternative. Studies have shown that miRNAs can be detected in culture media and that they are of intra-cellular origin (Valadi et al., 2007).

Therefore, profiling of miRNAs in embryonic SM could be used as a representation of intracellular miRNA expression. This can provide an indication of certain genes and molecular pathways at play within a given embryo.

Initial studies attempting to profile miRNAs in the SM were unsuccessful. Katz-Jaffe and

Reynolds profiled euploid blastocyst SM and failed to detect the expression of 377 miRNAs on their assay (Katz-Jaffe & McReynolds, 2013). With improvements in low-input miRNA extraction protocols and sensitivity of assays, Kropp and colleagues in 2014 were the first to report of miRNAs being detected in embryonic SM. Kropp and colleagues individually cultured bovine embryos from day 5 (morula) to day 8 (blastocyst) of development. At day 8, embryos

were categorized as either degenerate (failed to form a blastocyst) or ones that progressed to a blastocyst (Kropp et al., 2014). Five miRNAs were analyzed in the embryos and their respective media using qPCR. It was determined that the extracellular miRNAs were of embryonic origin, as miRNAs highly expressed in media were also expressed intracellularly (Kropp et al., 2014). In addition, miR-25, miR-302c, miR-196a2, and miR-181a expression was found to be higher in degenerate embryos compared to blastocyst embryos (Kropp et al., 2014). Interestingly, miR-

196a2 was only detected in unconditioned media and media conditioned with degenerate embryos (Kropp et al., 2014). This suggest that miR-196a2 was up-taken by viable embryos, opening up the possibility of using miRNA media supplementation for improving blastocyst outcome. Kropp and colleagues were the first to demonstrate, in bovine, that miRNAs can be detected in embryonic SM and that different expression patterns can be correlated with embryo quality. Rosenbluth and colleague in the same year expressed similar findings, but in human IVF

SM. In this study, Rosenbluth and colleagues used a heterologous qPCR array capable of detecting 754 miRNAs in SM derived from blastocyst embryos that resulted and failed to produce a pregnancy (Rosenbluth, Shelton, Wells, Sparks, & Van Voorhis, 2014). The researchers found that miR-372, miR-191 and miR-645 were differentially expressed in media conditioned with embryos that resulted in pregnancy versus those that failed (Rosenbluth et al.,

2014). The cumulative findings of both Kropp and colleagues and Rosenbluth and colleagues show strong evidence that miRNAs are detectable in the embryonic SM and that differential expression of miRNAs can be correlated to embryo quality and pregnancy outcome.

Other studies that have been conducted focussed on elucidating the roles that extracellular miRNAs play in embryonic development. In 2015, Kropp and Khatib conducted a study with the aim of characterizing the global miRNA expression of SM conditioned with

degenerate and normal blastocyst. Results from the study saw 11 differentially expression miRNAs (DEM), with all being upregulating in degenerated blastocyst media (Kropp & Khatib,

2015). miR-24, miR-191, and miR-148a were chosen for validation and miR-24 was selected for a subsequent media supplementation study (Kropp & Khatib, 2015). Embryos cultured with miR-24 presented a decrease of 27.3% in blastocyst rates when compared to embryos cultured in untreated media (Kropp & Khatib, 2015). Further analysis revealed that embryos cultured with miR-24 had higher levels of intra-cellular miR-24, suggesting that extracellular transcripts were being up-taken by embryos in culture (Kropp & Khatib, 2015). It is also important to note that the downstream mRNA target of miR-24, CDKN1b, was also downregulated in embryos cultured with miR-24. Since CDKN1b is a protein associated with cell cycle regulation, the authors postulate that extra-cellular miR-24 negatively influenced blastocyst development by interfering with CDKN1b expression (Kropp & Khatib, 2015). Overall, Kropp and Khatib were the first to demonstrate a potential mechanism by which extracellular miRNAs may influence embryo development. miRNAs secreted or native in embryo culture media may serve as a tool for cell-to-cell communication.

In fact, a number of studies have been conducted suggesting that extracellular miRNAs in

SM may be used for mediating the crosstalk between the endometrium and embryo during implantation. One of those studies were conducted by Capalbo and colleagues whereby a cross comparative analysis was done between miRNAs expressed in SM conditioned with embryos that implanted and failed to implant (Capalbo et al., 2016). Two miRNAs, miR-20a and miR-

30c, were highly expressed in non-implanted embryos, in comparison to implanted embryos. In- silico analysis suggested involvement that the two miRNAs had roles in 23 implantation-related pathways (Capalbo et al., 2016). To determine the origin of SM miRNAs, Capalbo and

colleagues performed a comparative analysis of miRNA expression between blastocyst SM, TE cells, and ICM cells. The researchers concluded that miRNAs from blastocyst SM were primarily from TE cells (Capalbo et al., 2016). It is postulated that TE cells use miRNAs as a paracrine signal for communicating with the endometrium. Depending on an embryo’s viability,

TE cells can secrete different miRNAs that can influence endometrium gene expression to either support or hinder implantation (Capalbo et al., 2016). Although Capalbo and colleagues did not perform studies to confirm their hypothesis, recent studies do indicate that gene expression can change in endometrial cells when co-cultured with embryo derived miRNAs (Cuman et al.,

2015; Gross, Kropp, & Khatib, 2017b). The endometrial genes that are influenced by embryonic miRNAs have roles in immune system, modulation of epithelial cell invasion, artery remodelling, and hormone receptor expression (Gross et al., 2017b). Overall, evidence suggest that blastocyst-derived miRNAs in SM may be used to facilitate maternal-to-embryo crosstalk.

Rationale miRNAs are involved in many pathways regulating the development of the pre-implantation embryo ranging from gametogenesis, fertilization, EGA, compaction, and blastocyst formation.

In addition, miRNAs have been detected in the SM of embryo culture systems. Research have indicated that these miRNAs are of embryonic origin and are primarily derived from TE cells of the developing blastocyst. miRNAs can also be endogenously found in culture media due the use of serum. Regardless of miRNA origins, research show that distinct miRNAs expression in the

SM can be detected between embryos of differing morphology and implantation outcome. More importantly, secreted miRNAs have the ability to alter gene expression in surrounding cells and tissues such as neighbouring embryos grown in culture and the endometrium. Therefore, miRNAs have the potential to act as embryotropic factors influencing the growth, development and implantation potential of embryos.

To date, the majority of research profiling miRNAs in SM were blastocyst derived.

Although practical to serve as an adjunct assessment method for embryo quality, it does not capture the dynamic miRNA turnover occurring at earlier stages. Potentially, the intracellular miRNAs regulating cleavage, EGA, and compaction may also be detectable in the SM.

Therefore, profiling at earlier stages may reveal early indicators of normal and aberrant embryo development. With the potential to serve as paracrine regulators of gene expression, early classification of embryos in culture may serve to improve blastocyst and implantation rates.

Therefore, the goal of this study was to globally profile the miRNA expression throughout the pre-implantation developmental period.

Hypothesis and Objective

Hypothesis

Embryos at the early, mid, and late stages of pre-implantation development will

have unique miRNA signatures, which will be detectable in the spent media.

Objectives

To test this hypothesis, the following objectives were addressed:

Objective 1: Profile the global miRNA expression in the spent media of group-

cultured embryos at the 2-cell, 8-cell, and/or blastocyst stage of development

Objective 2: Profile the global miRNA expression in the spent media of group-

cultured slow and fast-growing embryos at the 2-cell, 8-cell, and blastocyst stage of

development

Chapter 1

Profiling of global miRNA expression in the spent media of group-cultured embryos at the 2- cell, 8-cell, and/or blastocyst stage of development

INTRODUCTION

Declining cattle fertility is a widely recognized problem resulting in economic losses and culling of cattle (Khatib et al., 2009). Although a multitude of factors contribute to declines in reproductive fitness, early embryonic mortality remains a major cause of infertility (Santos,

Thatcher, Chebel, Cerri, & Galvão, 2004). It is estimated that 75-80% of fetal losses following successful AI occurs within this period and is more frequently observed in repeat service cattle

(Perkel et al., 2015). With declining successes with traditional AI, the industry has looked towards the use of IVF systems, as an alternative means of attaining embryos for transfer.

According to the IETS, the period of 2013-2017 has seen consistent increases in embryos being produced, with 2017 seeing almost one million embryos produced in-vitro (Perry, 2014, 2015,

2016, 2017; Viana, 2018). Although the rise of IVF systems is promising as it allows for the selection of potentially genetically superior gametes and embryos, its uses within the industry has only seen limited success as pregnancy and calving rates remain similar when compared to natural breeding or AI (Calhaz–Jorge et al., 2017). Combining limited successes with higher cost associated to materials and skilled labour requirements, the mainstream use of IVF systems in the industry is yet to be seen.

A limitation in the IVF system that needs to be addressed for its mainstream application in the industry is the methodologies used for assessing embryo viability. Currently, the industry standard is morphological assessment, which involves the embryologist to score embryos on parameters based on appearance such as cleavage rate, blastomere size, blastomere symmetry, number of blastomeres, homology of cytoplasm, and zona pellucida thickness, as outlined by the

IETS (Mori, Otoi, & Suzuki, 2002). Although studies have shown that morphological

assessment do correlate with better implantation and calving rates, the subjective nature of scoring by the embryologist makes the process inconsistent (Rocha et al., 2016). Studies have shown that morphologically high-quality embryos may possess aneuploidies that can alter an embryo’s overall developmental potential (Khatib et al., 2009).

Efforts have been made towards developing other non-invasive forms of assessment such as the characterization of miRNAs in the SM conditioned with embryos. miRNAs are small non- coding RNAs (18-22nt) involved in post-transcriptional regulation of gene expression causing either the translational repression or controlled degradation of the mRNA (Wahid et al., 2010).

Binding in the 3’untranslated region is not completely complementary, therefore allowing miRNAs to have multiple gene targets across an organism (Wahid et al., 2010). These gene targets have been associated to have influences in all biological systems, such as apoptosis, growth, proliferation, reproduction, and development, with expression occurring in a spatially and temporally specific manner (Bartel, 2004).

In embryos, multiple studies have been conducted showing that miRNAs play a significant role in coordinating gene expression throughout this crucial developmental period. It has been determined that certain miRNAs are required at key stages of the pre-implantation developmental period, defined as from day 0 to day 8, with miR-25 and miR-181 being identified as being expressed in the early and late stages, respectively (Tesfaye et al., 2009). It is also noted that a host of miRNAs may play a role in regulating genes that facilitate the processes involved in maternal to embryonic transition, often seen as one of the greatest developmental milestone in early embryonic development (Tesfaye et al., 2009).

In light of new research, it has also been implicated that these temporal changes in miRNA expression may also be reflected in the SM. Multiple studies have shown that miRNAs

can be released into the extracellular environment via containment in exosomes, apoptotic bodies, or bound to proteins. In embryos, studies conducted by Kropp et al., 2014, Rosenbluth et al., 2015, and Kropp et al., 2015, showed that miRNAs can be detected in the SM of bovine and human IVF systems (Kropp & Khatib, 2015; Kropp et al., 2014; Rosenbluth et al., 2014). These studies showed that there are significant differences in the miRNAs detected in the SM of degenerate embryos and embryos that successfully formed into blastocyst. This suggest that miRNAs in the SM may be used as biomarkers of early embryonic health as changes in intracellular miRNA expression may be reflected in the extra-cellular milieu, and that these changes may reflect the overall health or quality of the embryo.

To the best of our knowledge, profiling of miRNAs in the SM have been limited to either a few candidate miRNAs, or only examined the blastocyst stage of development. Preliminary studies conducted in our lab using previously cited candidate miRNAs suggest that miRNAs can be detected in the SM of cleavage-staged embryos. Therefore, the objective of this study is to globally profile the miRNAs present in the spent SM throughout the pre-implantation developmental period using a microarray approach. This method will allow for the discovery of miRNAs detected at earlier stages of embryo development, as well as discovery of miRNAs that are shared between or throughout the pre-implantation developmental period.

Materials and Methods

Chemicals

All chemicals were attained from Sigma-Aldrich, Oakville, ON, Canada, unless stated otherwise.

Oocyte collection and in-vitro production of bovine embryos

Bovine ovaries were collected from a local abattoir (Cargill Canada, Guelph, Ontario) and transported to the laboratory in a thermo flask under phosphate buffered saline (NaCl, 136.9 mM; Na2HPO4, 8.1 mM; KCL, 1.47 mM; KH2PO4, 1.19 mM; MgCl2.6H2O, 0.49 mM) at a temperature of 35-36C. Follicles ranging from 4mm-8mm were aspirated using an

18G vacutainer needle and was suspended in HEPES-buffered Hams F-10 supplemented with

2% donor calf serum (PAA Laboratories Inc., ON, Canada). Cumulus oocytes complexes

(COCs) were washed twice with 3mL synthetic in-vitro maturation (S-IVM) media (Sigma-

Aldrich) and washed once with 3mL S-IVM supplemented with 0.5 g/ml of follicle stimulating hormone, 1 g/ml of luteinizing hormone and 1 g/ml of estradiol. Approximately, groups of 15-20

COCs with homogenous cytoplasm and 4-5 layers of granulosa cells were matured in 80μl drops of S-IVM under a layer of silicone oil for 22-24 hours at 38.5C in an atmosphere of 5% CO2 with 100% humidity. After maturation, the COCs were washed twice with 3ml HEPES buffered

Tyrode’s albumin-lactate-pyruvate medium (HEPES/Sperm TALP) supplemented with 15%

BSA (0.0084 mg/ml final; fatty acid free) and washed twice with 3mL IVF-TALP (IVF-TALP consisting of Tyrode’s solution, supplemented with 15% BSA and 2 mg/ml heparin). Approximately 20 COCs were placed in 80μl drops of IVF-TALP under a layer of silicone oil. Frozen thawed bovine sperm was prepared using swim-up technique. Thawed sperm

was placed in HEPES/Sperm TALP and incubated for 45 minutes at 38.5C in an atmosphere of

5% CO2 with 100% humidity prior to centrifugation at 200g for 7 minutes. The COCs and sperm

6 were co-incubated at a final concentration of 1.0 x 10 at 38.5C in 5% CO2 with maximum humidity. At 18 hours post fertilization (hpf) , the presumptive zygotes were denuded by gentle vortexing for 90 seconds, followed by washing twice with 3ml HEPES/Sperm TALP, and once with in-vitro culture (IVC) media ( CaCl2•2H2O, 1.17 mM; KCL, 7.16 mM; KH2PO4, 1.19 mM; MgCl2•6H2O, 0.49 mM; NaCl, 107.7 mM; NaHCO3, 25.07 mM, Na lactate (60% syrup),

3.3 mM; ChemiconMillipore, Billerica, MA, USA) supplemented with 50μL of 100x non- essential amino acids (glycine, L-alanine, L-asparagine, L-aspartic acid, L-glutamic acid, L-proline,

L-serine; all 0.2 mM final), 100μL 50x essential amino acids (L-arginine hydrochloride, 0.6 mM final; L-cysteine, 0.1 mM final; L-histidine hydrochlorideH2O, 0.2 mM final; L-isoleucine, 0.4 mM final; L-leucine, 0.4 mM final; L-lysine hydrochloride, 0.4 mM final; L-methionine, 0.1 mM final; L-phenylalanine, 0.2 mM final; L-threonine, 0.4 mM final; L-tyrosine, 0.2 mM final; L- tryptophan, 0.05 mM final; L-valine, 0.4 mM final), 25μL of sodium pyruvate (0.00886 mg/ml final), 2.5μL of gentamicin (25 mg/ml final; all from Invitrogen, Burlington, ON, Canada), and

280μl of 15% bovine serum albumin (0.0084 mg/ml final). Approximately 30 presumptive zygotes (PZ) with homogenous cytoplasm were cultured in 30μl of IVC media under silicone oil at 38.5C in an atmosphere of 5% CO2, 5% O2, 90% N2 and cohorts of embryos were cultured to the 2-cell, 8-cell and/or blastocyst stage of development and SM was collected at specific HPF after each desired time stage was reached (Table 1).

Cell Stage Hour-Post Fertilization (HPF)

2-cell 18-30

8-cell 60-80

Blastocyst 168-192

Table 1: SM collection schedule at various stages (adapted from Van Soom et al., 1997; Perkel and Madan, 2017).

Collection of spent in-vitro culture media

Prior to collection of SM at each developmental stage, developmental rates for 2-cell, 8- cell and blastocyst formation was recorded to ensure that culture conditions were representative of average IVP production rates as reported by (Van Soom et al., 1997). Following this, the embryos were removed from the IVC drops, washed 5 times in PBS, and flash frozen in liquid nitrogen and stored at -80C for future downstream gene analysis. Approximately 25μL of SM was collected form each drop, and samples were pooled together in 1.5mL Eppendorf tubes for each of the developmental stage analyzed. The pooled samples were flash frozen in liquid nitrogen and stored at –80C until RNA processing. In total, 3000μL, 3300μL, and 1250μL of

SM was collected from 2-cell, 8-cell, and blastocyst staged embryos, respectively. In addition,

1050μL of unconditioned media, which did not contact any embryos, was collected to serve as a negative control. miRNA extraction

miRNA extraction was isolated from SM and plain media using a RNeasy mini kit

(Qiagen, Hilden, Germany), as downstream array analysis required total RNA sample input.

Briefly, 350μL of SM and plain media was aliquoted to a 2.5mL Eppendorf tube and equal volumes of QIAzol lysis reagent was added, vortexed for 20 seconds, and placed on the benchtop at room temperature for 10 minutes. This was followed by the addition of 350μL of chloroform and incubated for 2 minutes at room temperature, prior to centrifugation at 12g

(15,000 RPM) at 4C for 15 minutes. After, the supernatant was placed into the RNeasy min

elute spin column for total RNA separation. Once all the supernatant was processed, washing steps using buffer RWT, buffer RPE, and 80% ethanol, was performed as per manufacturer protocol. The RNA was eluted using 30μL of RNAse-free water and immediately stored in

-80C prior to microarray analysis. In total, 3 biological replicates of pooled SM from each timed stage of development and plain media was processed and prepared for microarray analysis. miRNA microarray hybridization

Microarray processing was all conducted by our collogues at Genome Quebec (McGill

University, Montreal, Quebec). Briefly, microarray profiling was conducted using the Affymetrix

GeneChip miRNA 4.0 array (Affymetrix, Santa Clara, CA, USA), according to manufacturer’s instructions and as described previously by Reza and colleagues (Reza et al., 2018). Briefly, each sample of RNA was labelled using the FlashTag Biotin RNA Labelling Kit (Genisphere, Hatfield,

PA, USA), quantified, fractionated, and hybridized to the miRNA microarray. The protocol is as follows: labelled RNA is heated to 99C for 5 minutes, then heated at 45C for 5 minutes, prior to hybridization via constant agitation at 60rpm for 16 hours at 48C on an Affymetrix 450 Fluidics

Station. The microarray chip is washed and stained with Genechip Fluidics Station 450, prior to being scanned with the use of an Affymetrix GCS 3,000 scanner and computed using the

Affymetrix Genechip command console software.

Statistical analysis

For Genechip microarray analysis, CEL files were imported in the Affymetrix

Transcriptome Analysis Console 4.0.2.15 (TAC) software in RMA+DMG (all organisms) mode. Comparative analysis was carried out between SM samples (2-cell, 8-cell, and blastocyst) and control (plain media) using fold-change and independent T-test, in which the null hypothesis was that no difference exists between the 2 groups. Probes were differentially expressed at a

fold-change of ≤ -2 or  2 (p-value < 0.05), where probe-sets were considered expressed if 

50% of samples have a detectable above background (DABG) values below DABG threshold of

< 0.05 and a false discovery rate (FDR) < 0.05. All statistical test and visualization of differentially expressed genes were done using TAC software.

Target Pathway Prediction of differentially expressed miRNAs

Functional analysis of DEM detected at 3 SM conditions was performed using

TargetScan Human 7.2 (http://www.targetscan.org/vert_71/) under Cow annotation, to construct a gene-list from the DEM. Genes with a cumulative context score of <-0.75 was included in the list. From the gene-list, gene-set enrichment analysis (GSEA) was conducted using PANTHER

(http://pantherdb.org/) with Bos Taurus selected for the organism option and functional classification, under gene-ontology: Biological Processes, was conducted. Pathways with a p- value of <0.05 was considered significantly enriched.

Results

Differentially expressed miRNAs in 2-cell, 8-cell, and blastocyst SM

Overall, a total of 111 miRNAs were differentially expressed in the SM conditioned with

2-cell, 8-cell, and/or blastocyst embryos, when compared to plain media. Thirteen miRNAs were detected in the 2-cell SM, 21 miRNAs were detected in 8-cell SM, and 77 miRNAs were detected in blastocyst SM (Figure 2). Overlapping the miRNA list from each SM condition allowed for the identification of condition-specific miRNAs and miRNAs shared between 2 or more conditions (Figure 3). Six miRNAs were solely detected in 2-cell SM, in which 2 were upregulated (bta-miR-2421 and bta-miR-2297) and 4 were downregulated in the SM (bta-miR-

296-5p, bta-miR-106b, bta-miR-122, and bta-miR-760-5p) (Figure 4). All the miRNAs detected in the 8-cell SM were shared between one or more SM conditions and no miRNAs were unique

to 8-cell SM. Of the 77 miRNAs identified in blastocyst SM, 56 miRNAs were exclusive to the

SM condition. Fifty-three miRNAs were upregulated (Supplementary table 1) and 3 miRNAs were downregulated in the media (Figure 5). The remaining detected miRNAs were shared between 2 or more SM conditions.

Figure 3. Venn diagram of DEM from 2-cell (yellow), 8-cell (green), and blastocyst SM (blue). Overlapping results showed DEM shared between 2 or more groups (14 miRNAs between 8-cell and Blastocyst SM; 7 miRNAs between all 3 SM conditions).

Total = 6

Up-regulated Down-regulated

Figure 4. A total of 6 miRNAs were significantly expressed in 2-cell SM. Two miRNAs were up-regulated, while 4 miRNAs were down-regulated in 2-cell SM.

Total = 23

Up-regulated Down-regulated

Figure 5. The top 20 of 53 significantly up-regulated miRNAs are graphed with the 3 significantly downregulated miRNAs exclusive to blastocyst SM.

Differentially expressed miRNAs shared between 2 or more SM conditions

Overlapping of the gene-list identified miRNAs that were co-detected in 8-cell and blastocyst SM and all 3 SM conditions. Fourteen miRNAs (bta-miR-296-3p, bta-miR-3141, bta- miR-1584-5p, bta-miR-2888, bta-miR-2374, bta-miR-2893, bta-miR-2899, bta-miR-1343-5p, bta-miR-2328-3p, bta-miR-2887, bta-miR-2487, bta-miR-92a, bta-miR-149-3p, and bta-miR-

1246) were co-detected in 8-cell and blastocyst SM (Figure 6). All 14 miRNAs were upregulated in comparison to 2 cell SM and unconditioned media. Interestingly, the degree of upregulation increased between 8-cell and blastocyst SM. Seven miRNAs were co-detected in all 3 SM conditions. Six miRNAs were upregulated (bta-miR-2281, bta-miR-2900, bta-miR-2885, bta- miR-1777a, bta-miR-2305, and bta-miR-1777b) (Figure 7) in comparison to unconditioned media and the degree of upregulation increased from one condition to another. One miRNA was consistently downregulated (bta-miR-450b) in comparison to unconditioned media and the degree of downregulation was consistent across all 3 conditions.

Total = 14

2-Cell SM Blastocyst SM Plain Media 8-Cell SM Figure 6. Fourteen miRNAs were significantly shared between 8-cell and blastocyst SM. All miRNAs increased in levels of up-regulation from the 8-cell to the blastocyst SM.

Total = 7

2-Cell SM 8-Cell SM Blastocyst SM Plain Media Figure 7. Seven miRNAs were significantly shared between all three SM conditions. 6 miRNAs increased in levels of up-regulation, while 1 miRNA remained consistently down-regulated (bta- miR-450b).

Predictions of miRNA-mRNA targets for stage-specific and shared differentially expressed miRNAs

2-Cell SM

miRNA-mRNA target prediction identified 44 mRNAs as potential targets for the 6 miRNAs identified exclusively to 2-cell SM (supplementary table 2). When inputted into

PANTHER, 42 genes were associated with the 44 predicted mRNAs, with the majority of genes clustering under cellular process (GO:0009987), biological regulation (GO:0065007), and metabolic process (GO:0008152) (Figure 8). The highly enriched genes were predicted targets of bta-miR-2421 (Table 2).

Blastocyst SM

Due to the high numbers of miRNAs detected in blastocyst SM, only the top 20 upregulated and 3 downregulated miRNAs were considered for miRNA-mRNA target

prediction. Of the top 20 upregulated miRNAs, 5 miRNAs (bta-miR-320a, bta-miR-3432, bta- let-7c, bta-miR-191, and bta-miR-378) were excluded from analysis due to predicted genes not meeting the cumulative context score of <-0.75 cutoff or not being found on the TargetScan database. From the remaining 18 miRNAs, a total of 218 mRNAs were identified as possible targets for blastocyst-specific miRNAs (supplementary table 3). When inputted into PANTHER,

186 genes were associated with the 218 predicted mRNAs, with the majority of genes clustering under cellular process (GO:0009987), biological regulation (GO:0065007), and metabolic process (GO:0008152) (Figure 8). The highly enriched genes were predicted targets of bta-miR-

7865, bta-miR-2295, and bta-miR-3613b (Table 2).

8-Cell and Blastocyst Shared miRNAs

Bta-miR-296-3p and bta-miR-2487 did not meet the cutoff criteria and was not found in the TargetScan database, respectively, thus was excluded from the analysis. Of the remaining 12 miRNAs, a total of 194 mRNAs were identified as potential targets (supplementary table 4).

When inputted into PANTHER, 168 genes were associated with the 194 predicted mRNAs, with the majority of genes clustering under cellular process (GO:0009987), biological regulation

(GO:0065007), and metabolic process (GO:0008152) (Figure 8) The highly enriched genes were predicted targets of bta-miR-1343-5p and bta-miR-2899 (Table 2). miRNAs common to 2-cell, 8-cell, and blastocyst SM

Bta-miR-2881 was not found in the database, thus was excluded from the overall analysis. Of the remaining 6 miRNAs co-detected in all 3 SM condition, a total of 527 miRNAs were identified as potential targets (supplementary table 5). When inputted into PANTHER, 445 genes were associated with the 527 predicted mRNAs, with the majority of genes clustering under cellular process (GO:0009987), biological regulation (GO:0065007), and metabolic

process (GO:0008152) (Figure 8). The highly enriched genes were predicted targets of bta-miR-

2305, bta-miR-2900, and bta-miR-1777a (Table 2).

Overall, GSEA analysis revealed that the majority of the miRNAs detected in the 4 conditions (2-cell SM, 8-cell SM, 8-cell/blastocyst SM, and common in all 3 SM) had predicted mRNA targets involved in cellular process, biological regulation, and metabolic process. For 2- cell SM, bta-miR-2421 and bta-miR-760-5p mRNA targets were over-represented after enrichment. In blastocyst SM, bta-miR-7865, bta-miR-2295, and bta-miR-3613b mRNA targets were highlighted most during GSEA analysis. For miRNA shared between two or more SM conditions, bta-miR-1343-5p, bta-miR-2899, bta-miR-2888, bta-miR-2305, bta-miR-2900, and bta-miR-1777a mRNA targets were highly represented in the 3 biological processes highlighted in PANTHER.

Annotated roles of differentially expressed miRNAs in literature

From the 111 miRNAs identified across 3 SM conditions, 32 miRNAs have been previously annotated in literature. 18 miRNAs have been identified in embryo related studies,

14 miRNAs in cancer-related studies, and 5 miRNAs in both embryo and cancer related studies. miRNAs (Table 3). It is important to note that all previously annotated miRNAs were exclusively detected in blastocyst SM.

Highly Represented miRNAs Genes represented in Cellular Process, Biological Regulation, and Metabolic Process 2-Cell SM bta-miR-2421 TCF4, POU6F2, RUNX1T1, TNRC6C, ONECUT2, TNRC6B, THRB Blastocyst SM bta-miR-7865 MZB1, FOSB, LAMTOR1, WDTC1, THRA

bta-miR-2295 MAPK8IP2, H1FX, SCRT1, MTA3, EOMES

bta-miR-3613b SIK2, USP42, CDK12, GLE1, IKZF4

Shared in 8-cell and bta-miR-1343-5p TFCP2L1, KCTD17, SIX5, PRX, SERPINE3 blastocyst SM bta-miR-2899 DLX1, MTA1, TBC1D14, HEYL, HCFC1

Common to 2-cell, 8-cell, bta-miR-2305 PLA2G2F, SMARCC2, SPRY4, CX3CL1, TEAD2, and blastocyst SM LMX1B, RUNX3, ERF, TBX6, ELK1, SOX15, SPRED2, DAGLA, HNF4A, COMMD7, ZFHX2, RNF144A, KMT2B, MAPK12, FEV, TIMP2, IKZF4, CSDC2, DERL3, CIC, RNPS1, SAMD4B bta-miR-2900 FOXJ2, DDA1, PTK2B, HOXA3, CNOT3, MAPK1, SRRM4, NRBP1, PPARD, TRAF3, NODAL, PRKCG, PIAS4, TCF7L2 bta-miR-1777a MSI1, CACNG7, TBX5, BRSK1, CRTC1, ZC3H4, TBX10, IGF1R, LEMD2, CTIF, HIF3A, DUSP16, SOX12 Table 2. miRNA-mRNA targets predicted to have roles in the top 3 biological pathways represented in GSEA analysis.

Study-Type miRNA Embryo- SM media profiling miR-24-3p, miR-191, miR-2887 Related Role in implantation miR-320a*, let-7b, miR-23b-3p*, miR-23a, miR-3141, miR- 92a*, miR-1246 Role in differentiation miR-371, miR-296-5p/3p*, miR-106b*, miR-125b Found in germ cells miR-3432, miR-2487, miR-2885, miR-1777b Cancer- Onco-genic miR-106b*, miR-760-5p, miR-371, let-7d, miR-222, miR- Related 378, miR-1343-5p, miR-92a* Tumour-suppressive miR-296-5p/3p*, miR-320a*, miR-320b, miR-23b-3p*, miR- 342, let-7c, let-7d, miR-191, miR-149-3p, miR-1246, miR- 450b *found in both embryo and cancer related studies

Table 3. List of miRNAs detected in all 3 SM conditions previously annotated in literature. The majority (18 miRNAs) have been profiled in embryo-based studies, while 14 miRNAs were explored in cancer-related studies. 5 miRNAs were cited in both embryo and cancer related studies. Discussion

To the best of our knowledge, this study was the first to characterize miRNAs in the SM throughout the pre-implantation period. We were able to identify miRNAs in the SM at early, mid, and late stages of development. The most diverse expression of miRNAs was detected in blastocyst SM. This finding is consistent with the works of other researchers (Gross et al., 2017b;

Kropp & Khatib, 2015; Kropp et al., 2014; Rosenbluth et al., 2014) and is largely attributable to the genetic, molecular, and cellular changes occurring during this stage of embryo development.

An embryo during this stage rapidly proliferates and differentiates to form the ICM and trophectoderm. Differentiation is a highly choreograph events that requires strict modulation of gene expression. Potentially, miRNAs play a role in synchronizing the events necessary for normal blastocyst development. Therefore, miRNA expression may increase during this stage of development, which is reflected in the SM.

Another explanation to the high numbers of miRNAs detected in blastocyst SM is due to the high cell numbers present at this stage of development. Prior to compaction and blastocyst formation, a growing embryo is made up of relatively few cells, enough to be distinguishable under stereomicroscope. With such few cells prior to compaction, it is believed that miRNA

concentrations are far too low to be detected with current assays. In a recent study, it was determined that the majority of miRNAs detected in blastocyst SM was trophectoderm derived.

Thus, this may contribute to the low numbers of miRNAs detected in earlier stages as the trophectoderm only forms at later stages of embryo development. Interestingly, the authors also postulated that the blastocyst secretes miRNAs as a method of paracrine communication with the endometrium. The diverse population of miRNAs secreted by trophectoderm cells may be up taken by endometrial cells. Once inside, the miRNAs may modulate gene expression to either favor or prevent the implantation of a blastocyst. Therefore, the diverse expression of miRNAs in blastocyst SM may serve a functional role in implantation and/or be a direct by-product of the high cell numbers present at this stage of development.

One finding that was unique in this study was the detection of miRNAs in cleavage stage embryos. Specifically, we were able to detect 6 miRNAs exclusive to 2-cell SM. Moreover, 2- cell SM was the only condition examined that had more miRNAs down-regulated (4 miRNAs) than up-regulated (2 miRNAs). These findings are interesting because this is the first report of miRNAs being detected in 2-cell SM. The wide coverage of miRNAs available on the microarray may have allowed for the detection of miRNAs. The genechip miRNA 4.0 array used for this study had over 46,228 probes comprising of 7815 probe sets from 71 different species.

Since the majority of the miRNAs identified in 2-cell SM and 8-cell SM are ones not previously annotated in embryo-related work, it is possible that the assays previous researchers used did not contain the probes present in our study. Most studies either examined a select few candidate miRNAs using qPCR or a wider population with qPCR array. Although both methods allow for higher sensitivity, lower sample input, and lower false detection rates, both approaches have fewer probe sets than the ones used in this study.

In addition to having more probe-sets, group culturing of embryo may have impacted miRNA detection. Prior studies examining miRNAs throughout the pre-implantation period performed either single-embryo culture or had a lower ratio of embryos to culture media volume.

These culturing conditions allowed researchers to discern the miRNAs in the SM to specific embryos. However, as mentioned before, low cell numbers contribute to the lack of miRNAs detected in media. Therefore, previous researcher was only able to detect miRNAs from blastocyst conditioned media, whereby cell numbers are higher. In this study, embryos were cultured in groups of 30 in 30μl of IVC media. The higher concentration of embryos in the media may have compensated for the few cells available to secrete and/or uptake miRNAs in the media.

Therefore, the diversity in the probe-sets present in the array used, in conjunction with higher embryos concentrations may have allowed for the detection of miRNAs in SM cultured with cleavage staged embryos.

Another interesting result from the miRNAs detected in 2-cell SM is that it is the only condition where more down-regulated miRNAs were detected in comparison to upregulated miRNAs. This indicates that the 2-cell embryo may be capable of up-taking miRNAs from the extracellular environment. Although undocumented in cleavage stage embryos, previous research has demonstrated that morula-stage embryos are capable of up-taking miRNAs, with subsequent effects on gene expression affecting blastocyst development. Our findings suggest that embryos may be able to up take miRNAs throughout the preimplantation period. This highlights a potential area for the development of therapeutics, whereby certain miRNAs can be supplemented into culture media to modulate gene expression that favors normal embryo development.

Conversely, our findings also highlight the gap in knowledge regarding the native miRNA population found in commercially available culture media. Presently, most culture medias use serum as a means of improving blastocyst yield. Serum contains various growth factors, adhesion factors, trace hormones, lipids, and minerals essential for the in-vitro culture of embryos. Since miRNAs can be found in a wide array of biofluids, it can be postulated that the serum used in culture media contain miRNAs. Currently, the miRNA profile of culture medias are not known and/or disclosed, however, research have shown that miRNAs are present in plain culture media. Instead of modulating genes to stimulate growth, miRNAs may work to negatively inhibit growth and development as observed by Kropp and colleagues in 2015. Since our findings suggest that embryos may be capable of up-taking miRNAs as early as the 2-cell stage, a detailed characterization of miRNAs in culture media may serve to improve the efficiency of in-vitro production systems. miRNAs known to negatively affect growth may be removed with the subsequent supplementation of miRNAs known to stimulate development.

Focusing on our findings in 8-cell SM, it was determined that 21 miRNAs were detected with no miRNAs exclusively found in the media. It is important to note that EGA occurs during the 8-cell stage in bovine embryos. Perhaps that lack of miRNAs exclusive to 8-cell SM may be related to this developmental event. EGA is the period in embryo development when maternally inherited transcripts are degraded, and transcription of embryonic genome begins (Graf et al.,

2014). miRNAs, on the other hand, serve to primarily inhibit gene expression. Tesfaye and colleagues do report that intracellular miRNA expression in embryos change dynamically. Some miRNAs are highly expressed in early and late stages of development, while being absent during

EGA. Therefore, our findings suggest that miRNA expression may temporally dampened during the 8-cell stage to allow for the events of EGA to proceed.

Although no miRNAs were exclusively detected in 8 cell SM, 7 of the 21 miRNAs were shared between across all 3 SM conditions. This is a significant finding as this is the first instance of miRNAs being demonstrated to have consistent expression in the SM throughout the preimplantation period. Moreover, 6 miRNAs increased, and 1 miRNA stayed consistently downregulated in expression from one condition to another. Therefore, it may indicate that these miRNAs may play a housekeeping role throughout embryonic development. GSEA analysis did reveal that bta-miR-2305, bta-miR-2900, and bta-miR-1777a mRNA targets were overrepresented in pathways pertaining to cellular process, biological regulation, and metabolic process. Regulation of these pathways are all necessary requirements for embryo development.

Perhaps, the promiscuous nature of miRNAs may allow certain miRNAs to be expressed throughout development but regulate different genes in a stage specific manner.

It is important to note that bta-miR-450b was the only miRNA consistently downregulated across all conditions. Aside from stage specific up take of miRNAs highlighted from our 2-cell SM result, this finding indicates that certain miRNAs may be up-taken throughout the preimplantation period. Thus, this reinforces the idea of embryo having the ability to interact with its environment. Expression of bta-miR-450b was higher in plain media in comparison to all SM condition, suggesting that this miRNA is native to the commercial media used in the study. There is little known regarding bta-miR-450b in embryo development, but a predicted mRNA target is CAM2KN1(calcium/calmodulin-dependent protein kinase II). In previous studies, it has been demonstrated that CAM2KN1 is an oncogene present in prostate cancer tissue (Wang et al., 2014). When CAM2KN1 expression was reduced, cell proliferation was impaired and apoptosis was increased (Wang et al., 2014). Therefore, it is possible that up take of bta-miR-450b in embryos may cause a subsequent decrease in expression of CAM2KN1

or other oncogenes, thereby impairing cell proliferation. Although this has not been explored, it once again highlights the need to profile the miRNA expression across commercially available medias.

Aside from the 7 miRNAs expressed across all 3 SM conditions, our study also detected

14 miRNAs that increased in expression in the SM from the 8-cell-to the blastocyst stage of development. It is known that specific miRNAs are expressed in the embryo from the period of

EGA to blastocyst formation, however, no study have been able to demonstrate these changes in the extracellular environment. Therefore, the 14 miRNAs that we identified may play a role in initiating and facilitating an embryos development post EGA. Although none of the miRNAs co- detected in 8-cell and blastocyst SM have been annotated in previous SM and embryo studies,

GSEA analysis revealed that predicted mRNA targets impact pathways regulating proliferation, differentiation, and metabolism.

From a broad perspective, the vast majority of miRNAs detected in this study have not been previously annotated in literature, thus their functions are widely unknown. However, literature search did reveal that 32 of the 56 miRNAs detected in blastocyst SM have been previously explored in research. miR-24-3p, miR-191, and miR-2887 have been detected in SM cultured with embryos that failed to progress to the blastocyst stage of development (Kropp &

Khatib, 2015). Moreover, miR-320a, let-7b, miR-23b-3p, miR-23a, miR-3141, miR-92a, miR-

1246 have been profiled in SM conditioned with embryos that failed to implant (Capalbo et al.,

2016). Although our study did not separate embryos based on blastocyst outcome and implantation, it appears that the miRNAs detected in blastocyst SM may be derived from poor quality blastocyst and/or ones that arrested. It has been observed that poorer quality embryos

have a more dynamic expression of proteins and metabolites (referenced). Potentially, this dynamic expression may also translate with miRNAs in SM.

It should also be noted that a cohort of blastocyst exclusive miRNAs have been previously annotated in cancer-related studies. Specifically, some blastocyst specific miRNAs have been cited to have either an oncogenic and/or tumour suppressive role in various tissues such as kidney, liver, prostate, and ovary. It is well established that parallels exist in the biological pathways used in embryogenesis and tumorigenesis. Molecular cues to govern morphological change, differentiation, proliferation, and invasion are all used by the pre- implantation embryo to establish pregnancy. These same processes are used by tumour cells to enhance growth, recruit cells, and coordinate spread to distant tissues. Therefore, connecting the findings of oncogenic studies may be invaluable in unlocking the roles of miRNAs in embryogenesis.

Overall, this study was able to detect miRNAs in the SM across the pre-implantation period. Cross-referencing of miRNAs from each condition allowed for the identification of stage-specific miRNAs and those shared across 2 or more SM conditions. All of the miRNAs identified have gene targets relating to pathways regulating cellular processes, biological regulation, and metabolism. Although the roles of the miRNAs identified are mostly unknown, those that have been identified suggest that miRNAs are indicative of intrinsic embryonic physiology. Future research should focus on validating the miRNAs identified in the study, both intracellularly and within the SM. From these findings, miRNA-mRNA target knockdowns can be performed to elucidate the roles miRNAs play in early embryo development.

Chapter 2

Profiling of global miRNA expression in the spent media of group-cultured slow and fast- growing embryos at the 2-cell, 8-cell, and blastocyst stage of development

INTRODUCTION

It is well established that embryos of differing developmental potential have different genomic, proteomic, and metabolomic profiles (Rødgaard et al., 2015). These variations in expression are detectable intracellular, and more recently, in the spent culture media of IVF systems (Rødgaard et al., 2015). Analysis of small molecules found in the spent media (SM) has the possibility of revealing biomarkers related to intrinsic embryo physiology. Strong evidence suggest that array-based metabolomics and miRNA analysis are good candidates for adjunct assessment methods of embryo quality. Metabolomics and miRNA profiling of embryo SM has revealed distinct signatures between embryos of different morphological appearance (Kropp &

Khatib, 2015), chromosomal status (Z. Yang et al., 2012), sex (Gross et al., 2017b), and implantation outcome (Capalbo et al., 2016).

Advancements in time-lapse imaging has also revealed the importance of morpho-kinetic assessment in assessing embryo quality (Aparicio et al., 2013). This relatively new technique has showed that the timing of the onset and duration of key morphological events, such as cleavage, compaction, and blastocyst formation, may indicate normal and aberrant embryo development.

Since in-vivo embryos develop faster than their in-vitro counterparts, it is commonly accepted that faster developing in-vitro embryos are healthier (La Salle, 2012). In fact, research has shown that in-vitro embryos that cleave earlier have higher blastocyst rates. Timing may be indicative of stress experienced by an embryo. The absence or low levels of stress factors such as reactive oxygen species, may mean that embryos can develop faster as less time can be spent initiating repair pathways (La Salle, 2012).

However, other groups have presented a counter idea that the slower growing embryo has more time to correctly initiate and choreograph the events of embryogenesis. Research by Velker

and colleagues compared slow-growing (SG) and fast-growing embryos, with in-vivo embryos, on factors such as methylation status, expression of imprinted genes, embryo cell number, and morphology (Velker, Denomme, & Mann, 2012). Their findings showed that SG embryos were most similar to in-vivo embryos on all parameter measured (La Salle, 2012). Genomic imprinting and expression of metabolic markers in SG embryos closely mirrored those of in-vivo embryos. Velker and colleagues postulate that FG embryos may transition too rapidly during the first few embryonic stages causing an inability to maintain epigenetic information (Velker et al.,

2012). It appears that embryos grow within a time range, whereby anything too slow may indicate a pathological condition, while too fast may signify an embryo erroneously rewired to move onto the next developmental stage.

To date, few studies have profiled SM conditioned with SG and FG embryos. One study that did so examined the metabolites present in the SM cultured with SG and FG embryos. Using

NMR microscopy, Perkel and Madan were able to detect distinct metabolic signatures in the SM between SG and FG embryos at the 2-cell, 8-cell, 16-cell, and blastocyst stage of development

(Perkel & Madan, 2017). Specifically, their data showed distinct differences between media cultured with the 4-cell SG and FG embryos for pyruvate, and at 16-cell stage for acetate, tryptophan, leucine/isoleucine, valine, and histidine. Four-cell SG embryos had higher consumption of pyruvate, while 16-cell SG embryos released more acetate into the media, in comparison to their FG counterparts (Perkel & Madan, 2017). Acetate is produced when there is an over-abundance of acetyl-coa, such as in situations where the kreb cycle or electron-transport chain is malfunctioning. Since both processes lie within the mitochondria, this observation suggest that SG embryos exhibit some level of mitochondrial dysfunction resulting in a metabolic disturbance.

Embryonic mitochondrial biogenesis analysis conducted in our lab revealed that metabolic distress maybe related to mitochondrial dysfunction. In this study, GLYCOX and

OXPHOS genes expression were examined in SG and FG embryos at the 2-cell, 8-cell, morula, and blastocyst stage of development (Merrill, 2016). GLYCOX and OXPHOS genes regulate pathways in embryo energy production, whereby OXPHOS is a mitochondrial dependant pathway and dominants in early embryo development, while GLYCOX dominant in late embryo development (Merrill, 2016). Data indicates that SG embryos had higher expression of both

OXPHOS and GLYCOX genes at all time-stages in comparison to FG embryos. This over- expression may serve as a compensatory mechanism for mitochondrial dysfunction occurring in

SG embryos. Overall, data from our lab suggest that metabolic assays are sensitive in detecting differences between embryos growing at different rates (Merrill, 2016).

Recent data from our lab has also shown that the miRNA expression in the SM are different between SG and FG embryos. In this preliminary experiment, 6 candidate miRNAs, miR-196a, miR-181a, miR-155-5p, miR-148a, miR-302c, miR-370 and snRNA U6, were examine in the SM conditioned with SG and FG embryos at the 2-cell, 8-cell, 16-cell stage, and blastocyst-staged embryos (Yang et al., 2017). MiR-181a, miR-148a, miR-155-5p and snRNA

U6 had increased expression in SG embryos in comparison to FG embryos (Yang et al., 2017).

This preliminary study shows SM profiling is sensitive in detecting miRNA differences between embryos growing at different developmental rates. Therefore, the aim of the present study was to globally profile, using a heterologous miRNA microarray, the miRNAs expression in the SM of

SG and FG embryos at the 2-cell, 8-cell, and blastocyst stage of development.

Materials and Methods

Chemicals

All chemicals were attained from Sigma-Aldrich, Oakville, ON, Canada, unless stated otherwise.

Oocyte collection and in-vitro production of bovine embryos

18G vacutainer needle and was suspended in HEPES-buffered Hams F-10, supplemented with

2% donor calf serum (PAA Laboratories Inc., ON, Canada). Cumulus oocyte complexes (COCs) were washed twice with 3ml synthetic S-IVM (Sigma-Aldrich) and washed once with 3mL S-

IVM supplemented with 0.5 g/ml of follicle stimulating hormone, 1 g/ml of luteinizing hormone and 1 g/ml of estradiol (Sigma-Aldrich). Approximately, groups of 15-20 COCs with homogenous cytoplasm and 4-5 layers of granulosa cells were matured in 80μl drops of S-IVM under a layer of silicone oil for 22-24 hours at 38.5C in an atmosphere of 5% CO2 with

100% humidity. After maturation, the COCs were washed twice with 3ml HEPES buffered

Tyrode’s albumin-lactate-pyruvate medium (HEPES/Sperm TALP) supplemented with 15%

BSA (0.0084 mg/ml final; fatty acid free, Sigma-Aldrich) and washed twice with 3mL IVF-

TALP (IVF-TALP consisting of Tyrode’s solution, supplemented with 15% BSA and 2 mg/ml heparin (Sigma-Aldrich)). Approximately 20 COCs were placed in 80μl drops of IVF-TALP under a layer of silicone oil. Frozen thawed bovine sperm was prepared using swim-up technique. Thawed sperm was placed in HEPES/Sperm TALP and incubated for 45 minutes at

38.5C in an atmosphere of 5% CO2 with 100% humidity prior to centrifugation at 200g for 7 minutes. The COCs and sperm were co-incubated at a final concentration of 1.0 x 106 at 38.5C in 5% CO2 with maximum humidity. At 18 hour post fertilization (hpf), the presumptive zygotes

(PZ) were denuded by gentle vortexing for 90 seconds, followed by washing twice with 3ml

HEPES/Sperm TALP, and once with in-vitro culture (IVC) media (CaCl2•2H2O, 1.17 mM;

KCL, 7.16 mM; KH2PO4, 1.19 mM; MgCl2•6H2O, 0.49 mM; NaCl, 107.7 mM; NaHCO3, 25.07 mM, Na lactate (60% syrup), 3.3 mM; ChemiconMillipore, Billerica, MA, USA) supplemented with 50μL of 100x non-essential amino acids (glycine, L-alanine, L-asparagine, L-aspartic acid, L- glutamic acid, L-proline, L-serine; all 0.2 mM final), 100μL 50x essential amino acids (L-arginine hydrochloride, 0.6 mM final; L-cysteine, 0.1 mM final; L-histidine hydrochlorideH2O, 0.2 mM final; L-isoleucine, 0.4 mM final; L-leucine, 0.4 mM final; L-lysine hydrochloride, 0.4 mM final;

L-methionine, 0.1 mM final; L-phenylalanine, 0.2 mM final; L-threonine, 0.4 mM final; L- tyrosine, 0.2 mM final; L-tryptophan, 0.05 mM final; L-valine, 0.4 mM final), 25μL of sodium pyruvate (0.00886 mg/ml final), 2.5μL of gentamicin (25 mg/ml final; all from Invitrogen,

Burlington, ON, Canada), and 280μl of 15% BSA (0.0084 mg/ml final). Approximately 30 PZ with homogenous cytoplasm were cultured in 30μl of IVC media under silicone oil at 38.5C in an atmosphere of 5% CO2, 5% O2, 90% N2. Each 3.5 ml dish contained 6 micro-drops of IVC media (each with a volume of 30 microliters), whereby the PZ were cultured.

Collection of spent in-vitro culture media conditioned with SG and FG embryos

At day 0 of culture, each of the 6 micro drops containing 30 PZ were assigned group numbers ranging from 1-6. This made it possible to follow the same cohort of embryos throughout the pre-implantation period, while collecting SM at specific time-points corresponding to the 2-cell, 8-cell, and blastocyst-stage of development. Microdrops were

classified as a SG or FG group at each time point, based on the percentage of embryos that have reached the desired morphological stage at a given time point. At 18-30 hpf, microdrops were considered SG if the cohort had <50% reach the 2-cell-stage and FG if the cohort had ≥ 50% reach the 2-cell stage. Once the groups were designated SG or FG, the embryos were placed into fresh microdrops of IVC media, retaining their original group number, and placed into the incubator for another 30 hours to reach the 8-cell stage. Approximately 25μl of conditioned media from each microdrop from 2-cell SG and FG groups were collected, pooled and placed in separate Eppendorf tubes. At the 8-cell stage, the cohort of embryos were assessed for 8-cell rate formation and cohorts were considered SG if the 8-cell rate was <50% and FG if the 8-cell rate was ≥ 50%. After designation, the embryos were placed into a fresh microdrop of IVC media, retaining their original group number, and placed into the incubator for another 72 hours to allow for blastocyst formation. Approximately 25μl of conditioned media from each microdrop from 8- cell SG and FG groups were collected, pooled, and placed in separate Eppendorf tubes. At the blastocyst stage, the cohort of embryos were assessed for a final time for blastocyst rate formation and cohorts were considered SG if blastocyst rates was <20% and FG if blastocyst rates was ≥ 20%. After designation, the embryos were taken out of the drop and the SG and FG embryos were placed in separate Eppendorf tubes and flashed frozen. Approximately 25μl of conditioned media from each microdrop from SG and FG groups were collected, pooled, and placed separately in Eppendorf tubes. Multiple IVF runs were completed until 1100μl of SM was collected for each developmental stage: 2-cell SG/2-cell FG, 8-cell SG/8-cell FG, and blastocyst

SG/blastocyst SG.

miRNA extraction

miRNA extraction was isolated from spent and uncondtioned IVC media using a RNeasy mini kit (Qiagen, Hilden, Germany) as downstream array analysis required total RNA sample input. Briefly, 350μL of spent and plain IVC media was aliquoted to a 2.5 mL Eppendorf tube and equal volumes of QIAzol lysis reagent was added, vortexed for 20 seconds, and placed on the benchtop at room temperature for 10 minutes. This was followed by the addition of 350μL of chloroform and incubated for 2 minutes at room temperature, prior to centrifugation at 12g

(15,000 RPM) at 4C for 15 minutes. After, the supernatant was placed into the RNeasy min elute spin column for total RNA separation. Once all the supernatant was processed, washing steps using buffer RWT, buffer RPE, and 80% ethanol, as per manufacturer protocol, was performed. The RNA was eluted using 30μL of RNAse-free water and immediately stored in

–80C prior to microarray analysis. In total, 3 biological replicates of pooled SM from 2-cell

SG/2-cell FG, 8-cell SG/8-cell FG, and blastocyst SG/blastocyst FG groups and plain media was processed and prepared for microarray analysis. miRNA microarray hybridization

Microarray processing was all conducted by our collogues at Genome Quebec (McGill

University, Montreal Quebec). Briefly, microarray profiling was conducted using the Affymetrix

GeneChip miRNA 4.0 assay (Affymetrix, Santa Clara, CA, USA), according to manufacturer’s instructions and as described previously by Reza et al., 2018 (Reza et al., 2018). Briefly, each sample of RNA was labelled using the FlashTag Biotin RNA Labelling Kit (Genisphere, Hatfield,

Station. The microarray chip is washed and stained with Genechip Fluidics Station 450, prior to being scanned with the use of an Affymetrix GCS 3,000 scanner and computed using the

Affymetrix Genechip command console software.

Statistical analysis

For Genechip microarray analysis, CEL files were imported in the Transcriptome

Analysis Console 4.0.2.15 (TAC) software in RMA+DMG (all organisms) mode. Comparative analysis was carried out between SM samples 2-cell SG/2-cell FG, 8-cell SG/8-cell FG, and blastocyst SG/blastocyst FG and control (unconditioned media) using fold-change and independent T-test, in which the null hypothesis was that no difference exists between the 2 groups. Probes were differentially expressed at a fold-change of ≤ -2 or  2 (p-value < 0.05), where probe-sets were considered expressed if  50% of samples have a detectable above background (DABG) values below DABG threshold of < 0.05 and a false discovery rate (FDR)

< 0.05. All statistical test and visualization of differentially expressed genes were done using

TAC software (version 4.0.2.15).

Target pathway prediction of differentially expressed miRNAs

Functional analysis of DEM detected between 2-cell SG/2-cell FG, 8-cell SG/8-cell FG, and blastocyst SG/blastocyst FG SM conditions was performed using TargetScan Human 7.2

(http://www.targetscan.org/vert_72/) under Cow annotation, to construct a gene-list from the

DEM. Genes with a cumulative context score of <-0.5 was included in the list. From the gene- list, gene-set enrichment analysis (GSEA) was conducted using DAVID 6.8

(https://david.ncifcrf.gov/) with the option gene-ontology: biological processes. Pathways with a p-value of <0.05 was considered significantly enriched.

Results

Differentially expressed miRNAs between 2-cell SG vs. 2-cell FG, 8-cell SG vs. 8-cell FG, and blastocyst SG vs. blastocyst FG SM

Overall, 34 DEM were identified between the 3 SM comparison groups, in which 14 miRNAs belonged to 2-cell SG vs. 2-cell FG, 7 miRNAs were detected between 8-cell SG vs. 8- cell FG, and 12 miRNAs were differentially expressed between the blastocyst SG and blastocyst

FG groups. Of the 14 DEM detected between 2-cell SG and 2-cell FG, 12 miRNAs and 2 miRNAs were upregulated and downregulated (Table 4), respectively, in 2-cell SG SM in comparison to 2-cell FG SM. For 8-cell SG and 8-cell FG, 6 miRNAs and 1 miRNA were upregulated and downregulated (Table 5) respectively, in 8-cell SG SM in comparison to 8-cell

FG SM. Of the 12 DEM detected between blastocyst SG and blastocyst FG SM, 9 miRNAs and

4 miRNAs were upregulated and downregulated (Table 6) in blastocyst SG SM in comparison to blastocyst FG SM, respectively. It appears that SG embryos are releasing, rather than up taking, miRNAs in their environment. Differences in miRNA expression in the SM seem to be highest at the early and late stages of pre-implantation development between SG and FG embryos.

Interestingly, the majority of miRNAs detected in SM were expressed in a stage-specific manner, with only 3 miRNAs being co-detected in more than one SM condition. Bta-miR-1343-

5p was upregulated in the SM of both 2-cell and 8-cell SG, in comparison to their 2-cell and 8- cell FG counterparts. Bta-miR-450b and bta-miR-760-5p was detected in both 8-cell and blastocyst SM. However, the expression of the two miRNAs differed between the two conditions. Both miRNAs were upregulated in the SM of 8-cell SG embryos, while the two miRNAs were downregulated in SM cultured with SG blastocyst. It should be noted that no miRNAs were consistently differentially expressed across all three SM groups.

miRNAs Fold-Change P-Value bta-miR-455-3p -2.83 1.19E-06 bta-miR-628 -2.07 0.0104 bta-miR-2359 2.02 0.0113 bta-miR-2412 2.03 0.0025 bta-miR-2452 2.68 0.0006 bta-miR-2325a 3.12 0.003 bta-miR-1343-5p 3.16 0.0184 bta-miR-2421 4.04 0.0002 bta-miR-2434 6.56 0.0005 bta-miR-2393 13.36 0.0004 bta-miR-2444 17.58 0.0005 bta-miR-2361 41.56 0.001 bta-miR-3613 47.62 0.0002 bta-miR-2325c 58.04 0.0003

Table 4. DEM between 2-Cell SG SM vs. 2-Cell FG SM. The majority of miRNAs were upregulated in 2-cell SG SM in comparison to 2-Cell FG SM.

miRNAs Fold-Change P-Value bta-miR-3613b -6.1 9.48E-06 bta-miR-1343-5p 2.14 0.0149 bta-miR-450b 3.04 0.0002 bta-miR-2487 3.23 6.61E-06 bta-miR-2885 4.09 1.48E-07 bta-miR-1281 4.27 0.0033 bta-miR-760-5p 4.55 0.0001

Table 5. DEM between 8-Cell SG SM vs. 8-Cell FG SM. The majority of miRNAs were upregulated in 8-cell SG SM in comparison to 8-Cell FG SM.

miRNAs Fold-Change P-Value bta-miR-450b -2.71 0.0002 bta-miR-760-5p -2.48 0.0076 bta-miR-2296 -2.34 0.0004 bta-miR-6535 -2.11 0.007 bta-miR-2402 2.18 1.66E-05 bta-miR-23a 2.34 5.21E-07 bta-miR-23b-3p 2.45 3.90E-06 bta-miR-17-5p 2.47 0.0102 bta-miR-2898 2.51 0.0046 bta-miR-615 2.66 0.0035 bta-miR-320a 2.79 1.45E-08 bta-miR-24-3p 2.83 8.65E-07

Table 6. DEM between blastocyst SG SM vs. blastocyst FG SM. The majority of miRNAs were upregulated in SG SM in comparison to FG SM.

Predictions of miRNA-mRNA targets for differentially expressed miRNAs detected between 2- cell SG vs. 2-cell FG, 8-cell SG vs. 8-cell FG, and blastocyst SG vs. blastocyst FG SM

With regards to 2-cell SG vs. 2-cell FG miRNAs, bta-miR-2361 did not have any predicted targets that met the cumulated weighted score cutoff, thus were excluded from the analysis. From the remaining 13 miRNAs differentially expressed, a total of 635 mRNAs were predicted (supplementary table 6). 137 mRNAs were significantly enriched across 35 biological pathways (Table 7). Moving forward to 8-cell SG vs. 8-cell FG miRNAs, bta-miR-2487 was not found on the TargetScan database, thus was excluded from the analysis. From the remaining 6

DEM, a total of 579 mRNAs were predicted (supplementary table 7). 141 mRNAs were significantly enriched across 36 different biological processes in DAVID (Table 8). Lastly, a total of 837 mRNA targets were predicted for the 12 miRNAs differentially expressed between blastocyst SG vs. blastocyst FG condition (supplementary table 8). When inputted into DAVID,

239 mRNAs were enriched across 76 different biological processes (Table 9). Due to the significant number of biological processes enriched in each comparison group, only the top 5

pathways with the most genes enriched were featured on the tables. Overall, the majority of the predicted targets of DEM across the 3 conditions clustered around biological processes controlling transcription and proliferation.

GO Term Genes P-Value Positive regulation of transcription from RNA polymerase II promoter 33 3.05E-04 Negative regulation of transcription from RNA polymerase II promoter 26 4.65E-04 Negative regulation of transcription, DNA-templated 14 0.03335617 Negative regulation of cell proliferation 13 0.02255407 Spermatogenesis 12 0.01965493

Table 7. Top 5 enriched biological pathways of predicted genes regulated by miRNAs differentially expressed in 2-cell SG SM vs. 2-Cell FG SM. GO Term Genes P-Value Positive regulation of transcription from RNA polymerase II promoter 33 1.86E-04 Intracellular signal transduction 14 0.045985 Negative regulation of cell proliferation 13 0.01862582 Protein transport 11 0.03235415 Positive regulation of ERK1 and ERK2 cascade 10 0.01051371

Table 8. Top 5 enriched biological pathways of predicted genes regulated by miRNAs differentially expressed in 8-cell SG SM vs 8-Cell FG SM. GO Term Genes P-Value Positive regulation of transcription from RNA polymerase II promoter 36 0.0078327 Regulation of transcription from RNA polymerase II promoter 26 1.16E-05 Positive regulation of cell proliferation 20 0.0062254 Small GTPase mediated signal transduction 19 8.66E-04 Positive regulation of transcription, DNA-templated 18 0.0236799

Table 9. Top 5 enriched biological pathways of predicted genes regulated by miRNAs differentially expressed in blastocyst SG SM vs blastocyst FG SM.

Cross-referencing the enriched genes from each comparison group with their respective miRNA-mRNA gene-list allowed for the identification of miRNAs whose gene targets were enriched in DAVID. Out of the 14 miRNAs differentially expressed between 2-cell SG vs. 2-cell

FG SM, bta-miR-1343-5p and bta-miR-2443 gene targets were highly represented in DAVID. Of the 7 miRNAs detected between 8-cell SG vs. 8-cell FG SM, bta-miR-1343-5p and bta-miR-

2885 gene targets had the highest representation in GSEA. Among the 12 miRNAs detected

between blastocyst SG vs. blastocyst FG SM, the gene targets of bta-miR-6535 were overly represented in DAVID.

Discussion

To the best of our knowledge, this study was the first to globally profile miRNAs in the

SM conditioned with embryos growing at different developmental rates. Our results indicate that distinct miRNA population can be identified between SG and FG embryos. More importantly, these unique miRNA signatures can be detected at the early, mid, and late stages of preimplantation embryo development. Our results suggest that SG embryos, at all conditions examined (2-cell SM, 8-cell SM, and blastocyst SM), preferentially release miRNAs into the extracellular environment. Although no other study has reported this, metabolomics studies have detected distinct metabolites in the SM conditioned with embryos differing in developmental rate and viability. These studies suggest that lower quality embryos are metabolically more active than their higher quality counterparts. Since metabolism is influenced by the genes expressed within the cell, it can be postulated that increases in embryonic metabolism is preceded by higher genetic activity. Perhaps, miRNAs are used within the embryo to initiate and modulate gene expression influencing metabolic turnover. Evidence do suggest that extracellular miRNA population serve as a good indicator of intracellular miRNA expression. Thus, our result indicates that increases in metabolic activity in non-viable and SG embryos may also be driven by increases in miRNA expression, which are detectable in SM.

Across the 3 conditions examined, only the miRNAs detected between blastocyst SG vs. blastocyst FG SM had some previous annotation in literature. Specifically, miR-320a and miR-

24-3p, which were detected to be upregulated in blastocyst SG SM, have also been cited in previous SM studies. According to Kropp and Khatib, miR-24-3p was one of the five miRNAs

that they detected to be upregulated in SM conditioned with degenerate blastocyst (Kropp &

Khatib, 2015). In a subsequent supplementation study, miR-24 was added to the plain media of morula stage embryos. Supplementation resulted in a 44-fold increase in expression of miR-24 in blastocyst cultured with miR-24 and 27.3% decrease in blastocyst rates (Kropp & Khatib, 2015).

Kropp and Khatib postulate that miR-24 influenced the expression of CDKN1b, which is a cell cycle regulator. Despite the differences in the classification of non-viable embryos, whereby we used developmental rate and Kropp and Khatib examined arrested/degenerate embryos, both studies indicate that miR-24 may serve as a biomarker of embryo viability at the blastocyst stage of development.

Another miRNA that was identified in this study and previously annotated in literature was miR-320a. A recent study by Berkhout and colleagues suggest that miR-320a is a pro- implantation marker secreted by embryos. Specifically, the researcher profiled the miRNome of

SM conditioned with embryos either scoring low or high in morphological scores (Berkhout et al., 2020). It was determined that miR-320a was secreted by higher quality embryos.

Subsequently, miR-320a was supplemented in the culture media of human embryonic stem cells.

Berkhout and colleagues saw that miR-320a was able to stimulate the migration of decidualized human embryonic stem cells, with downstream transcriptome analysis revealing that miR-320a modulate genes regulating cell adhesion and cytoskeleton organization (Berkhout et al., 2020).

Interestingly, our study found miR-320a to be upregulated in SM conditioned with SG embryos.

Capalbo and colleagues have postulated that embryos release miRNAs into the extracellular environment as a means of paracrine communication with endometrial tissue. Thus, the findings in our study suggest the miR-320a may serve to inhibit implantation as it was found to be released by SG embryos. Although consensus about the role of miR-320a is mixed, it should be

noted that our study and the one conducted by Berkhout and colleagues were done in different species and culture conditions were not identical. Thus, inter-species differences and environmental conditions may have influenced the findings of both studies. Perhaps, miR-320a may have both inhibitory and stimulatory effects on implantation as miRNAs have various targets within the genome.

Aside from miR-320a and miR-24, miR-615 and miR-17 have also been previously annotated in literature, albiet in cancer-related studies. Specifically, miR-615 have been characterized to play a role in angiogenic events influencing tumorigenesis. Icli and colleagues demonstrated that miR-615 have anti-angiogenic effects, whereby expression of the miRNA significantly inhibited endothelial cell proliferation and migration (Icli et al., 2019). Similarly, miR-17 have been cited in literature to have anti-oncogenic effects. Hossain and colleagues reported that miR-17 transfection in breast cancer tissue resulted in the translational repression of the breast cancer associated gene A1B1. The subsequent downregulation of A1B1 decreased breast cancer proliferation. Thus, it seems that the miRNAs upregulated in SG embryos at the blastocyst stage have roles in cancer development. As previously discussed in objective one, cancer and embryogenesis rely on similar pathways for growth and development. Therefore, it is interesting to see that cancer related miRNAs are detectable in media conditioned with embryos growing at different rates. Perhaps, these anti-proliferative miRNAs in cancer, serve to inhibit growth and development in an embryo.

Aside from previously annotated miRNAs, GSEA analysis also revealed novel stage specific miRNA biomarkers. Bta-miR-1343 and bta-miR-2443 were miRNAs upregulated in SM cultured with 2-cell SG embryos. GSEA analysis indicated that the gene targets of the two miRNAs had roles regulating transcription and cell proliferation. Specifically, the majority of

gene targets had biological implications pertaining to positive and negative regulation of transcription from RNA polymerase II promoter. This is an interesting finding as previous research suggest that little to no transcription occurs at the 2-cell stage in bovine embryos. Prior to the 8-cell stage, the developing bovine embryo relies on parentally inherited transcripts

(mRNA and miRNA) and proteins for survival. Therefore, it is unclear as to why the gene targets of miR-1343 and miR-2443 would cluster around promoting transcriptional events.

However, research by Vassena and colleagues do suggest that embryos are capable of transcription prior to EGA. Through genomic wide transcript analysis of human oocytes and embryos, Vassena and colleagues detected a series of successive waves of embryonic transcriptional initiation events beginning as early as the 2-cell stage (Vassena et al., 2011).

Therefore, their findings suggest that transcriptional events in human embryos may begin as early as the 2-cell stage, and not at the 4-8 cell stage as previously thought. Although unexplored in bovine embryos, our results indicate that transcriptional events may be occurring earlier than

EGA. It can be postulated that SG embryos may initiate transcriptional events earlier as a response to its delayed development. Early activation of embryonic genome may serve as a repair mechanism to salvage an embryo during the pre-implantation period.

It should also be noted that bta-miR-1343 and bta-miR-2443 had gene targets relating to spermatogenesis. Previous research profiling the origins of embryonic miRNAs have suggested that the majority of miRNAs expressed prior to EGA is of maternal origin. However, researchers did discover that embryos can also inherit sperm-borne miRNAs. Although bta-miR-1343 and bta-miR-2443 have not been annotated in mammalian sperm, results from our study suggest that these miRNAs are of paternal origin capable of influencing transcriptional events in an embryo.

It should also be highlighted that bta-miR-1343, bta-miR-760-5p, and bta-miR-450b were co-detected in more than one SM conditions. Bta-miR-1343 was found to be upregulated in the

SM of SG embryos at the 2-cell and 8-cell stage. With regards to bta-miR-760-5p and bta-miR-

450bs, their expression was detected in media conditioned with the SG 8-cell and blastocyst embryos. Contrasting bta-miR-1343, both bta-miR-760-5p and bta-miR-450b were upregulated in 8-cell SG media, then were downregulated in blastocyst SG media. Although all 3 miRNAs have not been previously annotated in embryos or in SM, their consistent expression between 2- cell and 8-cell or 8-cell and blastocyst SM respectively, suggest that they may have functional roles in normal and aberrant embryo development.

Overall, this study was the first to detect miRNA expression differences in the SM between SG and FG embryos. Across all 3 developmental stages examined (2-cell, 8-cell, and blastocyst), embryos with delayed development expressed more miRNAs in the SM, than those developing at a faster rate. It is postulated that the difference in miRNA expression is associated with increases in metabolic activity observable in non-viable embryos. Moreover, our findings also highlight novel miRNA biomarker that correlated with slow and fast-growing embryos at 2- cell, 8-cell and blastocyst stage of development. Future research should focus on validating these miRNAs in the SM and within the embryo with subsequent gene expression studies to further elucidate the roles of these miRNAs in embryonic development.

SUMMARY AND FUTURE DIRECTIONS

The overall aim of this study was to globally profile miRNAs in the SM throughout the preimplantation period to elucidate miRNA biomarkers related to normal and aberrant embryo development. The goal of objective one was to determine whether miRNA can be detected in media prior to the blastocyst stage of development. Therefore, miRNA SM profiling was conducted on samples conditioned with 2-cell, 8-cell, and blastocyst embryos. Results from objective 1 show that with the use of a heterologous microarray, miRNAs can be detected throughout the pre-implantation period. Specifically, 6 miRNAs were exclusively detected in 2- cell SM and 56 miRNAs were unique to blastocyst SM. No miRNAs were exclusive to 8-cell

SM. Overlapping the results from each SM conditions also allowed for the identification of miRNAs shared between 8-cell and blastocyst SM, and miRNAs commonly expressed in all 3 conditions. GSEA analysis of potential gene targets of DEM suggest that the miRNAs have roles pertaining to cellular process, biological regulation, and metabolism. Overall, objective 1 was able to illustrate that miRNAs in SM can be detected throughout the pre-implantation period. It seems that the dynamic changes in intracellular miRNA population may be reflected in the extracellular environment.

The goal of objective two was to determine whether SM could be used to detect distinct miRNA signatures between embryos growing at different developmental rates. Therefore, comparative miRNA profiling was conducted on media samples conditioned with 2-cell SG vs.

2-cell FG embryos, 8-cell SG vs. 8-cell-FG embryos, and blastocyst SG vs. blastocyst FG embryos. Results from objective 2 show that with the use of a heterologous microarray, distinct miRNA signatures can be detected between embryos growing at different rates. More importantly, these distinct miRNA signatures can be detected at 2-cell, 8-cell, and blastocyst

stage of development. It seems that SG embryos release more miRNAs into the SM then their

FG counterparts. GSEA analysis revealed that the majority of miRNAs differentially expressed between the SG and FG embryos have gene target influencing cell proliferation and transcription. Bta-miR-1343 have been identified as a potential biomarker for both 2-cell and 8- cell SG embryos, while bta-miR-2443 and bta-miR-2885 may be used as markers for 2-cell and

8-cell embryos, respectively. With regards to SG blastocyst embryos, bta-miR-6535 had overrepresented gene targets in GSEA, thus may be used as a potential biomarker for aberrant late embryo development. Another finding of the study was the discovery of miRNAs in 2-cell

SG SM that may have roles in regulating transcription. Therefore, our findings suggest that SG embryos may initiate a wave of early transcriptional activity in response to delayed development.

Overall, this study was the first to distinguish distinct miRNA populations in SM between SG and FG embryos throughout the preimplantation period.

Although the results from both objective 1 and objective 2 are promising signs for the use of miRNAs as biomarkers of embryo development, it must be noted that these results must be further validated with qPCR. One of the disadvantages related to the use of array-based technologies for high-throughput genomic sequencing is that results are susceptible to a modest false-detection rate. Cross-hybridization of probes may induce high background levels resulting in the false-detection of probes. Therefore, miRNAs detected in both objective 1 and objective 2 should be further validated with qPCR within the SM, as well as within the embryo and/or blastocyst.

Once validation is complete and false candidates have been removed, the remaining validated miRNAs and their gene targets should be analyzed. One challenge in validating miRNA-mRNA relationship lies in the promiscuous binding that miRNAs exhibits with its

targets. Although in-silico software may predict a miRNA-mRNA pairing, it is difficult to discern whether the presence of the specific miRNA in question resulted in the downstream repression of an mRNA target. Perhaps, other unaccounted miRNAs may be influencing the gene expression of mRNA targets and/or the miRNA being studies is non-specifically binding to other mRNA targets. One approach to ensure that only the miRNA-mRNA relationship in question is being influence is by selecting target genes whose cumulative context scores and seed regions are <-1 and completely complimentary.

From these validation studies, miRNA mimic studies can be conducted to determine the widespread effects of miRNA supplementation on embryonic development. Potentially, dual luciferase assays can be conducted to explicitly highlight levels of mRNA down-regulation upon administration of miRNAs. Overall, this study highlighted key miRNA targets that may have a role in normal and aberrant preimplantation embryo development. With future validations studies and pathway analysis, miRNAs in the SM of in-vitro culture systems may be used as an adjunct assessment method for embryo quality.

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APPENDIX

S. Table 1. DEM in blastocyst SM

miRNAs Fold- miRNAs Fold- miRNAs Fold- Change Change Change

bta-miR-371 77.52 bta-miR-17-5p 5.55 bta-miR-346 2.93 bta-miR-320a 43.52 bta-miR-361 5.38 bta-miR-6535 2.8 bta-miR-3432 29.92 bta-miR-2407 5.14 bta-miR-16a 2.59

bta-let-7b 24.94 bta-miR-125a 5.04 bta-miR-30a-5p 2.58 bta-miR-24-3p 19.02 bta-miR-2455 4.99 bta-miR-744 2.4 bta-miR-23b-3p 15.97 bta-miR-423-5p 4.97 bta-miR-151-5p 2.39 bta-miR-7865 14.99 bta-let-7a-5p 4.74 bta-miR-182 2.37 bta-miR-342 14.38 bta-miR-30d 4.44 bta-miR-664 2.2

bta-miR-2402 12.83 bta-miR-2442 4.4 bta-miR-2324 2.2 bta-miR-320b 10.27 bta-miR-6529 4.3 bta-miR-2348 2.13 bta-let-7c 10.27 bta-miR-2904 4.07 bta-miR-155 2.13 bta-miR-23a 10.08 bta-miR-26a 3.95 bta-miR-2288 2.04 bta-miR-2295 8.48 bta-miR-193a-5p 3.93 bta-miR-669 2.01

bta-let-7d 8.36 bta-miR-2392 3.81 bta-miR-2457 -2.24 bta-miR-222 8.26 bta-miR-2426 3.7 bta-miR-2287 -2.74 bta-miR-191 7.54 bta-miR-221 3.39 bta-miR-3613b -3.42

bta-miR-2436-5p 6.92 bta-miR-2413 3.17 bta-miR-2412 6.5 bta-miR-2428 3.16 bta-miR-125b 5.78 bta-miR-2309 3.12

bta-miR-378 5.59 bta-miR-20a 3

S. Table 2 Predicted mRNA targets of DEM in 2-cell SM

miRNA Predicted Cumulative miRNAs Predicted Cumulative mRNAs Context Score mRNAs Context Score

bta-miR-2421 XKR4 -2.17 bta-miR-122 GYS1 -0.96

bta-miR-2421 NFIA -1.79 bta-miR-760-5p CD300LB -1.29

bta-miR-2421 ONECUT2 -1.5 bta-miR-760-5p GORASP1 -1.27

bta-miR-2421 TCF4 -1.21 bta-miR-760-5p AC016722.1 -0.99

bta-miR-2421 ELAVL4 -1.14 bta-miR-760-5p CT62 -0.98

bta-miR-2421 NFIB -1.09 bta-miR-760-5p GLIPR2 -0.93

bta-miR-2421 THRB -1 bta-miR-760-5p SLC4A11 -0.9

bta-miR-2421 GABRB3 -1 bta-miR-760-5p RHOU -0.87

bta-miR-2421 POU6F2 -0.99 bta-miR-760-5p OR5AU1 -0.85

bta-miR-2421 TNRC6C -0.92 bta-miR-760-5p PNKD -0.84

bta-miR-2421 IGIP -0.91 bta-miR-760-5p OR1L3 -0.84

bta-miR-2421 TNRC6B -0.87 bta-miR-760-5p DOCK1 -0.82

bta-miR-2421 GLIPR1L1 -0.82 bta-miR-760-5p PRPH2 -0.81

bta-miR-2421 CYLC2 -0.81 bta-miR-760-5p DMKN -0.78

bta-miR-2421 RUNX1T1 -0.75 bta-miR-760-5p DNAJC15 -0.77

bta-miR-2297 SRCAP -1 bta-miR-760-5p LRP11 -0.76

bta-miR-2297 G3BP1 -1 bta-miR-760-5p POSTN -0.75

bta-miR-2297 C17orf58 -0.93 bta-miR-760-5p CYHR1 -0.75

bta-miR-2297 DCAF4L2 -0.84

bta-miR-2297 JTB -0.82

bta-miR-2297 TSC22D3 -0.8

bta-miR-2297 C15orf60 -0.77

bta-miR-296-5p FAU -0.93

bta-miR-296-5p MS4A13 -0.84

bta-miR-296-5p APOE -0.76

bta-miR-296-5p BET1L -0.76

S. Table 3. Predicted mRNA targets of DEM in blastocyst SM

miRNA Predicted Cumulative miRNAs Predicted mRNAs Cumulative mRNAs Context Context Score Score

bta-miR-371 BOD1L2 -1.17 bta-miR-2295 SCRT1 -0.91

bta-let-7b ST8SIA1 -0.8 bta-miR-2295 TMEM109 -0.88

bta-miR-24-3p STRADB -0.85 bta-miR-2295 CERK -0.87

bta-miR-24-3p TCF7 -0.84 bta-miR-2295 OXLD1 -0.86

bta-miR-24-3p C12orf43 -0.84 bta-miR-2295 CAMTA2 -0.85

bta-miR-24-3p ENTPD6 -0.84 bta-miR-2295 PTPN7 -0.85

bta-miR-24-3p LSM10 -0.79 bta-miR-2295 XKR7 -0.84

bta-miR-24-3p SNN -0.78 bta-miR-2295 H1FX -0.84

bta-miR-24-3p GBA2 -0.76 bta-miR-2295 MYPOP -0.84

bta-miR-24-3p SLCO2B1 -0.75 bta-miR-2295 ZNF385A -0.83

bta-miR-23b-3p SS18L2 -1.09 bta-miR-2295 MAPK8IP2 -0.83

bta-miR-23b-3p ELF5 -0.81 bta-miR-2295 SLC27A4 -0.81

bta-miR-7865 PRRT2 -2.49 bta-miR-2295 GPR68 -0.8

bta-miR-7865 SYNGR1 -1.32 bta-miR-2295 CDK2AP2 -0.8

bta-miR-7865 PIANP -1.17 bta-miR-2295 ACKR2 -0.8

bta-miR-7865 NFIC -1.16 bta-miR-2295 SYNGR4 -0.79

bta-miR-7865 C12orf36 -1.15 bta-miR-2295 LTB -0.78 CSNK2B-LY6G5B- bta-miR-7865 TSPAN18 -1.12 bta-miR-2295 1181 -0.78

bta-miR-7865 SHISA6 -1.08 bta-miR-2295 POLR2J2 -0.77

bta-miR-7865 RNF165 -1.08 bta-miR-2295 BEST4 -0.77 bta-miR-7865 BOD1L2 -1.07 bta-miR-2295 FXYD5 -0.76 bta-miR-7865 CREB3L2 -1.04 bta-miR-2295 KCNK3 -0.76 bta-miR-7865 C1orf68 -1.03 bta-miR-2295 PPAPDC1A -0.76 bta-miR-7865 AIF1L -1 bta-miR-2295 AQP7 -0.76 bta-miR-7865 GORASP1 -0.99 bta-miR-2295 SLC38A10 -0.75 bta-miR-7865 SYNGR4 -0.99 bta-miR-2295 KIAA0930 -0.75 bta-miR-7865 PKLR -0.96 bta-miR-2295 EOMES -0.75 bta-miR-7865 UPK2 -0.94 bta-miR-2295 APOE -0.75 bta-miR-7865 MARVELD1 -0.92 bta-miR-2295 LAMTOR4 -0.75 bta-miR-7865 OLIG2 -0.9 bta-let-7d HMGA2 -2.74 bta-miR-7865 KRT80 -0.89 bta-let-7d FIGN -1.53 bta-miR-7865 MS4A8 -0.86 bta-let-7d ARID3B -1.42 bta-miR-7865 DCX -0.85 bta-let-7d LIN28B -1.41 bta-miR-7865 ADAM19 -0.85 bta-let-7d TRIM71 -1.27 bta-miR-7865 ZNF226 -0.85 bta-let-7d POLR2J2 -1.03 bta-miR-7865 DCTN3 -0.84 bta-let-7d LIN28A -0.92 bta-miR-7865 MYADM -0.84 bta-let-7d USP44 -0.91 bta-miR-7865 ATP6V0E2 -0.84 bta-let-7d FZD3 -0.89 bta-miR-7865 THRSP -0.84 bta-let-7d IGDCC3 -0.87 bta-miR-7865 HNRNPH2 -0.84 bta-let-7d AC140061.12 -0.87 bta-miR-7865 CYP8B1 -0.83 bta-let-7d IGF2BP1 -0.86 bta-miR-7865 GUCA1A -0.82 bta-let-7d YOD1 -0.84 bta-miR-7865 THRA -0.82 bta-let-7d VSTM5 -0.84 bta-miR-7865 LAMTOR4 -0.82 bta-let-7d ZBTB8B -0.84 bta-miR-7865 PRR24 -0.81 bta-let-7d PPP1R15B -0.8 bta-miR-7865 AKAP5 -0.81 bta-let-7d FAM222B -0.8 bta-miR-7865 MT-ND4L -0.81 bta-let-7d CDK8 -0.8 bta-miR-7865 FOSB -0.81 bta-let-7d NGF -0.8 bta-miR-7865 SST -0.81 bta-let-7d CCL7 -0.78 bta-miR-7865 HOXC8 -0.8 bta-let-7d HAND1 -0.75 bta-miR-7865 SLC7A8 -0.8 bta-miR-222 PVRL1 -1.63 bta-miR-7865 MAFG -0.8 bta-miR-222 GABRA1 -1.11 bta-miR-7865 PPME1 -0.8 bta-miR-222 CDKN1B -1.04 bta-miR-7865 KCNJ10 -0.8 bta-miR-222 PGAP1 -0.99 bta-miR-7865 MZB1 -0.8 bta-miR-2436-5p MYL6B -1.18 bta-miR-7865 SPRYD3 -0.79 bta-miR-2436-5p DGCR2 -1.15 bta-miR-7865 URM1 -0.78 bta-miR-2436-5p ZNF282 -1.08 bta-miR-7865 GIPC1 -0.78 bta-miR-2436-5p MZF1 -1.07 bta-miR-7865 RTBDN -0.78 bta-miR-2436-5p REPIN1 -0.93

bta-miR-7865 RASL10B -0.78 bta-miR-2436-5p CYP2F1 -0.86 bta-miR-7865 WFDC2 -0.77 bta-miR-2436-5p CNPY3 -0.84 bta-miR-7865 LAMTOR1 -0.77 bta-miR-2436-5p KAAG1 -0.76 bta-miR-7865 SPR -0.77 bta-miR-2436-5p CCL4L2 -0.76 bta-miR-7865 WDTC1 -0.76 bta-miR-2412 PML -1.17 bta-miR-7865 HNRNPUL1 -0.76 bta-miR-2412 COX6B2 -1.09 bta-miR-7865 AK4 -0.76 bta-miR-2412 TNFSF13 -1.04 bta-miR-7865 NHLH1 -0.76 bta-miR-2412 C15orf32 -1.03 bta-miR-7865 NRSN2 -0.75 bta-miR-2412 WNT4 -0.97 bta-miR-342 FAM53C -0.83 bta-miR-2412 CTD-3203P2.2 -0.91 bta-miR-2402 C1orf134 -0.98 bta-miR-2412 SNX32 -0.85 bta-miR-2402 ZMAT3 -0.87 bta-miR-2412 TNFSF12-TNFSF13 -0.85 bta-miR-2402 GDI2 -0.86 bta-miR-2412 SLC34A2 -0.81 bta-miR-2402 MRPL32 -0.84 bta-miR-2412 C1QTNF6 -0.78 bta-miR-2402 AC117834.1 -0.81 bta-miR-2412 KXD1 -0.75 bta-miR-2402 C19orf53 -0.77 bta-miR-125b RNPEPL1 -0.79 bta-miR-320b GIPC3 -4.41 bta-miR-125b DRAM2 -0.79 bta-miR-320b HIPK2 -1.62 bta-miR-125b ARID3B -0.75 bta-miR-320b NFIC -1.45 bta-miR-2457 IQCJ -1.18 bta-miR-320b SPN -1.2 bta-miR-2457 CRYGC -1.08 bta-miR-320b FAM43B -1.05 bta-miR-2457 SPIN3 -0.8 bta-miR-320b ONECUT3 -0.97 bta-miR-2287 PRAF2 -0.9 bta-miR-320b POLR2J2 -0.93 bta-miR-2287 WDR45 -0.82 bta-miR-320b HIVEP3 -0.92 bta-miR-2287 AF196779.12 -0.82 bta-miR-320b C3orf72 -0.91 bta-miR-2287 C15orf48 -0.75 bta-miR-320b TGM2 -0.89 bta-miR-2287 ZNF397 -0.75 bta-miR-320b RPL39 -0.87 bta-miR-3613b LGI2 -1 bta-miR-320b ST3GAL3 -0.87 bta-miR-3613b DEDD -1 bta-miR-320b AL049747.1 -0.83 bta-miR-3613b KDELR2 -1 bta-miR-320b BET1L -0.8 bta-miR-3613b MRRF -1 bta-miR-320b KCNK3 -0.79 bta-miR-3613b GOLT1B -1 bta-miR-320b POP5 -0.77 bta-miR-3613b ZFP37 -1 bta-miR-320b SDK2 -0.77 bta-miR-3613b USP38 -1 bta-miR-320b C10orf53 -0.77 bta-miR-3613b IKZF4 -1 bta-miR-320b NPTXR -0.76 bta-miR-3613b SNX30 -1 bta-miR-320b HSFX1 -0.76 bta-miR-3613b USP42 -1 bta-miR-320b CHTF8 -0.76 bta-miR-3613b CEP128 -1 bta-miR-320b TFRC -0.75 bta-miR-3613b CCDC132 -1 bta-miR-23a ZNF655 -1.15 bta-miR-3613b SIK2 -1 bta-miR-23a ACVR1C -0.92 bta-miR-3613b RHOQ -1

bta-miR-23a TFRC -0.77 bta-miR-3613b AGAP2 -1

bta-miR-23a PNRC2 -0.76 bta-miR-3613b RBM25 -1

bta-miR-23a PKP4 -0.75 bta-miR-3613b GAPVD1 -1

bta-miR-2295 AC006372.1 -1.65 bta-miR-3613b GAS7 -1

bta-miR-2295 NAPA -1.45 bta-miR-3613b CPD -1

bta-miR-2295 MTA3 -1.45 bta-miR-3613b HSD17B12 -1

bta-miR-2295 MVB12B -1.16 bta-miR-3613b TMBIM6 -1

bta-miR-2295 CCDC69 -1.15 bta-miR-3613b GLE1 -1 RP11- bta-miR-2295 429E11.3 -1.03 bta-miR-3613b MFN2 -1

bta-miR-2295 AL391421.1 -1.02 bta-miR-3613b CDK12 -1

bta-miR-2295 AIDA -1 bta-miR-3613b ARHGAP35 -1

bta-miR-2295 THRA -0.99 bta-miR-3613b FAM126A -1 bta-miR-3613b bta-miR-2295 SORCS2 -0.94 ZIC5 -1 bta-miR-3613b bta-miR-2295 RAB35 -0.92 KPNA6 -1

bta-miR-3613b

bta-miR-2295 FMNL1 -0.92 LONRF2 -1

S. Table 4. Predicted mRNA targets of DEM in 8-cell and blastocyst SM

miRNA Predicted Cumulative miRNAs Predicted Cumulative mRNAs Context mRNAs Context Score Score

bta-miR-2899 PLXNA1 -2.04 bta-miR-3141 DMTN -0.86

bta-miR-2899 CMIP -1.71 bta-miR-2899 CLCN2 -0.86

bta-miR-1584-5p CPLX2 -1.68 bta-miR-2899 TTYH3 -0.86

bta-miR-1584-5p LDHAL6B -1.56 bta-miR-1343-5p SPSB1 -0.86

bta-miR-2888 TNS1 -1.51 bta-miR-1343-5p TFCP2L1 -0.86

bta-miR-2899 PACSIN1 -1.51 bta-miR-1343-5p TFEB -0.86

bta-miR-1343-5p PRX -1.51 bta-miR-1343-5p FHL3 -0.86

bta-miR-2899 NAT8L -1.5 bta-miR-2887 TTYH3 -0.86

bta-miR-2899 ARC -1.49 bta-miR-3141 RAB8A -0.85

bta-miR-1343-5p KIAA0513 -1.48 bta-miR-2888 C2orf66 -0.85

bta-miR-2899 GAS8 -1.47 bta-miR-2899 AXIN1 -0.85

bta-miR-2899 BRSK2 -1.42 bta-miR-1343-5p SIPA1L3 -0.85

bta-miR-1584-5p GIPC3 -1.35 bta-miR-2899 PHKG2 -0.84

bta-miR-2899 FBXL16 -1.34 bta-miR-1343-5p LDLRAP1 -0.84

bta-miR-1584-5p RP11-131H24.4 -1.32 bta-miR-2888 CD3E -0.83

bta-miR-1343-5p KSR2 -1.29 bta-miR-2374 C9orf171 -0.83

bta-miR-2899 OR10S1 -1.27 bta-miR-2899 MXD4 -0.83

bta-miR-1343-5p RAB37 -1.27 bta-miR-2899 HCFC1 -0.83

bta-miR-3141 MS4A15 -1.24 bta-miR-1343-5p KCTD17 -0.83

bta-miR-1343-5p LY6G6C -1.24 bta-miR-1343-5p SIX5 -0.83 bta-miR-2899 ADAMTS13 -1.23 bta-miR-2328-3p IPO11 -0.83 bta-miR-2899 MAFK -1.22 bta-miR-1584-5p P2RX6 -0.82 bta-miR-1343-5p EHD2 -1.22 bta-miR-1584-5p SPINT1 -0.82 bta-miR-2899 PAM16 -1.2 bta-miR-2374 IQSEC3 -0.82 bta-miR-2899 SYNGR3 -1.18 bta-miR-2374 WDR48 -0.82 bta-miR-2374 G6PC3 -1.17 bta-miR-2899 PHOX2B -0.82 bta-miR-2888 FAM222B -1.15 bta-miR-2899 SYT8 -0.82 bta-miR-2328-3p C6orf163 -1.15 bta-miR-2899 ZNF385A -0.82 bta-miR-2899 ATP6V0C -1.14 bta-miR-1343-5p KB-1507C5.2 -0.82 bta-miR-2899 RCVRN -1.14 bta-miR-1343-5p MVB12B -0.82 bta-miR-1343-5p C17orf103 -1.13 bta-miR-1584-5p MAPT -0.81 bta-miR-2893 RP11-422N16.3 -1.11 bta-miR-2899 RNF166 -0.81 bta-miR-2899 RAX -1.11 bta-miR-2899 THY1 -0.81 bta-miR-1246 GSG1L -1.11 bta-miR-2328-3p ONECUT3 -0.81 bta-miR-2899 ZDHHC8 -1.1 bta-miR-3141 TTYH3 -0.8 bta-miR-2899 PAX2 -1.1 bta-miR-3141 SSR1 -0.8 bta-miR-1343-5p GAS8 -1.1 bta-miR-3141 RTL1 -0.8 bta-miR-2374 ZCCHC24 -1.09 bta-miR-2888 CPLX2 -0.8 bta-miR-1246 TMPRSS11A -1.07 bta-miR-2888 PFN1 -0.8 bta-miR-2899 UBALD1 -1.06 bta-miR-1343-5p ATG9A -0.8 bta-miR-3141 SCRT1 -1.05 bta-miR-1343-5p HMGA1 -0.8 bta-miR-2899 LENG8 -1.05 bta-miR-1246 ZNF23 -0.8 bta-miR-2888 AL359878.1 -1.03 bta-miR-2374 C6orf211 -0.79 bta-miR-2899 REPIN1 -1.03 bta-miR-2893 ZNF784 -0.79 bta-miR-1343-5p RGMA -1.03 bta-miR-2893 DBNDD2 -0.79 RP11- bta-miR-1584-5p LCE3E -1.02 bta-miR-2893 93B14.6 -0.79 bta-miR-2899 TBC1D14 -1.02 bta-miR-2899 SPI1 -0.79 bta-miR-2899 MTA1 -1.01 bta-miR-2899 PPIB -0.79 bta-miR-2899 FAM222B -1.01 bta-miR-2899 RECQL5 -0.79 bta-miR-2888 HSPB7 -1 bta-miR-2899 CNDP2 -0.79 bta-miR-2888 ERN1 -1 bta-miR-2899 C1orf35 -0.79 bta-miR-2888 STON2 -1 bta-miR-2899 C17orf103 -0.79 bta-miR-2888 SPTBN1 -1 bta-miR-2899 HEYL -0.79 bta-miR-2888 LMNB2 -1 bta-miR-1343-5p TBC1D13 -0.79 bta-miR-2899 MADD -1 bta-miR-1343-5p GRB7 -0.79 bta-miR-2899 TRABD -1 bta-miR-2328-3p hsa-mir-1199 -0.79 bta-miR-2899 SCRT1 -1 bta-miR-1584-5p MTX2 -0.78 bta-miR-1343-5p OSM -1 bta-miR-1584-5p PLEKHF1 -0.78 bta-miR-149-3p FBXW8 -1 bta-miR-1584-5p ACYP1 -0.78

CORO7- bta-miR-2899 PAM16 -0.98 bta-miR-2888 CTDSP1 -0.78 bta-miR-1584-5p ZNF740 -0.97 bta-miR-2899 PDLIM2 -0.78 bta-miR-2899 MKNK2 -0.97 bta-miR-2899 HID1 -0.78 bta-miR-2899 RAB8A -0.97 bta-miR-1343-5p KLK14 -0.78 bta-miR-2899 MGLL -0.97 bta-miR-1343-5p UBE2QL1 -0.78 bta-miR-2899 NFAM1 -0.95 bta-miR-2887 AC093802.1 -0.78 bta-miR-1343-5p LYPD1 -0.95 bta-miR-1584-5p LY6E -0.77 bta-miR-1343-5p PKD1 -0.95 bta-miR-1584-5p GEMIN4 -0.77 bta-miR-1343-5p GPA33 -0.95 bta-miR-1584-5p GPRIN1 -0.77 bta-miR-1343-5p MS4A15 -0.95 bta-miR-2888 CENPB -0.77 bta-miR-1584-5p LCE3D -0.94 bta-miR-2888 ZNF225 -0.77 bta-miR-2888 MUC5B -0.94 bta-miR-2374 S100A11 -0.77 bta-miR-2328-3p TMCC1 -0.94 bta-miR-2374 FAM107A -0.77 bta-miR-3141 PRKAB1 -0.93 bta-miR-1343-5p THTPA -0.77 bta-miR-1584-5p CD37 -0.93 bta-miR-1343-5p PIRT -0.77 bta-miR-1584-5p GNAT1 -0.93 bta-miR-1343-5p TNS4 -0.77 bta-miR-2899 TCP11 -0.93 bta-miR-1343-5p HEPACAM -0.77 bta-miR-2899 GRIN1 -0.93 bta-miR-1343-5p STIM1 -0.77 bta-miR-2887 RPAIN -0.93 bta-miR-2328-3p KCTD5 -0.77 bta-miR-1584-5p FAM222B -0.92 bta-miR-3141 C17orf107 -0.76 bta-miR-2888 PCSK1N -0.92 bta-miR-2888 INSL3 -0.76 bta-miR-2893 AC008948.1 -0.92 bta-miR-2888 IQSEC2 -0.76 bta-miR-2899 DLX1 -0.92 bta-miR-2899 IGLON5 -0.76 bta-miR-2899 ZCCHC24 -0.92 bta-miR-2899 ZC3H3 -0.76 bta-miR-1343-5p COTL1 -0.92 bta-miR-1343-5p RRP1 -0.76 bta-miR-2893 SLC7A8 -0.91 bta-miR-1343-5p PACRG -0.76 bta-miR-2899 FAM127A -0.91 bta-miR-1343-5p PSORS1C1 -0.76 bta-miR-1343-5p FKBP1A -0.91 bta-miR-1343-5p GAREML -0.76 bta-miR-1343-5p HOXB5 -0.91 bta-miR-2328-3p ZRSR1 -0.76 bta-miR-2887 BHLHA15 -0.91 bta-miR-1246 ORC6 -0.76 bta-miR-2887 SLC44A3 -0.91 bta-miR-1584-5p NFIX -0.75 bta-miR-3141 MAPK11 -0.9 bta-miR-2899 NRG2 -0.75 bta-miR-2888 SLC25A23 -0.9 bta-miR-2899 VWA1 -0.75 bta-miR-2893 BOD1L2 -0.9 bta-miR-2899 CCDC74A -0.75 bta-miR-2374 VAMP2 -0.89 bta-miR-2899 LTBP4 -0.75 bta-miR-2899 RP11-20I23.1 -0.89 bta-miR-2899 FOSB -0.75 bta-miR-3141 POU2F2 -0.88 bta-miR-1343-5p NUPR1 -0.75 bta-miR-2888 PAX5 -0.88 bta-miR-1343-5p SERPINE3 -0.75 bta-miR-2888 SYCE2 -0.88 bta-miR-1343-5p AQP5 -0.75 bta-miR-2899 GRM4 -0.88 bta-miR-1343-5p TSTA3 -0.75

bta-miR-2899 MS4A15 -0.88 bta-miR-1343-5p GLIS1 -0.75

bta-miR-2899 TAGLN -0.88 bta-miR-2887 TOR2A -0.75

bta-miR-2899 C20orf194 -0.88 bta-miR-2887 GORASP1 -0.75

bta-miR-1246 FUT9 -0.88 bta-miR-1246 AL355390.1 -0.75

bta-miR-3141 FAM19A5 -0.87

S. Table 5. Predicted mRNA targets of DEM common to 2-cell, 8-cell, blastocyst SM

miRNA Predicted mRNAs Cumulat miRNAs Predicted mRNAs Cumulat ive ive Context Context Score Score

bta-miR-1777b 44080 -0.81 bta-miR-2900 MAPKAPK2 -1.4

bta-miR-1777b 44083 -1.39 bta-miR-1777b MAPKAPK2 -0.96

bta-miR-1777a ABCF2 -0.86 bta-miR-2885 MARK4 -0.83

bta-miR-1777b AC007040.11 -0.75 bta-miR-1777b MARVELD1 -1.15

bta-miR-2900 AC026202.1 -1 bta-miR-1777a MASP2 -0.82

bta-miR-1777b AC026202.1 -0.96 bta-miR-2305 MDGA1 -2.37

bta-miR-2305 AC068987.1 -1.07 bta-miR-1777b MESDC1 -0.86

bta-miR-2305 AC093802.1 -0.89 bta-miR-1777b METTL7B -0.77

bta-miR-1777b ACOX3 -1.08 bta-miR-2900 MEX3B -0.81

bta-miR-1777a ACTN4 -0.95 bta-miR-2305 MEX3C -0.79

bta-miR-1777b ACTR3B -0.81 bta-miR-2305 MFGE8 -0.95

bta-miR-2900 ADAM11 -0.82 bta-miR-2900 MFGE8 -0.93

bta-miR-2305 ADAMTS10 -0.79 bta-miR-2305 MGAT5 -0.83

bta-miR-2900 ADAMTS13 -0.79 bta-miR-2900 MIDN -1.42

bta-miR-1777b ADD1 -0.75 bta-miR-1777b MIDN -0.78

bta-miR-2305 ADRM1 -0.83 bta-miR-2900 MIF -0.95

bta-miR-2305 AF196779.12 -1.14 bta-miR-2900 MKNK2 -1.86

bta-miR-2305 AGAP1 -1.22 bta-miR-1777b MKNK2 -0.85

bta-miR-1777a AGFG2 -1.02 bta-miR-2305 MKNK2 -0.79

bta-miR-2885 AGFG2 -0.95 bta-miR-1777a MLLT6 -1.17

bta-miR-2305 AGPAT3 -1.24 bta-miR-1777a MMP15 -0.75

bta-miR-2900 AHDC1 -1.25 bta-miR-2305 MMP2 -1.03

bta-miR-2305 AIF1L -0.85 bta-miR-2900 MS4A15 -1.56

bta-miR-2305 AK8 -0.78 bta-miR-1777b MS4A15 -1.23

bta-miR-1777b AKNA -0.81 bta-miR-1777a MSI1 -2.29

bta-miR-2900 AL117190.3 -0.85 bta-miR-2305 MSI1 -1.44

bta-miR-1777b AL117190.3 -0.83 bta-miR-1777b MSI1 -0.76

bta-miR-1777a AL117190.3 -0.75 bta-miR-1777a MTSS1L -1.34

bta-miR-2885 AL450307.1 -0.82 bta-miR-2305 MTSS1L -1.09

bta-miR-2885 AL590822.1 -0.75 bta-miR-2305 MUC5B -2.19 bta-miR-2305 ANAPC15 -0.78 bta-miR-1777b MUL1 -0.94 bta-miR-2900 ANK1 -1.03 bta-miR-2900 MUL1 -0.85 bta-miR-1777a ANK1 -0.96 bta-miR-2900 MVB12B -0.8 bta-miR-1777b ANK1 -0.96 bta-miR-2900 MXD4 -1.17 bta-miR-2305 ANKRD13B -0.86 bta-miR-2885 MZF1 -0.86 bta-miR-1777a ANKRD52 -1.44 bta-miR-1777b NAB2 -0.82 bta-miR-2305 ANKRD52 -0.81 bta-miR-1777a NACC1 -0.97 bta-miR-2900 AP000350.10 -0.85 bta-miR-1777b NANOS3 -0.75 bta-miR-2900 AP000350.4 -1.04 bta-miR-2900 NAPRT1 -0.77 bta-miR-2305 AP001816.1 -0.75 bta-miR-2900 NAT8L -1.96 bta-miR-2900 ARC -1.72 bta-miR-2305 NCF1 -0.92 bta-miR-1777b ARC -1.21 bta-miR-2305 NCKAP5L -0.78 bta-miR-1777a ARHGAP1 -0.83 bta-miR-1777a NFAM1 -0.83 bta-miR-2900 ARHGAP17 -1.01 bta-miR-2305 NFIC -1.67 bta-miR-1777a ARHGAP17 -0.99 bta-miR-2305 NFIX -0.75 bta-miR-1777b ARHGAP17 -0.85 bta-miR-2305 NGB -0.94 bta-miR-2900 ARHGAP23 -0.79 bta-miR-2305 NKAIN1 -1.13 bta-miR-2900 ARHGAP39 -0.93 bta-miR-2305 NKAIN4 -0.79 bta-miR-1777a ATN1 -1.33 bta-miR-2885 NKX2-5 -0.8 bta-miR-1777b ATN1 -1.23 bta-miR-2900 NODAL -0.85 bta-miR-2900 ATN1 -1.07 bta-miR-2305 NOP2 -0.88 bta-miR-1777a ATP1A3 -1.09 bta-miR-2900 NPTX2 -1.41 bta-miR-2900 ATP1A3 -0.78 bta-miR-2305 NPTXR -0.76 bta-miR-1777b ATP1A3 -0.76 bta-miR-2900 NRBP1 -0.83 bta-miR-1777a ATP1B2 -0.8 bta-miR-1777b NRGN -1.1 bta-miR-2900 ATP6V0C -1.46 bta-miR-1777a NRGN -1.06 bta-miR-1777b ATP6V0C -0.84 bta-miR-2900 NTNG2 -0.91 bta-miR-2900 AVPR2 -1.07 bta-miR-2305 NUMBL -0.92 bta-miR-1777b AVPR2 -0.97 bta-miR-1777b NUP210 -0.78 bta-miR-2900 B3GAT1 -1.21 bta-miR-2305 NXF1 -1.13 bta-miR-2900 BANP -0.91 bta-miR-2305 ONECUT3 -1.97 bta-miR-2305 BCL2L1 -2 bta-miR-1777a ONECUT3 -1.27 bta-miR-2305 BEAN1 -1.52 bta-miR-1777a OTP -1.05 bta-miR-2305 BFSP2 -0.76 bta-miR-1777b OTP -1.02 bta-miR-2305 BGN -0.8 bta-miR-2305 P2RX6 -0.96 bta-miR-1777a BMP1 -1.29 bta-miR-2900 PACSIN1 -1.05 bta-miR-1777b BOK -0.91 bta-miR-2305 PACSIN1 -0.92 bta-miR-2305 BPIFA2 -0.75 bta-miR-2885 PACSIN2 -0.75 bta-miR-1777a BRSK1 -0.84 bta-miR-2900 PAM16 -1.38

bta-miR-2900 BRSK2 -1.6 bta-miR-1777b PAX2 -2.16 bta-miR-2900 BTBD9 -0.94 bta-miR-2900 PAX2 -2.15 bta-miR-1777b C10orf55 -0.91 bta-miR-2305 PCDHGA1 -1.21 bta-miR-2305 C15orf52 -0.92 bta-miR-2305 PCDHGA10 -1.21 bta-miR-2305 C17orf103 -1.07 bta-miR-2305 PCDHGA11 -1.18 bta-miR-2900 C17orf107 -1.02 bta-miR-2305 PCDHGA12 -1.21 bta-miR-2305 C17orf50 -0.83 bta-miR-2305 PCDHGA2 -1.21 bta-miR-1777b C17orf50 -0.77 bta-miR-2305 PCDHGA3 -1.21 bta-miR-1777b C17orf74 -0.87 bta-miR-2305 PCDHGA4 -1.21 bta-miR-2305 C19orf43 -0.84 bta-miR-2305 PCDHGA5 -1.21 bta-miR-2900 C19orf73 -1.04 bta-miR-2305 PCDHGA6 -1.21 bta-miR-1777b C1QL1 -0.88 bta-miR-2305 PCDHGA7 -1.21 bta-miR-2305 C1QL4 -0.75 bta-miR-2305 PCDHGA8 -1.21 bta-miR-2305 C20orf112 -0.77 bta-miR-2305 PCDHGA9 -1.21 bta-miR-2305 C21orf67 -1.19 bta-miR-2305 PCDHGB1 -1.22 bta-miR-2305 C22orf23 -0.75 bta-miR-2305 PCDHGB2 -1.21 bta-miR-2305 C22orf26 -0.83 bta-miR-2305 PCDHGB3 -1.21 bta-miR-2885 C3orf20 -0.79 bta-miR-2305 PCDHGB4 -1.22 bta-miR-2900 C3orf27 -0.76 bta-miR-2305 PCDHGB6 -1.21 bta-miR-2900 C6orf223 -0.92 bta-miR-2305 PCDHGB7 -1.21 bta-miR-2305 C7orf41 -0.76 bta-miR-2305 PCDHGC3 -1.21 bta-miR-1777a C9orf62 -1.12 bta-miR-2305 PCDHGC4 -1.21 bta-miR-2885 CABP1 -0.83 bta-miR-2305 PCDHGC5 -1.65 bta-miR-2900 CABP7 -1.1 bta-miR-2305 PDE4A -1.14 bta-miR-1777a CACNG7 -1.4 bta-miR-1777a PEX14 -0.78 bta-miR-2900 CACNG7 -0.98 bta-miR-2900 PEX6 -0.75 bta-miR-450b CAMK2N1 -1.01 bta-miR-2305 PGAP3 -1.28 bta-miR-1777a CAPN15 -0.8 bta-miR-2305 PHOX2B -0.97 bta-miR-1777b CAPN15 -0.75 bta-miR-2900 PIAS4 -0.78 bta-miR-2305 CASKIN2 -0.79 bta-miR-2900 PIRT -0.98 bta-miR-2305 CBLN3 -0.81 bta-miR-1777a PITX1 -0.87 bta-miR-2900 CBX6 -2.03 bta-miR-2305 PKNOX2 -1.02 bta-miR-1777b CBX6 -1.18 bta-miR-2885 PLA2G1B -1.08 bta-miR-2900 CBX7 -1.5 bta-miR-2305 PLA2G2F -0.91 bta-miR-1777b CBX7 -0.95 bta-miR-1777b PLEKHO2 -0.78 bta-miR-2885 CCDC64 -0.8 bta-miR-2900 PLXNA1 -0.82 bta-miR-1777b CCDC74A -0.81 bta-miR-2900 PODXL2 -1.03 bta-miR-1777b CCL3L1 -0.75 bta-miR-1777b PODXL2 -0.82 bta-miR-2900 CD248 -0.79 bta-miR-2305 POLR1A -0.98 bta-miR-2900 CD300LB -0.85 bta-miR-2900 POLR2J2 -1.46

bta-miR-1777a CDIP1 -0.92 bta-miR-2900 POLR2L -0.81 bta-miR-2305 CDK5R1 -0.75 bta-miR-2900 POU2F2 -1.46 bta-miR-1777b CDK5R2 -0.91 bta-miR-2900 PPARD -1.02 bta-miR-2305 CDKN1A -1.28 bta-miR-1777a PPARD -0.93 bta-miR-2305 CDR2L -0.89 bta-miR-1777b PPARD -0.84 bta-miR-1777b CDYL2 -0.92 bta-miR-1777b PPFIA3 -0.9 bta-miR-2305 CEACAM19 -1.14 bta-miR-2900 PPP1R12C -1 bta-miR-1777a CECR1 -0.88 bta-miR-1777a PPP1R3B -0.77 bta-miR-2305 CELF5 -1.21 bta-miR-2305 PRAF2 -1.23 bta-miR-2900 CEP170B -1.71 bta-miR-2900 PRKAB1 -0.8 bta-miR-2900 CERS1 -1.6 bta-miR-2900 PRKACA -0.81 bta-miR-1777a CERS1 -1.31 bta-miR-2900 PRKCG -1.14 bta-miR-1777b CERS1 -0.94 bta-miR-1777b PRKCG -0.81 bta-miR-2885 CERS1 -0.83 bta-miR-2305 PRR3 -0.75 bta-miR-2305 CIC -0.84 bta-miR-2900 PRSS36 -0.84 bta-miR-1777a CITED4 -1.27 bta-miR-2900 PSAPL1 -0.77 bta-miR-1777b CKM -1.05 bta-miR-2305 PSMD8 -0.77 bta-miR-2900 CKM -0.89 bta-miR-2885 PTBP1 -0.75 bta-miR-2305 CLDN2 -0.92 bta-miR-2900 PTK2B -1.14 bta-miR-2305 CLIP3 -1.27 bta-miR-1777b PTK2B -0.98 bta-miR-1777b CLPP -0.82 bta-miR-2305 PTPN7 -1.02 bta-miR-1777b CNDP2 -0.8 bta-miR-2900 PTPRS -0.97 bta-miR-2900 CNFN -1 bta-miR-1777a R3HDM4 -0.81 bta-miR-1777a CNFN -0.98 bta-miR-1777b RAB11B -1.11 bta-miR-1777b CNFN -0.95 bta-miR-2900 RAB11B -0.87 bta-miR-2305 CNIH2 -1.01 bta-miR-2305 RAB3A -1.66 bta-miR-2900 CNOT3 -1.29 bta-miR-2900 RAB8A -1.7 bta-miR-2305 CNPY3 -0.84 bta-miR-2900 RAD51L3-RFFL -0.78 bta-miR-2900 CNTFR -0.82 bta-miR-2900 RADIL -1.41 bta-miR-2305 COMMD7 -0.86 bta-miR-2305 RAPGEFL1 -0.89 bta-miR-2900 COPS7A -0.8 bta-miR-2900 RASL10B -1.39 bta-miR-2305 COPS7A -0.76 bta-miR-2305 RASL10B -0.82 bta-miR-2305 COPZ1 -1.18 bta-miR-1777b RASL10B -0.77 bta-miR-2900 CORO7-PAM16 -1.13 bta-miR-2305 RAX -1.21 bta-miR-2900 COTL1 -0.78 bta-miR-2885 RAX -0.91 bta-miR-1777b COX6B2 -1.61 bta-miR-2305 RBFOX3 -1.08 bta-miR-2900 CRIP2 -1.77 bta-miR-2900 RCVRN -1.13 bta-miR-2305 CRMP1 -0.75 bta-miR-1777b REM2 -0.8 bta-miR-1777a CRTC1 -0.81 bta-miR-1777a RGMA -2.44 bta-miR-2305 CSDC2 -1.23 bta-miR-2885 RGMA -1.23

bta-miR-1777a CTIF -0.95 bta-miR-2305 RGMA -0.96 bta-miR-1777b CTIF -0.9 bta-miR-2305 RHO -0.75 bta-miR-2305 CTRL -0.81 bta-miR-2900 RHOD -0.78 bta-miR-1777a CTXN1 -0.97 bta-miR-2305 RHOG -0.87 bta-miR-2305 CX3CL1 -1.33 bta-miR-2305 RNF144A -0.76 bta-miR-1777b CYB5R3 -1.16 bta-miR-2305 RNF185 -0.76 bta-miR-2305 CYP2R1 -0.88 bta-miR-2900 RNF222 -0.79 bta-miR-1777a CYP2S1 -1.12 bta-miR-2305 RNF44 -1.56 bta-miR-2305 CYP3A4 -0.82 bta-miR-2305 RNPS1 -1.43 bta-miR-2305 CYP46A1 -1.49 bta-miR-2305 ROGDI -0.91 bta-miR-1777b CYP4F22 -0.83 bta-miR-1777b RP11-195F19.5 -1.04 bta-miR-2305 DAB2IP -1.01 bta-miR-2900 RP11-195F19.5 -0.75 bta-miR-2900 DAB2IP -0.81 bta-miR-2900 RP11-20I23.1 -1.12 bta-miR-2305 DAGLA -1.21 bta-miR-2305 RP11-247C2.2 -0.8 bta-miR-2900 DDA1 -1.07 bta-miR-2900 RP11-527L4.2 -1.41 bta-miR-1777a DDR1 -0.76 bta-miR-2305 RP11-94B19.4 -1.01 bta-miR-1777b DDX39B -0.85 bta-miR-2305 RPH3A -1.13 bta-miR-2305 DERL3 -1.02 bta-miR-1777a RPS6KA2 -1.37 bta-miR-1777b DET1 -0.76 bta-miR-2900 RPS6KA2 -0.84 bta-miR-1777a DKFZP761J1410 -0.88 bta-miR-1777b RPS6KA2 -0.8 bta-miR-2885 DKFZP779J2370 -0.78 bta-miR-2305 RPUSD1 -1.14 bta-miR-1777b DLGAP3 -0.79 bta-miR-2305 RUNX3 -0.94 bta-miR-2900 DLGAP3 -0.76 bta-miR-2305 SAMD4B -0.93 bta-miR-1777a DNAH17-AS1 -0.95 bta-miR-1777b SBK1 -1.1 bta-miR-1777b DNAJC4 -0.96 bta-miR-2900 SBK1 -0.82 bta-miR-2305 DPM2 -0.8 bta-miR-2900 SCN1B -0.8 bta-miR-1777a DUSP16 -0.87 bta-miR-2900 SCRT1 -2.53 bta-miR-1777b DUSP22 -0.78 bta-miR-1777b SCRT1 -1.52 bta-miR-2900 ECE1 -0.77 bta-miR-1777a SDK2 -0.9 bta-miR-1777b EEF1A2 -0.86 bta-miR-1777b SEC61A1 -0.79 bta-miR-1777a EGLN3 -0.83 bta-miR-2900 SEPT9 -1.02 bta-miR-1777b EGLN3 -0.8 bta-miR-2305 SETD1B -2.24 bta-miR-2900 EHD2 -1.29 bta-miR-2900 SGSM1 -1.02 bta-miR-1777b EHD2 -1.02 bta-miR-1777a SHOX -1.01 bta-miR-2305 EIF2AK1 -0.76 bta-miR-2305 SIPA1L3 -1.22 bta-miR-2305 ELAVL3 -0.97 bta-miR-1777b SIPA1L3 -0.8 bta-miR-2305 ELK1 -0.88 bta-miR-2305 SIT1 -0.8 bta-miR-2900 ELN -1.73 bta-miR-1777b SIVA1 -0.96 bta-miR-2305 ENG -0.78 bta-miR-1777b SLC12A9 -0.77 bta-miR-2900 EPHA8 -0.92 bta-miR-2305 SLC17A7 -0.88

bta-miR-1777a EPHB2 -0.79 bta-miR-2305 SLC25A23 -1.17 bta-miR-1777a EPS8L2 -1.43 bta-miR-2885 SLC25A28 -1.02 bta-miR-2305 ERF -0.78 bta-miR-2900 SLC29A4 -0.85 bta-miR-1777b EXOSC5 -0.94 bta-miR-1777a SLC41A1 -0.8 bta-miR-2305 FAM131B -0.77 bta-miR-2305 SLC45A3 -0.96 bta-miR-1777a FAM132A -0.98 bta-miR-2305 SLC48A1 -0.99 bta-miR-2305 FAM155B -0.92 bta-miR-2305 SLC6A1 -1.52 bta-miR-1777b FAM19A5 -1.07 bta-miR-2305 SLC6A17 -0.86 bta-miR-2900 FAM19A5 -0.97 bta-miR-1777b SLC6A8 -1 bta-miR-2885 FAM219A -0.75 bta-miR-1777b SLC7A1 -0.83 bta-miR-2305 FAM222B -2.06 bta-miR-2305 SMARCC2 -0.8 bta-miR-2305 FAM43B -1.12 bta-miR-2305 SMR3B -0.79 bta-miR-1777b FAM53B -0.75 bta-miR-2900 SNCB -0.75 bta-miR-2305 FAM57B -0.92 bta-miR-1777b SOD3 -1 bta-miR-2885 FAM73B -0.86 bta-miR-1777a SOD3 -0.96 bta-miR-2305 FAM83F -0.82 bta-miR-2305 SORCS2 -2 bta-miR-2900 FBXL16 -1.95 bta-miR-1777a SOST -1.2 bta-miR-2900 FBXL18 -0.86 bta-miR-1777b SOST -0.9 bta-miR-2305 FBXO46 -1.65 bta-miR-1777a SOX12 -1.11 bta-miR-2305 FEV -0.9 bta-miR-2305 SOX15 -0.81 bta-miR-2305 FGF4 -0.76 bta-miR-1777b SOX3 -1.46 bta-miR-2305 FIBCD1 -1.49 bta-miR-2305 SPEG -0.87 bta-miR-2305 FKBP8 -1.06 bta-miR-1777b SPI1 -1.05 bta-miR-1777b FLOT2 -0.85 bta-miR-2305 SPRED2 -0.76 bta-miR-2900 FOXJ2 -0.81 bta-miR-2900 SPRN -0.9 bta-miR-2305 FOXP4 -0.79 bta-miR-1777b SPRN -0.82 bta-miR-1777a FRMPD3 -0.95 bta-miR-1777a SPRN -0.77 bta-miR-2900 FUOM -1.21 bta-miR-2305 SPRY4 -1.35 bta-miR-2305 FXYD1 -0.8 bta-miR-2885 SPSB4 -0.81 bta-miR-1777b FXYD6 -1.28 bta-miR-2305 SPTB -0.79 bta-miR-2900 FXYD6 -1.14 bta-miR-2900 SRRM4 -0.77 bta-miR-2305 G6PD -1.32 bta-miR-2305 SSBP3-AS1 -0.99 bta-miR-1777b GAL -0.78 bta-miR-2305 ST3GAL3 -0.81 bta-miR-2900 GAS8 -1.09 bta-miR-2305 STX1A -0.99 bta-miR-1777b GAS8 -0.99 bta-miR-2900 STX1A -0.76 bta-miR-1777b GATA4 -1.2 bta-miR-2900 SUCLG1 -0.76 bta-miR-2900 GATSL2 -0.8 bta-miR-2305 SUV420H2 -1.3 bta-miR-1777b GNA12 -0.91 bta-miR-2305 SYNGAP1 -1.04 bta-miR-1777a GOLGA2 -0.84 bta-miR-1777a SYNGR1 -0.94 bta-miR-2305 GORASP1 -1.48 bta-miR-1777a TBC1D22B -1

bta-miR-1777b GORASP1 -1.15 bta-miR-2305 TBKBP1 -0.82 bta-miR-1777b GPR144 -0.91 bta-miR-1777a TBX10 -0.88 bta-miR-2305 GPS1 -1.15 bta-miR-1777a TBX5 -0.97 bta-miR-2305 GPSM1 -0.87 bta-miR-2900 TBX5 -0.81 bta-miR-1777b GSE1 -0.75 bta-miR-2305 TBX6 -0.85 bta-miR-2900 GTF3C5 -0.92 bta-miR-2900 TCF7L2 -1.02 bta-miR-2305 HCN2 -0.97 bta-miR-2305 TEAD2 -0.93 bta-miR-1777b HCN2 -0.84 bta-miR-2900 TFF3 -0.88 bta-miR-1777a HDGF -0.8 bta-miR-2900 TGM2 -0.96 bta-miR-1777a HIF3A -0.96 bta-miR-2305 THRA -1.26 bta-miR-2305 HIPK2 -0.78 bta-miR-2900 THRA -0.78 bta-miR-1777a HIRIP3 -1.21 bta-miR-2305 THTPA -0.75 bta-miR-2305 HMGA1 -1.04 bta-miR-2305 TIMP2 -0.77 bta-miR-2305 HNF4A -0.87 bta-miR-1777b TLX3 -1.41 bta-miR-2900 HOXA3 -1.66 bta-miR-2900 TLX3 -0.79 bta-miR-2305 HOXA7 -0.85 bta-miR-2305 TMEM105 -0.75 bta-miR-2305 HOXB1 -1.44 bta-miR-2900 TMEM127 -1.05 bta-miR-2900 HOXB8 -0.77 bta-miR-1777b TMEM127 -0.82 bta-miR-2305 HOXC12 -1.32 bta-miR-2305 TMEM201 -1.21 bta-miR-1777a HOXC6 -1.35 bta-miR-2305 TMEM213 -0.75 bta-miR-2900 HOXC6 -1.03 bta-miR-2305 TMEM229B -0.78 bta-miR-2305 HPCA -0.75 bta-miR-2305 TMEM249 -0.75 bta-miR-2900 HPD -2.44 bta-miR-2305 TMEM257 -0.76 bta-miR-2305 HRK -0.88 bta-miR-1777a TMEM63C -1.21 bta-miR-1777a HS3ST3B1 -0.93 bta-miR-1777b TMEM63C -0.76 bta-miR-2900 HSPB6 -1.43 bta-miR-2305 TNFRSF12A -0.77 bta-miR-2305 HSPB7 -1.85 bta-miR-2305 TNNT2 -1.08 bta-miR-1777a HSPG2 -0.98 bta-miR-1777a TNRC18 -2.02 bta-miR-2305 HTR5A-AS1 -0.96 bta-miR-2305 TNS1 -0.75 bta-miR-1777a IFITM2 -0.88 bta-miR-1777a TOM1L2 -0.96 bta-miR-2305 IGDCC3 -0.88 bta-miR-2305 TP53I11 -0.76 bta-miR-1777a IGF1R -0.81 bta-miR-2305 TPBGL -0.97 bta-miR-2305 IGF2 -1.2 bta-miR-2900 TRABD -1.69 bta-miR-2305 IGFBP2 -0.9 bta-miR-1777b TRABD -1.1 bta-miR-2305 IKZF4 -1 bta-miR-1777a TRABD -0.82 bta-miR-2305 IQSEC2 -1.48 bta-miR-2900 TRAF3 -0.82 bta-miR-1777b IQSEC3 -1 bta-miR-1777b TRAF3 -0.77 bta-miR-2900 IQSEC3 -0.95 bta-miR-2900 TRMT61A -0.77 bta-miR-1777a IQSEC3 -0.82 bta-miR-2900 TRPV4 -1.08 bta-miR-2900 ISLR2 -0.77 bta-miR-1777b TRPV4 -0.88

bta-miR-2305 KCNAB2 -1.2 bta-miR-1777b TTYH3 -2.26 bta-miR-2305 KCNC1 -0.81 bta-miR-2900 TTYH3 -2.17 bta-miR-2305 KCNC3 -1.6 bta-miR-2305 TUBB3 -0.96 bta-miR-1777b KCNIP3 -0.87 bta-miR-2305 TUBB4A -0.87 bta-miR-2305 KCNK12 -0.79 bta-miR-2305 TUSC2 -1.17 bta-miR-2305 KCNK3 -1.04 bta-miR-2305 TUSC5 -0.76 bta-miR-2305 KCTD2 -1.08 bta-miR-1777b UAP1L1 -1.33 bta-miR-1777b KHSRP -1.12 bta-miR-1777a UAP1L1 -0.91 bta-miR-2900 KHSRP -0.81 bta-miR-2900 UAP1L1 -0.84 bta-miR-2900 KIAA1671 -1.2 bta-miR-1777a UBALD1 -0.93 bta-miR-1777a KIF21B -1.88 bta-miR-2305 UBALD1 -0.86 bta-miR-1777b KIF21B -1.84 bta-miR-2885 UBALD1 -0.76 bta-miR-2900 KIF21B -1.71 bta-miR-2305 URM1 -1.06 bta-miR-2305 KIF21B -0.88 bta-miR-2305 USB1 -1.34 bta-miR-1777b KIFC2 -0.89 bta-miR-2305 VAMP1 -0.88 bta-miR-2900 KLC2 -1.41 bta-miR-2305 VAMP2 -1.66 bta-miR-2900 KLHL22 -0.94 bta-miR-2885 VAMP8 -0.81 bta-miR-2305 KMT2B -0.9 bta-miR-2900 VGF -1.17 bta-miR-2305 KMT2D -2.79 bta-miR-1777a VGF -0.92 bta-miR-1777b KRTAP4-4 -1.54 bta-miR-2305 VPS9D1 -1.11 bta-miR-1777b KSR1 -0.87 bta-miR-2305 VSX2 -0.77 bta-miR-2305 KSR2 -1.26 bta-miR-2305 WDR45 -1.14 bta-miR-2305 LARP1 -1.14 bta-miR-2900 WDTC1 -0.8 bta-miR-1777a LARS2 -0.77 bta-miR-2885 WNK2 -1.13 bta-miR-2900 LCN8 -0.78 bta-miR-1777a WNT7B -0.79 bta-miR-2305 LDB3 -0.79 bta-miR-2305 XKR7 -1.84 bta-miR-2305 LDHD -0.84 bta-miR-2900 XYLT1 -1 bta-miR-1777a LEMD2 -0.79 bta-miR-2885 YIF1B -0.95 bta-miR-1777b LEMD2 -0.76 bta-miR-1777b YWHAH -1.4 bta-miR-2885 LENG8 -0.85 bta-miR-2900 YWHAH -0.82 bta-miR-2305 LIF -1.19 bta-miR-2305 ZBTB7A -2.18 bta-miR-2305 LIN37 -0.94 bta-miR-1777a ZC3H4 -0.89 bta-miR-2900 LMOD1 -0.83 bta-miR-2305 ZC3H7B -1.66 bta-miR-2305 LMTK3 -1.31 bta-miR-1777b ZC3H7B -0.92 bta-miR-2305 LMX1B -1.74 bta-miR-2305 ZCCHC24 -1 bta-miR-2305 LRCH4 -1.11 bta-miR-2900 ZCCHC24 -0.94 bta-miR-2305 LRRC61 -1.35 bta-miR-2900 ZDHHC1 -0.89 bta-miR-1777a LSP1 -0.84 bta-miR-1777a ZDHHC8 -0.97 bta-miR-2900 LTBP4 -1.15 bta-miR-2305 ZFHX2 -0.85 bta-miR-2885 LTBP4 -0.83 bta-miR-2305 ZFP36L1 -1.46

bta-miR-1777b LTBP4 -0.83 bta-miR-2305 ZNF385A -1.47

bta-miR-2305 LY6K -0.84 bta-miR-1777a ZNF385A -1.01

bta-miR-2305 MAF -0.9 bta-miR-2900 ZNF385A -0.99

bta-miR-2900 MAFK -1.42 bta-miR-1777b ZNF385A -0.95

bta-miR-1777b MAP2K7 -0.77 bta-miR-2305 ZNF648 -0.75

bta-miR-2900 MAP4 -0.75 bta-miR-2900 ZNF783 -0.82

bta-miR-2900 MAPK1 -1.03 bta-miR-2305 ZSWIM4 -1

bta-miR-2305 MAPK12 -0.97

S. Table 6. Predicted mRNA targets of miRNAs differentially expressed between 2-cell SG

SM vs. 2-Cell FG SM.

miRNA Gene Cumulative miRNA Gene Cumulative Context Context Score Score

bta-miR-1343-5p PRX -1.51 bta-miR-2393 SRGAP3 -1 bta-miR-1343-5p KIAA0513 -1.48 bta-miR-2393 CLMP -1 bta-miR-1343-5p KSR2 -1.29 bta-miR-2393 MECP2 -1

bta-miR-1343-5p RAB37 -1.27 bta-miR-2393 NKX2-1 -1 bta-miR-1343-5p LY6G6C -1.24 bta-miR-2393 KLF12 -1 bta-miR-1343-5p EHD2 -1.22 bta-miR-2393 NKD1 -1 bta-miR-1343-5p C17orf103 -1.13 bta-miR-2393 ETV6 -1 bta-miR-1343-5p GAS8 -1.1 bta-miR-2393 RORA -1 bta-miR-1343-5p RGMA -1.03 bta-miR-2393 BRCA2 -0.85

bta-miR-1343-5p OSM -1 bta-miR-2393 RP11-67H2.1 -0.74 bta-miR-1343-5p LYPD1 -0.95 bta-miR-2393 CYLC2 -0.69 bta-miR-1343-5p PKD1 -0.95 bta-miR-2393 FAM171B -0.63 bta-miR-1343-5p MS4A15 -0.95 bta-miR-2393 C21orf37 -0.61 bta-miR-1343-5p GPA33 -0.95 bta-miR-2393 TTC30B -0.58

bta-miR-1343-5p COTL1 -0.92 bta-miR-2393 DNAJC1 -0.55 bta-miR-1343-5p FKBP1A -0.91 bta-miR-2393 OR51I2 -0.55 bta-miR-1343-5p HOXB5 -0.91 bta-miR-2393 GYPA -0.54 bta-miR-1343-5p SPSB1 -0.86 bta-miR-2393 SLC25A31 -0.54 bta-miR-1343-5p TFCP2L1 -0.86 bta-miR-2393 KRCC1 -0.54

bta-miR-1343-5p TFEB -0.86 bta-miR-2393 FAM213A -0.52 bta-miR-1343-5p FHL3 -0.86 bta-miR-2393 AC090186.1 -0.51 bta-miR-1343-5p SIPA1L3 -0.85 bta-miR-2393 OR4F4 -0.51 bta-miR-1343-5p LDLRAP1 -0.84 bta-miR-2393 DYDC1 -0.5 bta-miR-1343-5p KCTD17 -0.83 bta-miR-2393 PMPCB -0.5 bta-miR-1343-5p SIX5 -0.83 bta-miR-2412 PML -1.17

bta-miR-1343-5p KB-1507C5.2 -0.82 bta-miR-2412 COX6B2 -1.09 bta-miR-1343-5p MVB12B -0.82 bta-miR-2412 TNFSF13 -1.04

bta-miR-1343-5p ATG9A -0.8 bta-miR-2412 C15orf32 -1.03 bta-miR-1343-5p HMGA1 -0.8 bta-miR-2412 WNT4 -0.97 bta-miR-1343-5p TBC1D13 -0.79 bta-miR-2412 CTD-3203P2.2 -0.91 bta-miR-1343-5p GRB7 -0.79 bta-miR-2412 SNX32 -0.85 bta-miR-1343-5p KLK14 -0.78 bta-miR-2412 TNFSF12-TNFSF13 -0.85 bta-miR-1343-5p UBE2QL1 -0.78 bta-miR-2412 SLC34A2 -0.81 bta-miR-1343-5p THTPA -0.77 bta-miR-2412 C1QTNF6 -0.78 bta-miR-1343-5p PIRT -0.77 bta-miR-2412 KXD1 -0.75 bta-miR-1343-5p TNS4 -0.77 bta-miR-2412 CPLX3 -0.71 bta-miR-1343-5p HEPACAM -0.77 bta-miR-2412 RBPMS2 -0.68 bta-miR-1343-5p STIM1 -0.77 bta-miR-2412 CRABP2 -0.66 bta-miR-1343-5p RRP1 -0.76 bta-miR-2412 RFX1 -0.65 bta-miR-1343-5p PACRG -0.76 bta-miR-2412 SPATA3 -0.59 bta-miR-1343-5p PSORS1C1 -0.76 bta-miR-2412 FAM53B -0.59 bta-miR-1343-5p GAREML -0.76 bta-miR-2412 GRAP -0.58 bta-miR-1343-5p NUPR1 -0.75 bta-miR-2412 PSCA -0.58 bta-miR-1343-5p SERPINE3 -0.75 bta-miR-2412 CMIP -0.58 bta-miR-1343-5p AQP5 -0.75 bta-miR-2412 FAM98C -0.57 bta-miR-1343-5p TSTA3 -0.75 bta-miR-2412 PIP5K1C -0.57 bta-miR-1343-5p GLIS1 -0.75 bta-miR-2412 SEPT5 -0.56 bta-miR-1343-5p SEC14L6 -0.74 bta-miR-2412 LZTS1 -0.56 bta-miR-1343-5p LY6E -0.74 bta-miR-2412 SUV39H1 -0.56 bta-miR-1343-5p CAMKV -0.74 bta-miR-2412 FAM102A -0.55 bta-miR-1343-5p TMEM150A -0.73 bta-miR-2412 IL18RAP -0.54 bta-miR-1343-5p TMCC2 -0.73 bta-miR-2412 PRPH -0.54 bta-miR-1343-5p SCAND1 -0.73 bta-miR-2412 ENPP7 -0.53 bta-miR-1343-5p LOH12CR1 -0.72 bta-miR-2412 TARBP2 -0.52 bta-miR-1343-5p IL17REL -0.72 bta-miR-2412 GBP5 -0.52 bta-miR-1343-5p TMEM27 -0.71 bta-miR-2412 AMZ1 -0.51 bta-miR-1343-5p PRRT2 -0.71 bta-miR-2412 GBGT1 -0.51 bta-miR-1343-5p ABHD14B -0.71 bta-miR-2412 ZNRF3 -0.5 bta-miR-1343-5p RNASEH2A -0.71 bta-miR-2421 XKR4 -2.17 bta-miR-1343-5p JPH4 -0.71 bta-miR-2421 NFIA -1.79 bta-miR-1343-5p NDRG4 -0.71 bta-miR-2421 ONECUT2 -1.5 bta-miR-1343-5p RARG -0.7 bta-miR-2421 TCF4 -1.21 bta-miR-1343-5p LIF -0.7 bta-miR-2421 ELAVL4 -1.14 bta-miR-1343-5p CABP7 -0.7 bta-miR-2421 NFIB -1.09 bta-miR-1343-5p RIMS4 -0.7 bta-miR-2421 GABRB3 -1 bta-miR-1343-5p AIF1L -0.7 bta-miR-2421 THRB -1 bta-miR-1343-5p PTMS -0.7 bta-miR-2421 POU6F2 -0.99 bta-miR-1343-5p CASQ1 -0.7 bta-miR-2421 TNRC6C -0.92 bta-miR-1343-5p SERPINB6 -0.7 bta-miR-2421 IGIP -0.91 bta-miR-1343-5p GJB4 -0.7 bta-miR-2421 TNRC6B -0.87

bta-miR-1343-5p KIAA1644 -0.7 bta-miR-2421 GLIPR1L1 -0.82 bta-miR-1343-5p FAM178B -0.69 bta-miR-2421 CYLC2 -0.81 bta-miR-1343-5p GPIHBP1 -0.69 bta-miR-2421 RUNX1T1 -0.75 bta-miR-1343-5p BLOC1S3 -0.68 bta-miR-2421 PRR26 -0.74 bta-miR-1343-5p FAM222B -0.68 bta-miR-2421 ZFHX3 -0.71 bta-miR-1343-5p SLC6A8 -0.68 bta-miR-2421 TSHZ2 -0.7 bta-miR-1343-5p USP21 -0.68 bta-miR-2421 NECAB1 -0.7 bta-miR-1343-5p ZNF703 -0.68 bta-miR-2421 ELAVL3 -0.65 bta-miR-1343-5p VAMP2 -0.67 bta-miR-2421 AL359878.1 -0.6 bta-miR-1343-5p ASTN1 -0.67 bta-miR-2421 LOH12CR2 -0.54 bta-miR-1343-5p FCRL6 -0.67 bta-miR-2421 RBMS1 -0.54 bta-miR-1343-5p APLNR -0.67 bta-miR-2421 PCLO -0.54 bta-miR-1343-5p DDX41 -0.67 bta-miR-2421 ZRSR1 -0.53 bta-miR-1343-5p FAM163A -0.67 bta-miR-2421 ZBTB7A -0.52 bta-miR-1343-5p WNK2 -0.67 bta-miR-2421 CREBRF -0.51 bta-miR-1343-5p GPRC5A -0.66 bta-miR-2421 GRIN2B -0.51 bta-miR-1343-5p CDR2L -0.66 bta-miR-2434 WDR38 -0.94 bta-miR-1343-5p RP11-195F19.5 -0.66 bta-miR-2434 MT-ATP8 -0.93 bta-miR-1343-5p SNX17 -0.66 bta-miR-2434 KCNMB1 -0.8 bta-miR-1343-5p S100A2 -0.66 bta-miR-2434 FSIP1 -0.8 bta-miR-1343-5p CERS3 -0.66 bta-miR-2434 CYSLTR2 -0.76 bta-miR-1343-5p PXN -0.65 bta-miR-2434 MEI4 -0.72 bta-miR-1343-5p CTB-96E2.2 -0.65 bta-miR-2434 UBTD2 -0.72 bta-miR-1343-5p C19orf35 -0.65 bta-miR-2434 SLC13A3 -0.71 bta-miR-1343-5p TAGLN -0.65 bta-miR-2434 CRISP2 -0.7 bta-miR-1343-5p KIF17 -0.65 bta-miR-2434 MT-ND4L -0.68 bta-miR-1343-5p HOXA6 -0.65 bta-miR-2434 IMPG1 -0.68 bta-miR-1343-5p CSMD2 -0.65 bta-miR-2434 PPIL1 -0.67 bta-miR-1343-5p UQCC1 -0.65 bta-miR-2434 NAMPT -0.64 bta-miR-1343-5p C17orf80 -0.64 bta-miR-2434 AC110619.2 -0.63 bta-miR-1343-5p PAX8 -0.64 bta-miR-2434 SEC61B -0.62 bta-miR-1343-5p NUDC -0.64 bta-miR-2434 TFRC -0.62 bta-miR-1343-5p NACC1 -0.64 bta-miR-2434 NRIP1 -0.6 bta-miR-1343-5p AC145676.2 -0.64 bta-miR-2434 YIPF1 -0.6 bta-miR-1343-5p CNNM4 -0.64 bta-miR-2434 FOXM1 -0.59 bta-miR-1343-5p LMAN2L -0.64 bta-miR-2434 RAD51B -0.57 bta-miR-1343-5p PLOD3 -0.64 bta-miR-2434 SYNE3 -0.57 bta-miR-1343-5p CALCOCO1 -0.64 bta-miR-2434 ADRB2 -0.56 bta-miR-1343-5p AC024940.1 -0.64 bta-miR-2434 C16orf92 -0.56 bta-miR-1343-5p CD300LB -0.64 bta-miR-2434 HRK -0.55 bta-miR-1343-5p EFHD2 -0.64 bta-miR-2434 DDX42 -0.55 bta-miR-1343-5p CBX2 -0.64 bta-miR-2434 TST -0.55 bta-miR-1343-5p KIF21B -0.64 bta-miR-2434 EXD1 -0.55

bta-miR-1343-5p KIAA0247 -0.63 bta-miR-2434 HOXB8 -0.55 bta-miR-1343-5p KDM6B -0.63 bta-miR-2434 AL589765.1 -0.55 bta-miR-1343-5p CYP46A1 -0.63 bta-miR-2434 ZNF770 -0.55 bta-miR-1343-5p PPP2R4 -0.63 bta-miR-2434 TFAP2A -0.54 bta-miR-1343-5p FOXI2 -0.63 bta-miR-2434 DMRTC2 -0.54 bta-miR-1343-5p CRY2 -0.62 bta-miR-2434 ZC3HAV1L -0.54 bta-miR-1343-5p STX6 -0.62 bta-miR-2434 FAM174A -0.54 bta-miR-1343-5p PITX1 -0.62 bta-miR-2434 NMUR2 -0.54 bta-miR-1343-5p LINC00632 -0.62 bta-miR-2434 CTRC -0.54 bta-miR-1343-5p RNF222 -0.62 bta-miR-2434 GABARAP -0.53 bta-miR-1343-5p PPARGC1B -0.62 bta-miR-2434 HOXC8 -0.53 bta-miR-1343-5p PCYT1B -0.62 bta-miR-2434 RP11-247C2.2 -0.53 bta-miR-1343-5p KIRREL -0.62 bta-miR-2434 ADGB -0.52 bta-miR-1343-5p EPB41 -0.62 bta-miR-2434 DNAI2 -0.52 bta-miR-1343-5p MEA1 -0.62 bta-miR-2434 TNK1 -0.52 bta-miR-1343-5p VSTM5 -0.61 bta-miR-2434 DPM2 -0.52 bta-miR-1343-5p SERINC2 -0.61 bta-miR-2434 PEX11G -0.51 bta-miR-1343-5p NFAM1 -0.61 bta-miR-2434 KRTAP19-2 -0.5 bta-miR-1343-5p TNS1 -0.61 bta-miR-2434 P2RY13 -0.5 bta-miR-1343-5p PYGM -0.61 bta-miR-2434 TP63 -0.5 bta-miR-1343-5p TATDN3 -0.61 bta-miR-2434 SOSTDC1 -0.5 bta-miR-1343-5p CLEC3A -0.6 bta-miR-2444 RP4-758J18.2 -1.05 bta-miR-1343-5p CTC-360G5.1 -0.6 bta-miR-2444 RP11-67H2.1 -0.92 bta-miR-1343-5p SHISA9 -0.6 bta-miR-2444 GORASP1 -0.91 bta-miR-1343-5p ERGIC1 -0.6 bta-miR-2444 RP11-105C20.2 -0.82 bta-miR-1343-5p SLC7A5 -0.6 bta-miR-2444 OR6C65 -0.79 bta-miR-1343-5p SNCG -0.6 bta-miR-2444 RP11-169F17.1 -0.79 bta-miR-1343-5p ALX4 -0.6 bta-miR-2444 AC090186.1 -0.69 bta-miR-1343-5p AGAP2-AS1 -0.59 bta-miR-2444 GLRA3 -0.68 bta-miR-1343-5p TNFSF12 -0.59 bta-miR-2444 TFEC -0.66 bta-miR-1343-5p RHOB -0.59 bta-miR-2444 AMELY -0.64 bta-miR-1343-5p PPIE -0.59 bta-miR-2444 AMELX -0.63 bta-miR-1343-5p KRTAP10-1 -0.59 bta-miR-2444 SMIM11 -0.62 bta-miR-1343-5p CHST1 -0.59 bta-miR-2444 AICDA -0.62 bta-miR-1343-5p PVRIG -0.59 bta-miR-2444 EI24 -0.61 bta-miR-1343-5p KCNMB1 -0.59 bta-miR-2444 NRXN1 -0.61 bta-miR-1343-5p MTAP -0.59 bta-miR-2444 CMBL -0.61 bta-miR-1343-5p PPP2R5D -0.59 bta-miR-2444 OR5H14 -0.6 bta-miR-1343-5p PRRG3 -0.59 bta-miR-2444 LCMT2 -0.58 bta-miR-1343-5p KCNC3 -0.59 bta-miR-2444 AJAP1 -0.58 bta-miR-1343-5p UCP2 -0.58 bta-miR-2444 PLGRKT -0.58 bta-miR-1343-5p TAL1 -0.58 bta-miR-2444 ELAVL3 -0.57 bta-miR-1343-5p AP001652.1 -0.58 bta-miR-2444 DRD5 -0.57

100

bta-miR-1343-5p NUP98 -0.58 bta-miR-2444 ZPBP -0.57 bta-miR-1343-5p GATS -0.58 bta-miR-2444 NXPE4 -0.57 bta-miR-1343-5p AC006946.15 -0.58 bta-miR-2444 DNMT3A -0.57 bta-miR-1343-5p TMEM79 -0.58 bta-miR-2444 RBFOX2 -0.56 bta-miR-1343-5p SNX12 -0.58 bta-miR-2444 AAED1 -0.56 bta-miR-1343-5p CELSR2 -0.57 bta-miR-2444 CDC26 -0.56 bta-miR-1343-5p KRT17 -0.57 bta-miR-2444 PTP4A2 -0.54 bta-miR-1343-5p NHP2 -0.57 bta-miR-2444 CIR1 -0.54 bta-miR-1343-5p DUSP28 -0.57 bta-miR-2444 AC140061.12 -0.54 bta-miR-1343-5p RBMXL2 -0.57 bta-miR-2444 SAMD12 -0.53 bta-miR-1343-5p PTK2B -0.57 bta-miR-2444 TLE1 -0.53 bta-miR-1343-5p PGS1 -0.57 bta-miR-2444 KB-1980E6.3 -0.53 bta-miR-1343-5p CSNK2B-LY6G5B-1181 -0.57 bta-miR-2444 ELAVL4 -0.53 bta-miR-1343-5p GABARAPL1 -0.57 bta-miR-2444 GKAP1 -0.52 bta-miR-1343-5p SYNGR1 -0.57 bta-miR-2444 PLCXD2 -0.52 bta-miR-1343-5p OBP2B -0.57 bta-miR-2444 C1orf63 -0.51 bta-miR-1343-5p LOXL1 -0.56 bta-miR-2444 AC002451.1 -0.51 bta-miR-1343-5p HDAC10 -0.56 bta-miR-2444 C20orf85 -0.51 bta-miR-1343-5p EMC10 -0.56 bta-miR-2444 SSMEM1 -0.51 bta-miR-1343-5p CEACAM1 -0.56 bta-miR-2444 C2orf83 -0.51 bta-miR-1343-5p CD7 -0.56 bta-miR-2444 ANGPTL6 -0.51 bta-miR-1343-5p MPP2 -0.56 bta-miR-2444 RP11-1102P16.1 -0.5 bta-miR-1343-5p ELOVL1 -0.56 bta-miR-2444 HNRNPUL1 -0.5 bta-miR-1343-5p LZTS2 -0.56 bta-miR-2444 AC005477.1 -0.5 bta-miR-1343-5p NYAP1 -0.56 bta-miR-2452 AC004899.1 -1.73 bta-miR-1343-5p DCHS1 -0.56 bta-miR-2452 NTNG1 -1.29 bta-miR-1343-5p HRH3 -0.56 bta-miR-2452 C15orf32 -1.17 bta-miR-1343-5p CLTCL1 -0.56 bta-miR-2452 PDE7B -1 bta-miR-1343-5p NIPSNAP1 -0.55 bta-miR-2452 LIMK2 -1 bta-miR-1343-5p GSC -0.55 bta-miR-2452 ZNF23 -0.88 bta-miR-1343-5p COL5A3 -0.55 bta-miR-2452 SMCP -0.83 bta-miR-1343-5p ADCYAP1R1 -0.55 bta-miR-2452 GGACT -0.73 bta-miR-1343-5p NOTUM -0.55 bta-miR-2452 TMPRSS11F -0.7 bta-miR-1343-5p RPP30 -0.55 bta-miR-2452 SPP2 -0.66 bta-miR-1343-5p POLR2J2 -0.55 bta-miR-2452 TBCE -0.66 bta-miR-1343-5p PNMA6A -0.55 bta-miR-2452 C2orf74 -0.64 bta-miR-1343-5p TNNT2 -0.55 bta-miR-2452 TMEM223 -0.64 bta-miR-1343-5p ONECUT3 -0.55 bta-miR-2452 APOBEC2 -0.62 bta-miR-1343-5p POLE3 -0.55 bta-miR-2452 SATL1 -0.62 bta-miR-1343-5p ATXN2L -0.55 bta-miR-2452 CRYBB1 -0.62 bta-miR-1343-5p C10orf105 -0.55 bta-miR-2452 TMED6 -0.59 bta-miR-1343-5p TBC1D22B -0.55 bta-miR-2452 C17orf67 -0.59 bta-miR-1343-5p PRSS42 -0.55 bta-miR-2452 PPAP2B -0.56

101

bta-miR-1343-5p DKFZP434O1614 -0.55 bta-miR-2452 C2orf83 -0.54 bta-miR-1343-5p FAM101A -0.55 bta-miR-2452 PSME4 -0.54 bta-miR-1343-5p PPP1R32 -0.55 bta-miR-2452 CDC37 -0.54 bta-miR-1343-5p STC1 -0.55 bta-miR-2452 ZNF785 -0.54 bta-miR-1343-5p CYB5R3 -0.54 bta-miR-2452 C2orf88 -0.52 bta-miR-1343-5p CCDC142 -0.54 bta-miR-2452 TAL2 -0.52 bta-miR-1343-5p C6orf223 -0.54 bta-miR-2452 VBP1 -0.51 bta-miR-1343-5p ATP6V0D1 -0.54 bta-miR-2452 AL138847.1 -0.5 bta-miR-1343-5p C2orf62 -0.54 bta-miR-2452 PPP1R14B -0.5 bta-miR-1343-5p PPP5C -0.54 bta-miR-2452 CHST9 -0.5 bta-miR-1343-5p IRGQ -0.54 bta-miR-2452 AC015987.2 -0.5 bta-miR-1343-5p CCL22 -0.54 bta-miR-2452 TBL1Y -0.5 bta-miR-1343-5p ZDHHC7 -0.54 bta-miR-3613a ZNF30 -0.88 bta-miR-1343-5p TRIM3 -0.54 bta-miR-3613a RNASE3 -0.88 bta-miR-1343-5p WDTC1 -0.54 bta-miR-3613a RNASE2 -0.88 bta-miR-1343-5p COLQ -0.54 bta-miR-3613a RP11-105C20.2 -0.81 bta-miR-1343-5p JUP -0.54 bta-miR-3613a AKAP6 -0.77 bta-miR-1343-5p LEPREL4 -0.54 bta-miR-3613a C9orf129 -0.76 bta-miR-1343-5p NTN3 -0.54 bta-miR-3613a EXOSC1 -0.75 bta-miR-1343-5p DCAKD -0.54 bta-miR-3613a GPX4 -0.66 bta-miR-1343-5p C1orf134 -0.53 bta-miR-3613a ZFYVE21 -0.64 bta-miR-1343-5p ELOVL2 -0.53 bta-miR-3613a CTC-241N9.1 -0.59 bta-miR-1343-5p PRKCG -0.53 bta-miR-3613a ANP32B -0.59 bta-miR-1343-5p FOXM1 -0.53 bta-miR-3613a C2orf73 -0.57 bta-miR-1343-5p RPH3A -0.53 bta-miR-3613a KRT35 -0.57 bta-miR-1343-5p TMEM151B -0.53 bta-miR-3613a HIST2H4B -0.56 bta-miR-1343-5p R3HCC1 -0.53 bta-miR-3613a AL162407.1 -0.54 bta-miR-1343-5p VPS53 -0.53 bta-miR-3613a CASP5 -0.53 bta-miR-1343-5p CCDC149 -0.53 bta-miR-3613a LYRM2 -0.53 bta-miR-1343-5p LDLRAD2 -0.53 bta-miR-3613a ZNF667 -0.52 bta-miR-1343-5p GABBR2 -0.53 bta-miR-3613a IFRD2 -0.52 bta-miR-1343-5p RHBDF2 -0.53 bta-miR-3613a AC007390.5 -0.52 bta-miR-1343-5p CX3CL1 -0.52 bta-miR-3613a STK38 -0.51 bta-miR-1343-5p LBH -0.52 bta-miR-3613a AL590452.1 -0.51 bta-miR-1343-5p ICAM1 -0.52 bta-miR-3613a GATA2 -0.5 bta-miR-1343-5p ABHD17A -0.52 bta-miR-3613a CYP2C9 -0.5 bta-miR-1343-5p TBC1D16 -0.52 bta-miR-3613b GAPVD1 -1 bta-miR-1343-5p GDF11 -0.52 bta-miR-3613b GAS7 -1 bta-miR-1343-5p CNOT3 -0.52 bta-miR-3613b USP42 -1 bta-miR-1343-5p GINS2 -0.52 bta-miR-3613b CCDC132 -1 bta-miR-1343-5p ANGPTL6 -0.52 bta-miR-3613b TMBIM6 -1 bta-miR-1343-5p FBXO31 -0.52 bta-miR-3613b KDELR2 -1 bta-miR-1343-5p TUBB4A -0.51 bta-miR-3613b GOLT1B -1

102

bta-miR-1343-5p ZBTB46 -0.51 bta-miR-3613b SIK2 -1 bta-miR-1343-5p SH3TC2 -0.51 bta-miR-3613b UBR2 -1 bta-miR-1343-5p ATP5D -0.51 bta-miR-3613b KPNA6 -1 bta-miR-1343-5p RAX -0.51 bta-miR-3613b MFN2 -1 bta-miR-1343-5p RNF165 -0.51 bta-miR-3613b HSD17B12 -1 bta-miR-1343-5p MMP24 -0.51 bta-miR-3613b DEDD -1 bta-miR-1343-5p SELRC1 -0.51 bta-miR-3613b CDK12 -1 bta-miR-1343-5p LPCAT3 -0.51 bta-miR-3613b IKZF4 -1 bta-miR-1343-5p VAT1 -0.51 bta-miR-3613b LONRF2 -1 bta-miR-1343-5p USP46 -0.51 bta-miR-3613b RHOQ -1 bta-miR-1343-5p SZT2 -0.51 bta-miR-3613b LGI2 -1 bta-miR-1343-5p CTRL -0.51 bta-miR-3613b FAM126A -1 bta-miR-1343-5p SLC16A2 -0.51 bta-miR-3613b ZFP37 -1 bta-miR-1343-5p C11orf42 -0.51 bta-miR-3613b SNX30 -1 bta-miR-1343-5p TOM1L2 -0.5 bta-miR-3613b GLE1 -1 bta-miR-1343-5p PLEKHG4 -0.5 bta-miR-3613b ZIC5 -1 bta-miR-1343-5p KRTAP10-9 -0.5 bta-miR-3613b AGAP2 -1 bta-miR-1343-5p THY1 -0.5 bta-miR-3613b RBM25 -1 bta-miR-1343-5p PRR11 -0.5 bta-miR-3613b ARHGAP35 -1 bta-miR-1343-5p GRINA -0.5 bta-miR-3613b MRRF -1 bta-miR-1343-5p GIPR -0.5 bta-miR-3613b USP38 -1 bta-miR-1343-5p TEX35 -0.5 bta-miR-3613b CPD -1 bta-miR-1343-5p GAS2L2 -0.5 bta-miR-3613b CEP128 -1 bta-miR-1343-5p TRPM4 -0.5 bta-miR-3613b CELF2 -0.92 bta-miR-1343-5p TMEM222 -0.5 bta-miR-3613b FGFBP2 -0.54 bta-miR-1343-5p C9orf171 -0.5 bta-miR-455-3p SLC25A3 -0.71 bta-miR-1343-5p BAHD1 -0.5 bta-miR-455-3p PSMA2 -0.63 bta-miR-1343-5p TPRG1L -0.5 bta-miR-455-3p HOXC4 -0.57 bta-miR-1343-5p UROC1 -0.5 bta-miR-455-3p NDUFA2 -0.56 bta-miR-1343-5p PROSC -0.5 bta-miR-455-3p CUL3 -0.54 bta-miR-1343-5p RCN3 -0.5 bta-miR-455-3p INIP -0.53 bta-miR-1343-5p DPF1 -0.5 bta-miR-455-3p TSPAN18 -0.51 bta-miR-1343-5p SH2B1 -0.5 bta-miR-455-3p COLEC12 -0.5 bta-miR-1343-5p RASD2 -0.5 bta-miR-455-3p SSR1 -0.5 bta-miR-1343-5p RALB -0.5 bta-miR-628 LL22NC03-63E9.3 -1.19 bta-miR-2325a LRRC4C -0.88 bta-miR-628 POC1B-GALNT4 -1.16 bta-miR-2325a SYNE2 -0.56 bta-miR-628 PGBD4 -1.11 bta-miR-2325a PTMA -0.56 bta-miR-628 BOD1L2 -0.95 bta-miR-2325a SCAF4 -0.52 bta-miR-628 GALNT4 -0.82 bta-miR-2325a FAM155A -0.5 bta-miR-628 KATNAL2 -0.79 bta-miR-2325c TTN -1.52 bta-miR-628 ADH7 -0.78 bta-miR-2325c SMAD4 -1 bta-miR-628 COL9A3 -0.72 bta-miR-2325c C7orf66 -0.89 bta-miR-628 CHST9 -0.72

103

bta-miR-2325c RSPO3 -0.88 bta-miR-628 TAS2R14 -0.72 bta-miR-2325c NECAB1 -0.87 bta-miR-628 NBPF16 -0.7 bta-miR-2325c BOD1L2 -0.87 bta-miR-628 GALNT9 -0.69 bta-miR-2325c RP11-595B24.2 -0.85 bta-miR-628 SPATA33 -0.69 bta-miR-2325c ELAVL4 -0.84 bta-miR-628 STEAP1B -0.68 bta-miR-2325c CACNG2 -0.73 bta-miR-628 AC013269.5 -0.67 bta-miR-2325c U2AF1 -0.69 bta-miR-628 AP3B2 -0.66 bta-miR-2325c PTMA -0.68 bta-miR-628 ZNF277 -0.65 bta-miR-2325c NRXN3 -0.66 bta-miR-628 FABP1 -0.64 bta-miR-2325c AGBL3 -0.64 bta-miR-628 SNRPA1 -0.63 bta-miR-2325c MTMR8 -0.59 bta-miR-628 EAF1 -0.62 bta-miR-2325c MUC19 -0.58 bta-miR-628 C8orf22 -0.61 bta-miR-2325c ZNF30 -0.58 bta-miR-628 DALRD3 -0.6 bta-miR-2325c CTD-2054N24.2 -0.57 bta-miR-628 SDIM1 -0.6 bta-miR-2325c FAM27E1 -0.57 bta-miR-628 CST3 -0.59 bta-miR-2325c FAM27E3 -0.57 bta-miR-628 HRNR -0.58 bta-miR-2325c OR5H14 -0.56 bta-miR-628 GOLT1B -0.58 bta-miR-2325c FAM27E2 -0.55 bta-miR-628 CCDC146 -0.58 bta-miR-2325c SMIM11 -0.55 bta-miR-628 ZBTB37 -0.57 bta-miR-2325c ZG16 -0.54 bta-miR-628 ATG4A -0.57 bta-miR-2325c EMC4 -0.53 bta-miR-628 PDCD5 -0.57 bta-miR-2325c FAM185A -0.51 bta-miR-628 ZGLP1 -0.56 bta-miR-2325c CTB-186H2.3 -0.51 bta-miR-628 SCRN3 -0.56 bta-miR-2325c PLN -0.51 bta-miR-628 CLMP -0.56 bta-miR-2325c RPN1 -0.51 bta-miR-628 TMEM57 -0.54 bta-miR-2325c DKFZP779J2370 -0.5 bta-miR-628 SERPINA5 -0.54 bta-miR-2359 RP4-758J18.2 -1.17 bta-miR-628 DMC1 -0.54 bta-miR-2359 SOX6 -1 bta-miR-628 FAN1 -0.53 bta-miR-2359 CDC123 -0.72 bta-miR-628 EXOSC10 -0.53 bta-miR-2359 SPINK13 -0.71 bta-miR-628 NADK -0.53 bta-miR-2359 S100A7 -0.67 bta-miR-628 DCX -0.53 bta-miR-2359 CCT6B -0.64 bta-miR-628 TRAF5 -0.53 bta-miR-2359 S100A7L2 -0.62 bta-miR-628 TRIM9 -0.52 bta-miR-2359 COX6A2 -0.61 bta-miR-628 TMEM200C -0.52 bta-miR-2359 MRPS18B -0.58 bta-miR-628 DAOA -0.52 bta-miR-2359 ELANE -0.56 bta-miR-628 CDC25C -0.52 bta-miR-2359 OSTN -0.56 bta-miR-628 RNF34 -0.51 bta-miR-2359 RP11-67H2.1 -0.54 bta-miR-628 ZFP41 -0.51 bta-miR-2359 TMEFF2 -0.53 bta-miR-628 CST9L -0.51 bta-miR-2359 LAMTOR3 -0.52 bta-miR-628 FUNDC2 -0.51 bta-miR-2359 SERPINB5 -0.51 bta-miR-628 AC074091.13 -0.51 bta-miR-2359 DCAF4L2 -0.5 bta-miR-628 DOK2 -0.51 bta-miR-2393 NBPF16 -1.05 bta-miR-628 MORC4 -0.51

104

bta-miR-2393 C12orf36 -1.04 bta-miR-628 NNT -0.5

bta-miR-628 KRT31 -0.5

S. Table 7. Predicted mRNA targets of miRNAs differentially expressed between 8-cell SG SM vs. 8-Cell FG SM.

miRNAs Genes Cumulative miRNAs Genes Cumulative Context Score Context Score

bta-miR-1281 HIST2H3A -1.22 bta-miR-1343-5p TUBB4A -0.51

bta-miR-1281 AL592284.1 -1 bta-miR-1343-5p ZBTB46 -0.51

bta-miR-1281 C22orf46 -0.96 bta-miR-1343-5p SH3TC2 -0.51

bta-miR-1281 SLC11A1 -0.94 bta-miR-1343-5p ATP5D -0.51

bta-miR-1281 GAB2 -0.89 bta-miR-1343-5p RAX -0.51

bta-miR-1281 MANBAL -0.83 bta-miR-1343-5p RNF165 -0.51

bta-miR-1281 LTB -0.83 bta-miR-1343-5p MMP24 -0.51

bta-miR-1281 MSANTD1 -0.8 bta-miR-1343-5p SELRC1 -0.51

bta-miR-1281 LIF -0.8 bta-miR-1343-5p LPCAT3 -0.51

bta-miR-1281 TEX22 -0.8 bta-miR-1343-5p VAT1 -0.51

bta-miR-1281 FAM98A -0.78 bta-miR-1343-5p USP46 -0.51

bta-miR-1281 CTD-3214H19.16 -0.78 bta-miR-1343-5p SZT2 -0.51

bta-miR-1281 ADAMTS13 -0.78 bta-miR-1343-5p CTRL -0.51

bta-miR-1281 AGFG2 -0.76 bta-miR-1343-5p SLC16A2 -0.51

bta-miR-1281 DDX49 -0.76 bta-miR-1343-5p C11orf42 -0.51

bta-miR-1281 USP39 -0.69 bta-miR-1343-5p TOM1L2 -0.5

bta-miR-1281 ZCCHC24 -0.69 bta-miR-1343-5p PLEKHG4 -0.5

bta-miR-1281 SLC25A21 -0.65 bta-miR-1343-5p KRTAP10-9 -0.5

bta-miR-1281 CELF1 -0.64 bta-miR-1343-5p THY1 -0.5

bta-miR-1281 SLC35B2 -0.64 bta-miR-1343-5p PRR11 -0.5

bta-miR-1281 FAM170B -0.64 bta-miR-1343-5p GRINA -0.5

bta-miR-1281 PRX -0.64 bta-miR-1343-5p GIPR -0.5

bta-miR-1281 GPR26 -0.63 bta-miR-1343-5p TEX35 -0.5

bta-miR-1281 APOL6 -0.63 bta-miR-1343-5p GAS2L2 -0.5

bta-miR-1281 LSM12 -0.6 bta-miR-1343-5p TRPM4 -0.5

bta-miR-1281 KLHL30 -0.59 bta-miR-1343-5p TMEM222 -0.5

bta-miR-1281 BMP8B -0.59 bta-miR-1343-5p C9orf171 -0.5

bta-miR-1281 AC074212.3 -0.59 bta-miR-1343-5p BAHD1 -0.5

bta-miR-1281 IDH2 -0.58 bta-miR-1343-5p TPRG1L -0.5

bta-miR-1281 SH3GLB2 -0.58 bta-miR-1343-5p UROC1 -0.5

bta-miR-1281 FLJ27365 -0.58 bta-miR-1343-5p PROSC -0.5

bta-miR-1281 PAQR6 -0.57 bta-miR-1343-5p RCN3 -0.5

bta-miR-1281 TSPO2 -0.57 bta-miR-1343-5p DPF1 -0.5

105

bta-miR-1281 FLJ00418 -0.56 bta-miR-1343-5p SH2B1 -0.5 bta-miR-1281 PHB2 -0.56 bta-miR-1343-5p RASD2 -0.5 bta-miR-1281 SCAMP3 -0.56 bta-miR-1343-5p RALB -0.5 bta-miR-1281 GRK5 -0.56 bta-miR-2885 RGMA -1.23 bta-miR-1281 RIMS4 -0.55 bta-miR-2885 WNK2 -1.13 bta-miR-1281 ZNF70 -0.55 bta-miR-2885 PLA2G1B -1.08 bta-miR-1281 BOK -0.55 bta-miR-2885 SLC25A28 -1.02 bta-miR-1281 NSG2 -0.54 bta-miR-2885 YIF1B -0.95 bta-miR-1281 KAZALD1 -0.54 bta-miR-2885 AGFG2 -0.95 bta-miR-1281 CNIH3 -0.54 bta-miR-2885 RAX -0.91 bta-miR-1281 FBXW7 -0.54 bta-miR-2885 MZF1 -0.86 bta-miR-1281 AC005609.1 -0.53 bta-miR-2885 FAM73B -0.86 bta-miR-1281 NAB2 -0.53 bta-miR-2885 LENG8 -0.85 bta-miR-1281 SLC39A11 -0.53 bta-miR-2885 LTBP4 -0.83 bta-miR-1281 BHLHE22 -0.52 bta-miR-2885 CERS1 -0.83 bta-miR-1281 KLHL29 -0.52 bta-miR-2885 CABP1 -0.83 bta-miR-1281 PMFBP1 -0.52 bta-miR-2885 MARK4 -0.83 bta-miR-1281 E4F1 -0.52 bta-miR-2885 AL450307.1 -0.82 bta-miR-1281 CLPP -0.52 bta-miR-2885 VAMP8 -0.81 bta-miR-1281 NFATC4 -0.52 bta-miR-2885 SPSB4 -0.81 bta-miR-1281 LDB2 -0.51 bta-miR-2885 NKX2-5 -0.8 bta-miR-1281 C1orf64 -0.51 bta-miR-2885 CCDC64 -0.8 bta-miR-1281 ZBTB7C -0.51 bta-miR-2885 C3orf20 -0.79 DKFZP779J237 bta-miR-1281 ZNF575 -0.5 bta-miR-2885 0 -0.78 bta-miR-1281 NKX6-3 -0.5 bta-miR-2885 UBALD1 -0.76 bta-miR-1281 AHRR -0.5 bta-miR-2885 FAM219A -0.75 bta-miR-1343-5p PRX -1.51 bta-miR-2885 PTBP1 -0.75 bta-miR-1343-5p KIAA0513 -1.48 bta-miR-2885 PACSIN2 -0.75 bta-miR-1343-5p KSR2 -1.29 bta-miR-2885 AL590822.1 -0.75 bta-miR-1343-5p RAB37 -1.27 bta-miR-2885 PEG10 -0.74 bta-miR-1343-5p LY6G6C -1.24 bta-miR-2885 FTCDNL1 -0.73 bta-miR-1343-5p EHD2 -1.22 bta-miR-2885 MIB2 -0.73 bta-miR-1343-5p C17orf103 -1.13 bta-miR-2885 ATAD3B -0.73 bta-miR-1343-5p GAS8 -1.1 bta-miR-2885 IQSEC2 -0.72 bta-miR-1343-5p RGMA -1.03 bta-miR-2885 MAP2K3 -0.71 bta-miR-1343-5p OSM -1 bta-miR-2885 HDAC11 -0.71 bta-miR-1343-5p LYPD1 -0.95 bta-miR-2885 C17orf103 -0.71 bta-miR-1343-5p PKD1 -0.95 bta-miR-2885 ACVR1B -0.71 bta-miR-1343-5p MS4A15 -0.95 bta-miR-2885 PTCD2 -0.71 bta-miR-1343-5p GPA33 -0.95 bta-miR-2885 AC002472.13 -0.7

106

bta-miR-1343-5p COTL1 -0.92 bta-miR-2885 FLJ45079 -0.7 bta-miR-1343-5p FKBP1A -0.91 bta-miR-2885 REPIN1 -0.7 bta-miR-1343-5p HOXB5 -0.91 bta-miR-2885 REXO2 -0.69 bta-miR-1343-5p SPSB1 -0.86 bta-miR-2885 FOSB -0.68 bta-miR-1343-5p TFCP2L1 -0.86 bta-miR-2885 GTF2IRD1 -0.68 bta-miR-1343-5p TFEB -0.86 bta-miR-2885 TFAP2A -0.68 bta-miR-1343-5p FHL3 -0.86 bta-miR-2885 TXNRD3 -0.67 bta-miR-1343-5p SIPA1L3 -0.85 bta-miR-2885 CTC1 -0.67 bta-miR-1343-5p LDLRAP1 -0.84 bta-miR-2885 ZNF827 -0.67 bta-miR-1343-5p KCTD17 -0.83 bta-miR-2885 CTXN1 -0.67 bta-miR-1343-5p SIX5 -0.83 bta-miR-2885 PI4KB -0.67 bta-miR-1343-5p KB-1507C5.2 -0.82 bta-miR-2885 PRKAR1B -0.66 bta-miR-1343-5p MVB12B -0.82 bta-miR-2885 KLHL18 -0.66 bta-miR-1343-5p ATG9A -0.8 bta-miR-2885 EXTL1 -0.66 bta-miR-1343-5p HMGA1 -0.8 bta-miR-2885 MIDN -0.66 bta-miR-1343-5p TBC1D13 -0.79 bta-miR-2885 MEX3B -0.65 bta-miR-1343-5p GRB7 -0.79 bta-miR-2885 TOX2 -0.65 bta-miR-1343-5p KLK14 -0.78 bta-miR-2885 EDNRA -0.65 bta-miR-1343-5p UBE2QL1 -0.78 bta-miR-2885 C20orf26 -0.65 bta-miR-1343-5p THTPA -0.77 bta-miR-2885 DOLPP1 -0.64 bta-miR-1343-5p PIRT -0.77 bta-miR-2885 PCGF3 -0.64 bta-miR-1343-5p TNS4 -0.77 bta-miR-2885 VPS37B -0.64 bta-miR-1343-5p HEPACAM -0.77 bta-miR-2885 HSPB6 -0.63 bta-miR-1343-5p STIM1 -0.77 bta-miR-2885 TWIST2 -0.62 bta-miR-1343-5p RRP1 -0.76 bta-miR-2885 SHISA6 -0.62 bta-miR-1343-5p PACRG -0.76 bta-miR-2885 ICOSLG -0.62 bta-miR-1343-5p PSORS1C1 -0.76 bta-miR-2885 ATP13A2 -0.62 bta-miR-1343-5p GAREML -0.76 bta-miR-2885 AC026703.1 -0.61 bta-miR-1343-5p NUPR1 -0.75 bta-miR-2885 TLR4 -0.61 bta-miR-1343-5p SERPINE3 -0.75 bta-miR-2885 LEMD2 -0.61 bta-miR-1343-5p AQP5 -0.75 bta-miR-2885 VAT1L -0.6 bta-miR-1343-5p TSTA3 -0.75 bta-miR-2885 CDK3 -0.6 bta-miR-1343-5p GLIS1 -0.75 bta-miR-2885 FOXK1 -0.6 bta-miR-1343-5p SEC14L6 -0.74 bta-miR-2885 CRTC1 -0.6 bta-miR-1343-5p LY6E -0.74 bta-miR-2885 TUFT1 -0.6 bta-miR-1343-5p CAMKV -0.74 bta-miR-2885 RP4-559A3.7 -0.59 bta-miR-1343-5p TMEM150A -0.73 bta-miR-2885 CHMP7 -0.59 bta-miR-1343-5p TMCC2 -0.73 bta-miR-2885 HRAS -0.59 bta-miR-1343-5p SCAND1 -0.73 bta-miR-2885 TMEM129 -0.59 bta-miR-1343-5p LOH12CR1 -0.72 bta-miR-2885 EEPD1 -0.58

107

bta-miR-1343-5p IL17REL -0.72 bta-miR-2885 MYO9B -0.58 bta-miR-1343-5p TMEM27 -0.71 bta-miR-2885 ZFP36L1 -0.58 bta-miR-1343-5p PRRT2 -0.71 bta-miR-2885 MVB12B -0.58 bta-miR-1343-5p ABHD14B -0.71 bta-miR-2885 MAP2K7 -0.57 bta-miR-1343-5p RNASEH2A -0.71 bta-miR-2885 GORASP1 -0.57 bta-miR-1343-5p JPH4 -0.71 bta-miR-2885 TTYH3 -0.57 bta-miR-1343-5p NDRG4 -0.71 bta-miR-2885 TRPC5 -0.57 bta-miR-1343-5p RARG -0.7 bta-miR-2885 RASA4 -0.56 bta-miR-1343-5p LIF -0.7 bta-miR-2885 OAF -0.56 bta-miR-1343-5p CABP7 -0.7 bta-miR-2885 APOC4 -0.56 bta-miR-1343-5p RIMS4 -0.7 bta-miR-2885 NCS1 -0.56 bta-miR-1343-5p AIF1L -0.7 bta-miR-2885 C10orf105 -0.56 bta-miR-1343-5p PTMS -0.7 bta-miR-2885 LRRC8A -0.56 bta-miR-1343-5p CASQ1 -0.7 bta-miR-2885 ARHGAP35 -0.56 bta-miR-1343-5p SERPINB6 -0.7 bta-miR-2885 PDCD5 -0.56 bta-miR-1343-5p GJB4 -0.7 bta-miR-2885 CBX2 -0.56 bta-miR-1343-5p KIAA1644 -0.7 bta-miR-2885 RPS6KA2 -0.56 bta-miR-1343-5p FAM178B -0.69 bta-miR-2885 CORO7 -0.55 bta-miR-1343-5p GPIHBP1 -0.69 bta-miR-2885 ATG4B -0.55 bta-miR-1343-5p BLOC1S3 -0.68 bta-miR-2885 CRB2 -0.55 bta-miR-1343-5p FAM222B -0.68 bta-miR-2885 MOCS1 -0.55 bta-miR-1343-5p SLC6A8 -0.68 bta-miR-2885 CCHCR1 -0.55 bta-miR-1343-5p USP21 -0.68 bta-miR-2885 JPH4 -0.54 bta-miR-1343-5p ZNF703 -0.68 bta-miR-2885 BRSK2 -0.54 bta-miR-1343-5p VAMP2 -0.67 bta-miR-2885 SBK2 -0.54 bta-miR-1343-5p ASTN1 -0.67 bta-miR-2885 FAM126A -0.53 bta-miR-1343-5p FCRL6 -0.67 bta-miR-2885 RBM38 -0.53 bta-miR-1343-5p APLNR -0.67 bta-miR-2885 CAMK2N2 -0.53 bta-miR-1343-5p DDX41 -0.67 bta-miR-2885 ATG16L1 -0.53 bta-miR-1343-5p FAM163A -0.67 bta-miR-2885 MEGF8 -0.53 bta-miR-1343-5p WNK2 -0.67 bta-miR-2885 CYTH1 -0.52 bta-miR-1343-5p GPRC5A -0.66 bta-miR-2885 TMEM222 -0.52 bta-miR-1343-5p CDR2L -0.66 bta-miR-2885 MEF2A -0.52 bta-miR-1343-5p RP11-195F19.5 -0.66 bta-miR-2885 ASIC3 -0.52 bta-miR-1343-5p SNX17 -0.66 bta-miR-2885 C19orf26 -0.52 bta-miR-1343-5p S100A2 -0.66 bta-miR-2885 FAM155A -0.52 bta-miR-1343-5p CERS3 -0.66 bta-miR-2885 SEMA4D -0.51 bta-miR-1343-5p PXN -0.65 bta-miR-2885 HIC2 -0.51 bta-miR-1343-5p CTB-96E2.2 -0.65 bta-miR-2885 CST5 -0.51 bta-miR-1343-5p C19orf35 -0.65 bta-miR-2885 KLHL26 -0.51

108

bta-miR-1343-5p TAGLN -0.65 bta-miR-2885 ATP6V0C -0.51 bta-miR-1343-5p KIF17 -0.65 bta-miR-2885 MSRB3 -0.51 bta-miR-1343-5p HOXA6 -0.65 bta-miR-2885 MUSTN1 -0.51 bta-miR-1343-5p CSMD2 -0.65 bta-miR-2885 SMG6 -0.51 bta-miR-1343-5p UQCC1 -0.65 bta-miR-2885 CALY -0.51 bta-miR-1343-5p C17orf80 -0.64 bta-miR-2885 TCN2 -0.51 bta-miR-1343-5p PAX8 -0.64 bta-miR-2885 KIF21B -0.51 bta-miR-1343-5p NUDC -0.64 bta-miR-2885 EZR -0.5 bta-miR-1343-5p NACC1 -0.64 bta-miR-2885 CACNA2D2 -0.5 bta-miR-1343-5p AC145676.2 -0.64 bta-miR-2885 KLF13 -0.5 bta-miR-1343-5p CNNM4 -0.64 bta-miR-2885 ONECUT3 -0.5 bta-miR-1343-5p LMAN2L -0.64 bta-miR-2885 NRARP -0.5 bta-miR-1343-5p PLOD3 -0.64 bta-miR-2885 PRR18 -0.5 bta-miR-1343-5p CALCOCO1 -0.64 bta-miR-2885 ADRA2B -0.5 bta-miR-1343-5p AC024940.1 -0.64 bta-miR-2885 KIAA0556 -0.5 bta-miR-1343-5p CD300LB -0.64 bta-miR-2885 PAX2 -0.5 bta-miR-1343-5p EFHD2 -0.64 bta-miR-450b CAMK2N1 -1.01 bta-miR-1343-5p CBX2 -0.64 bta-miR-450b C2orf74 -0.67 bta-miR-1343-5p KIF21B -0.64 bta-miR-450b RP11-169F17.1 -0.67 bta-miR-1343-5p KIAA0247 -0.63 bta-miR-450b CHMP2B -0.66 bta-miR-1343-5p KDM6B -0.63 bta-miR-450b RAB40A -0.64 bta-miR-1343-5p CYP46A1 -0.63 bta-miR-450b XRCC4 -0.63 bta-miR-1343-5p PPP2R4 -0.63 bta-miR-450b CT62 -0.62 bta-miR-1343-5p FOXI2 -0.63 bta-miR-450b RAET1L -0.62 bta-miR-1343-5p CRY2 -0.62 bta-miR-450b SPTSSB -0.6 bta-miR-1343-5p STX6 -0.62 bta-miR-450b UBD -0.59 bta-miR-1343-5p PITX1 -0.62 bta-miR-450b ZNF23 -0.57 bta-miR-1343-5p LINC00632 -0.62 bta-miR-450b CDKN2C -0.57 bta-miR-1343-5p RNF222 -0.62 bta-miR-450b PARK7 -0.55 bta-miR-1343-5p PPARGC1B -0.62 bta-miR-450b SLC17A6 -0.55 bta-miR-1343-5p PCYT1B -0.62 bta-miR-450b C15orf32 -0.54 bta-miR-1343-5p KIRREL -0.62 bta-miR-450b B2M -0.53 bta-miR-1343-5p EPB41 -0.62 bta-miR-450b TFRC -0.53 bta-miR-1343-5p MEA1 -0.62 bta-miR-450b TRO -0.51 bta-miR-1343-5p VSTM5 -0.61 bta-miR-760-5p CD300LB -1.29 bta-miR-1343-5p SERINC2 -0.61 bta-miR-760-5p GORASP1 -1.27 bta-miR-1343-5p NFAM1 -0.61 bta-miR-760-5p AC016722.1 -0.99 bta-miR-1343-5p TNS1 -0.61 bta-miR-760-5p CT62 -0.98 bta-miR-1343-5p PYGM -0.61 bta-miR-760-5p GLIPR2 -0.93 bta-miR-1343-5p TATDN3 -0.61 bta-miR-760-5p SLC4A11 -0.9

109

bta-miR-1343-5p CLEC3A -0.6 bta-miR-760-5p RHOU -0.87 bta-miR-1343-5p CTC-360G5.1 -0.6 bta-miR-760-5p OR5AU1 -0.85 bta-miR-1343-5p SHISA9 -0.6 bta-miR-760-5p PNKD -0.84 bta-miR-1343-5p ERGIC1 -0.6 bta-miR-760-5p OR1L3 -0.84 bta-miR-1343-5p SLC7A5 -0.6 bta-miR-760-5p DOCK1 -0.82 bta-miR-1343-5p SNCG -0.6 bta-miR-760-5p PRPH2 -0.81 bta-miR-1343-5p ALX4 -0.6 bta-miR-760-5p DMKN -0.78 bta-miR-1343-5p AGAP2-AS1 -0.59 bta-miR-760-5p DNAJC15 -0.77 bta-miR-1343-5p TNFSF12 -0.59 bta-miR-760-5p LRP11 -0.76 bta-miR-1343-5p RHOB -0.59 bta-miR-760-5p POSTN -0.75 bta-miR-1343-5p PPIE -0.59 bta-miR-760-5p CYHR1 -0.75 bta-miR-1343-5p KRTAP10-1 -0.59 bta-miR-760-5p SLC25A45 -0.74 bta-miR-1343-5p CHST1 -0.59 bta-miR-760-5p AC004899.1 -0.73 bta-miR-1343-5p PVRIG -0.59 bta-miR-760-5p TMED4 -0.73 bta-miR-1343-5p KCNMB1 -0.59 bta-miR-760-5p POLR2J2 -0.73 bta-miR-1343-5p MTAP -0.59 bta-miR-760-5p KCTD11 -0.72 bta-miR-1343-5p PPP2R5D -0.59 bta-miR-760-5p PAX8 -0.72 bta-miR-1343-5p PRRG3 -0.59 bta-miR-760-5p SLC26A10 -0.71 bta-miR-1343-5p KCNC3 -0.59 bta-miR-760-5p ZFP36 -0.7 bta-miR-1343-5p UCP2 -0.58 bta-miR-760-5p LTA -0.68 bta-miR-1343-5p TAL1 -0.58 bta-miR-760-5p DDIT4 -0.67 bta-miR-1343-5p AP001652.1 -0.58 bta-miR-760-5p ELAVL4 -0.67 bta-miR-1343-5p NUP98 -0.58 bta-miR-760-5p ZNF3 -0.67 bta-miR-1343-5p GATS -0.58 bta-miR-760-5p APH1A -0.67 bta-miR-1343-5p AC006946.15 -0.58 bta-miR-760-5p RPGRIP1 -0.67 bta-miR-1343-5p TMEM79 -0.58 bta-miR-760-5p C14orf132 -0.66 bta-miR-1343-5p SNX12 -0.58 bta-miR-760-5p FAM109A -0.66 bta-miR-1343-5p CELSR2 -0.57 bta-miR-760-5p KRTAP9-7 -0.65 bta-miR-1343-5p KRT17 -0.57 bta-miR-760-5p SEC13 -0.64 bta-miR-1343-5p NHP2 -0.57 bta-miR-760-5p PON2 -0.64 bta-miR-1343-5p DUSP28 -0.57 bta-miR-760-5p GEMIN2 -0.63 bta-miR-1343-5p RBMXL2 -0.57 bta-miR-760-5p TBC1D10C -0.63 bta-miR-1343-5p PTK2B -0.57 bta-miR-760-5p SLC25A48 -0.62 bta-miR-1343-5p PGS1 -0.57 bta-miR-760-5p WFDC2 -0.61 bta-miR-1343-5p CSNK2B-LY6G5B-1181 -0.57 bta-miR-760-5p ESM1 -0.59 bta-miR-1343-5p GABARAPL1 -0.57 bta-miR-760-5p ILF2 -0.58 bta-miR-1343-5p SYNGR1 -0.57 bta-miR-760-5p NAT9 -0.58 bta-miR-1343-5p OBP2B -0.57 bta-miR-760-5p ICAM2 -0.58 bta-miR-1343-5p LOXL1 -0.56 bta-miR-760-5p SLC29A2 -0.58 bta-miR-1343-5p HDAC10 -0.56 bta-miR-760-5p SYP -0.57

110

bta-miR-1343-5p EMC10 -0.56 bta-miR-760-5p ACR -0.57 bta-miR-1343-5p CEACAM1 -0.56 bta-miR-760-5p GDE1 -0.57 bta-miR-1343-5p CD7 -0.56 bta-miR-760-5p HPDL -0.57 bta-miR-1343-5p MPP2 -0.56 bta-miR-760-5p RP11-144F15.1 -0.56 bta-miR-1343-5p ELOVL1 -0.56 bta-miR-760-5p ARFRP1 -0.56 bta-miR-1343-5p LZTS2 -0.56 bta-miR-760-5p C16orf91 -0.56 bta-miR-1343-5p NYAP1 -0.56 bta-miR-760-5p CHP2 -0.56 bta-miR-1343-5p DCHS1 -0.56 bta-miR-760-5p BCL2L2 -0.56 bta-miR-1343-5p HRH3 -0.56 bta-miR-760-5p C12orf5 -0.56 bta-miR-1343-5p CLTCL1 -0.56 bta-miR-760-5p GOT1 -0.55 bta-miR-1343-5p NIPSNAP1 -0.55 bta-miR-760-5p PCBD2 -0.55 bta-miR-1343-5p GSC -0.55 bta-miR-760-5p DLK2 -0.55 bta-miR-1343-5p COL5A3 -0.55 bta-miR-760-5p CDK6 -0.54 bta-miR-1343-5p ADCYAP1R1 -0.55 bta-miR-760-5p CACNG8 -0.54 bta-miR-1343-5p NOTUM -0.55 bta-miR-760-5p TMEM169 -0.54 bta-miR-1343-5p RPP30 -0.55 bta-miR-760-5p AL450307.1 -0.54 bta-miR-1343-5p POLR2J2 -0.55 bta-miR-760-5p RAD54B -0.54 bta-miR-1343-5p PNMA6A -0.55 bta-miR-760-5p OR10A4 -0.53 bta-miR-1343-5p TNNT2 -0.55 bta-miR-760-5p ENDOV -0.53 bta-miR-1343-5p ONECUT3 -0.55 bta-miR-760-5p MRPS18B -0.53 bta-miR-1343-5p POLE3 -0.55 bta-miR-760-5p GIMAP6 -0.53 bta-miR-1343-5p ATXN2L -0.55 bta-miR-760-5p TWIST1 -0.53 bta-miR-1343-5p C10orf105 -0.55 bta-miR-760-5p C17orf64 -0.52 bta-miR-1343-5p TBC1D22B -0.55 bta-miR-760-5p DAPP1 -0.52 bta-miR-1343-5p PRSS42 -0.55 bta-miR-760-5p JPH2 -0.52 bta-miR-1343-5p DKFZP434O1614 -0.55 bta-miR-760-5p STMN4 -0.52 bta-miR-1343-5p FAM101A -0.55 bta-miR-760-5p SH2D2A -0.51 bta-miR-1343-5p PPP1R32 -0.55 bta-miR-760-5p MLANA -0.51 bta-miR-1343-5p STC1 -0.55 bta-miR-760-5p ARHGEF2 -0.5 bta-miR-1343-5p CYB5R3 -0.54 bta-miR-760-5p WNT9B -0.5 bta-miR-1343-5p CCDC142 -0.54 bta-miR-760-5p KDM1A -0.5 bta-miR-1343-5p C6orf223 -0.54 bta-miR-760-5p ASIC4 -0.5 bta-miR-1343-5p ATP6V0D1 -0.54 bta-miR-760-5p PRNT -0.5 bta-miR-1343-5p C2orf62 -0.54 bta-miR-760-5p RASSF2 -0.5 bta-miR-1343-5p PPP5C -0.54 bta-miR-760-5p OR4M2 -0.5 bta-miR-1343-5p IRGQ -0.54 bta-miR-760-5p BCL2L1 -0.5 bta-miR-1343-5p CCL22 -0.54 bta-miR-3613b HSD17B12 -1 bta-miR-1343-5p ZDHHC7 -0.54 bta-miR-3613b LONRF2 -1 bta-miR-1343-5p TRIM3 -0.54 bta-miR-3613b MFN2 -1 bta-miR-1343-5p WDTC1 -0.54 bta-miR-3613b RHOQ -1

111

bta-miR-1343-5p COLQ -0.54 bta-miR-3613b KPNA6 -1

bta-miR-1343-5p JUP -0.54 bta-miR-3613b USP38 -1

bta-miR-1343-5p LEPREL4 -0.54 bta-miR-3613b MRRF -1

bta-miR-1343-5p NTN3 -0.54 bta-miR-3613b CDK12 -1

bta-miR-1343-5p DCAKD -0.54 bta-miR-3613b GAS7 -1

bta-miR-1343-5p C1orf134 -0.53 bta-miR-3613b TMBIM6 -1

bta-miR-1343-5p ELOVL2 -0.53 bta-miR-3613b USP42 -1

bta-miR-1343-5p PRKCG -0.53 bta-miR-3613b KDELR2 -1

bta-miR-1343-5p FOXM1 -0.53 bta-miR-3613b ZFP37 -1

bta-miR-1343-5p RPH3A -0.53 bta-miR-3613b CCDC132 -1

bta-miR-1343-5p TMEM151B -0.53 bta-miR-3613b ZIC5 -1

bta-miR-1343-5p R3HCC1 -0.53 bta-miR-3613b SNX30 -1

bta-miR-1343-5p VPS53 -0.53 bta-miR-3613b SIK2 -1

bta-miR-1343-5p CCDC149 -0.53 bta-miR-3613b IKZF4 -1

bta-miR-1343-5p LDLRAD2 -0.53 bta-miR-3613b ARHGAP35 -1

bta-miR-1343-5p GABBR2 -0.53 bta-miR-3613b FAM126A -1

bta-miR-1343-5p RHBDF2 -0.53 bta-miR-3613b GOLT1B -1

bta-miR-1343-5p CX3CL1 -0.52 bta-miR-3613b CEP128 -1

bta-miR-1343-5p LBH -0.52 bta-miR-3613b GLE1 -1

bta-miR-1343-5p ICAM1 -0.52 bta-miR-3613b AGAP2 -1

bta-miR-1343-5p ABHD17A -0.52 bta-miR-3613b RBM25 -1

bta-miR-1343-5p TBC1D16 -0.52 bta-miR-3613b LGI2 -1

bta-miR-1343-5p GDF11 -0.52 bta-miR-3613b GAPVD1 -1

bta-miR-1343-5p CNOT3 -0.52 bta-miR-3613b UBR2 -1

bta-miR-1343-5p GINS2 -0.52 bta-miR-3613b CPD -1

bta-miR-1343-5p ANGPTL6 -0.52 bta-miR-3613b DEDD -1

bta-miR-1343-5p FBXO31 -0.52 bta-miR-3613b CELF2 -0.92

bta-miR-3613b FGFBP2 -0.54

S. Table 8. Predicted mRNA targets of miRNAs differentially expressed between blastocyst SG SM vs. blastocyst FG SM.

miRNAs Genes Cumulative miRNAs Genes Cumulative Context Score Context Score

bta-miR-2296 AK2 -0.59 bta-miR-760-5p TMEM169 -0.54

bta-miR-2296 AP001579.1 -0.57 bta-miR-760-5p TWIST1 -0.53

bta-miR-2296 ASB16 -0.57 bta-miR-760-5p WFDC2 -0.61

bta-miR-2296 BARHL2 -0.56 bta-miR-760-5p WNT9B -0.5

bta-miR-2296 BBS7 -0.54 bta-miR-760-5p ZFP36 -0.7

bta-miR-2296 C3orf65 -0.54 bta-miR-760-5p ZNF3 -0.67

112

bta-miR-2296 C7orf13 -0.53 bta-miR-17-5p GPR6 -0.89 bta-miR-2296 C7orf76 -0.79 bta-miR-17-5p PDCD1LG2 -0.88 bta-miR-2296 C9orf24 -0.74 bta-miR-17-5p CCL1 -0.79 bta-miR-2296 C9orf78 -0.55 bta-miR-17-5p PKD2 -0.73 bta-miR-2296 CCDC77 -0.5 bta-miR-17-5p HAUS8 -0.69 bta-miR-2296 CD226 -0.64 bta-miR-17-5p PTHLH -0.64 bta-miR-2296 CHSY3 -0.51 bta-miR-17-5p IRF9 -0.63 bta-miR-2296 CLDN22 -0.86 bta-miR-17-5p DERL2 -0.6 bta-miR-2296 CYP3A43 -0.51 bta-miR-17-5p RHOC -0.57 bta-miR-2296 DEFB127 -0.76 bta-miR-17-5p PLEKHA3 -0.53 bta-miR-2296 DEPDC4 -0.63 bta-miR-17-5p C7orf43 -0.53 bta-miR-2296 EFCAB10 -0.56 bta-miR-17-5p IL6ST -0.52 bta-miR-2296 ERCC8 -0.66 bta-miR-17-5p AGFG2 -0.52 bta-miR-2296 FABP4 -0.66 bta-miR-17-5p SEMA4B -0.51 bta-miR-2296 GPA33 -0.6 bta-miR-17-5p RASL11B -0.5 bta-miR-2296 HDGFRP3 -0.64 bta-miR-23a ZNF655 -1.15 bta-miR-2296 KANSL1L -0.63 bta-miR-23a ACVR1C -0.92 bta-miR-2296 KCNC2 -0.57 bta-miR-23a TFRC -0.77 bta-miR-2296 KCNIP4 -0.51 bta-miR-23a PNRC2 -0.76 bta-miR-2296 KIFC1 -0.57 bta-miR-23a PKP4 -0.75 bta-miR-2296 LST1 -0.66 bta-miR-23a SNRPC -0.7 bta-miR-2296 OSBPL10 -0.55 bta-miR-23a PDE4B -0.65 bta-miR-2296 PAX4 -0.52 bta-miR-23a PFDN6 -0.62 bta-miR-2296 PCED1B -0.74 bta-miR-23a ZNF23 -0.59 bta-miR-2296 PIM2 -0.76 bta-miR-23a YES1 -0.59 bta-miR-2296 PRAMEF18 -0.6 bta-miR-23a TOP1 -0.59 bta-miR-2296 PTMA -0.6 bta-miR-23a CCDC82 -0.58 bta-miR-2296 RP11-181C3.1 -0.51 bta-miR-23a TFPI2 -0.58 bta-miR-2296 RP11-404P21.8 -0.5 bta-miR-23a DHFR -0.58 bta-miR-2296 RP11-422N16.3 -0.76 bta-miR-23a PNMA1 -0.56 bta-miR-2296 SKP1 -0.57 bta-miR-23a LRAT -0.56 bta-miR-2296 TCF7L2 -0.69 bta-miR-23a RALYL -0.56 bta-miR-2296 TDRD10 -0.81 bta-miR-23a PDE7A -0.55 bta-miR-2296 TIGD3 -0.52 bta-miR-23a C10orf118 -0.54 bta-miR-2296 TMEM51 -0.78 bta-miR-23a SETD8 -0.54 bta-miR-2296 TMEM66 -0.53 bta-miR-23a IGSF8 -0.53 bta-miR-2296 TSNAX -0.5 bta-miR-23a PKIA -0.53 bta-miR-2296 UNC45A -0.61 bta-miR-23a B3GNT1 -0.53 bta-miR-2296 WNT2 -0.69 bta-miR-23a MYL12B -0.53 bta-miR-2296 ZNF791 -0.95 bta-miR-23a HMGB2 -0.51

113

bta-miR-450b B2M -0.53 bta-miR-23a SATB1 -0.51 bta-miR-450b C15orf32 -0.54 bta-miR-23a ANKHD1 -0.5 bta-miR-450b C2orf74 -0.67 bta-miR-23b-3p SS18L2 -1.09 bta-miR-450b CAMK2N1 -1.01 bta-miR-23b-3p ELF5 -0.81 bta-miR-450b CDKN2C -0.57 bta-miR-23b-3p ERBB2IP -0.59 bta-miR-450b CHMP2B -0.66 bta-miR-23b-3p WBP2 -0.55 bta-miR-450b CT62 -0.62 bta-miR-23b-3p PPM1D -0.54 bta-miR-450b PARK7 -0.55 bta-miR-23b-3p AUH -0.53 bta-miR-450b RAB40A -0.64 bta-miR-23b-3p SLC1A1 -0.52 bta-miR-450b RAET1L -0.62 bta-miR-23b-3p ZIC5 -0.5 bta-miR-450b RP11-169F17.1 -0.67 bta-miR-23b-3p RAB39B -0.5 bta-miR-450b SLC17A6 -0.55 bta-miR-24-3p STRADB -0.85 bta-miR-450b SPTSSB -0.6 bta-miR-24-3p TCF7 -0.84 bta-miR-450b TFRC -0.53 bta-miR-24-3p C12orf43 -0.84 bta-miR-450b TRO -0.51 bta-miR-24-3p ENTPD6 -0.84 bta-miR-450b UBD -0.59 bta-miR-24-3p LSM10 -0.79 bta-miR-450b XRCC4 -0.63 bta-miR-24-3p SNN -0.78 bta-miR-450b ZNF23 -0.57 bta-miR-24-3p GBA2 -0.76 bta-miR-6535 AC012215.1 -0.51 bta-miR-24-3p SLCO2B1 -0.75 bta-miR-6535 AC093802.1 -0.81 bta-miR-24-3p BCL2L11 -0.74 bta-miR-6535 ACOT8 -0.7 bta-miR-24-3p FAM78B -0.72 bta-miR-6535 ADRA2A -0.83 bta-miR-24-3p DNAJB12 -0.67 bta-miR-6535 AGPAT3 -0.55 bta-miR-24-3p TSPAN14 -0.67 bta-miR-6535 AL117190.3 -0.54 bta-miR-24-3p LMBR1L -0.67 bta-miR-6535 ALKBH5 -0.52 bta-miR-24-3p KCNK2 -0.66 bta-miR-6535 AMOT -0.55 bta-miR-24-3p FASLG -0.65 bta-miR-6535 AMOTL2 -0.62 bta-miR-24-3p FST -0.65 bta-miR-6535 ANKRD52 -0.94 bta-miR-24-3p ALAD -0.62 bta-miR-6535 AP1M2 -0.85 bta-miR-24-3p RAB3IL1 -0.61 bta-miR-6535 APLN -0.75 bta-miR-24-3p NEFM -0.61 bta-miR-6535 APOC4 -0.65 bta-miR-24-3p CCDC58 -0.61 bta-miR-6535 ARF3 -0.96 bta-miR-24-3p RAP1B -0.6 bta-miR-6535 ARL2 -0.58 bta-miR-24-3p WNT8B -0.59 bta-miR-6535 ARNT2 -0.52 bta-miR-24-3p SCML1 -0.59 bta-miR-6535 ARRB1 -0.67 bta-miR-24-3p MIDN -0.59 bta-miR-6535 ASNA1 -0.59 bta-miR-24-3p ABCB9 -0.59 bta-miR-6535 ATCAY -0.57 bta-miR-24-3p VAMP5 -0.58 bta-miR-6535 ATP6V0A1 -0.55 bta-miR-24-3p ZNF697 -0.58 bta-miR-6535 ATP6V0E2 -0.81 bta-miR-24-3p UBALD2 -0.57 bta-miR-6535 ATXN7L3 -0.9 bta-miR-24-3p SIRPA -0.57

114

bta-miR-6535 BAIAP2L2 -0.55 bta-miR-24-3p IFNG -0.56 bta-miR-6535 BARHL1 -0.59 bta-miR-24-3p BBC3 -0.56 bta-miR-6535 BCAT2 -0.71 bta-miR-24-3p C10orf62 -0.56 bta-miR-6535 BCL2L2 -0.81 bta-miR-24-3p ATG4A -0.56 bta-miR-6535 BEST3 -0.58 bta-miR-24-3p LIMD2 -0.54 bta-miR-6535 C10orf111 -0.69 bta-miR-24-3p HNF1B -0.53 bta-miR-6535 C11orf68 -0.53 bta-miR-24-3p AC005003.1 -0.53 bta-miR-6535 C19orf35 -0.68 bta-miR-24-3p SLC25A39 -0.53 bta-miR-6535 C1orf134 -0.53 bta-miR-24-3p TRIM55 -0.53 bta-miR-6535 C1orf172 -0.57 bta-miR-24-3p RAP1A -0.53 bta-miR-6535 C1QL1 -0.52 bta-miR-24-3p TOP1 -0.52 bta-miR-6535 C20orf27 -0.54 bta-miR-24-3p CDKN1B -0.52 bta-miR-6535 C20orf96 -0.64 bta-miR-24-3p CLLU1 -0.52 bta-miR-6535 C21orf67 -0.59 bta-miR-24-3p KCTD21 -0.52 bta-miR-6535 C2orf91 -0.78 bta-miR-24-3p RNF138 -0.52 bta-miR-6535 CACNB1 -0.69 bta-miR-24-3p FAM45A -0.51 bta-miR-6535 CACNG7 -0.95 bta-miR-24-3p CAMK2B -0.51 bta-miR-6535 CALCOCO1 -0.78 bta-miR-24-3p B3GNT5 -0.51 bta-miR-6535 CALM2 -0.69 bta-miR-24-3p RNF115 -0.51 bta-miR-6535 CAMK2A -0.55 bta-miR-24-3p C8orf58 -0.51 bta-miR-6535 CD300LB -0.64 bta-miR-24-3p TSC22D2 -0.5 bta-miR-6535 CD300LG -0.55 bta-miR-24-3p STC2 -0.5 bta-miR-6535 CDCA3 -0.72 bta-miR-24-3p C16orf59 -0.5 bta-miR-6535 CDKN1A -0.6 bta-miR-24-3p SNTB1 -0.5 bta-miR-6535 CHCHD5 -0.51 bta-miR-24-3p RAB4B -0.5 bta-miR-6535 CHRNA7 -0.51 bta-miR-2402 C1orf134 -0.98 bta-miR-6535 CHST1 -0.52 bta-miR-2402 ZMAT3 -0.87 bta-miR-6535 CHST3 -0.57 bta-miR-2402 GDI2 -0.86 bta-miR-6535 CLEC3A -0.54 bta-miR-2402 MRPL32 -0.84 bta-miR-6535 CLSTN3 -0.52 bta-miR-2402 AC117834.1 -0.81 bta-miR-6535 CNN1 -0.54 bta-miR-2402 C19orf53 -0.77 bta-miR-6535 CNTFR -0.51 bta-miR-2402 GABRA2 -0.74 bta-miR-6535 CNTROB -0.56 bta-miR-2402 UBE2V2 -0.73 bta-miR-6535 CPLX2 -0.51 bta-miR-2402 AC069547.1 -0.69 bta-miR-6535 CRLF1 -0.68 bta-miR-2402 TMEM239 -0.65 bta-miR-6535 CSDC2 -0.75 bta-miR-2402 PLN -0.64 bta-miR-6535 CST9 -0.51 bta-miR-2402 LYPLA1 -0.62 bta-miR-6535 CTNNBIP1 -0.55 bta-miR-2402 TMEM182 -0.62 bta-miR-6535 CTNND1 -0.54 bta-miR-2402 MPPED2 -0.6 bta-miR-6535 CTXN1 -0.56 bta-miR-2402 KCMF1 -0.59

115

bta-miR-6535 CXorf24 -0.59 bta-miR-2402 PRIM2 -0.59 bta-miR-6535 DAGLA -0.59 bta-miR-2402 SFRP5 -0.59 bta-miR-6535 DCX -0.55 bta-miR-2402 ALOX15 -0.59 bta-miR-6535 DCXR -0.5 bta-miR-2402 CLRN3 -0.58 bta-miR-6535 DDAH1 -0.61 bta-miR-2402 FLI1 -0.58 bta-miR-6535 DIRAS2 -0.65 bta-miR-2402 LDOC1L -0.57 bta-miR-6535 DKFZP761J1410 -0.75 bta-miR-2402 RINT1 -0.56 bta-miR-6535 DPF2 -0.59 bta-miR-2402 MORC1 -0.54 bta-miR-6535 DPP10 -0.73 bta-miR-2402 IL33 -0.54 bta-miR-6535 DTX3 -0.85 bta-miR-2402 CYTH3 -0.54 bta-miR-6535 EFNB1 -1.01 bta-miR-2402 CGNL1 -0.53 bta-miR-6535 EFS -0.51 bta-miR-2402 PLP1 -0.53 bta-miR-6535 EGR3 -0.58 bta-miR-2402 OLA1 -0.53 bta-miR-6535 ELAVL3 -0.8 bta-miR-2402 CYP2C18 -0.53 bta-miR-6535 ELN -0.94 bta-miR-2402 FBXO38 -0.52 bta-miR-6535 ELOVL4 -0.53 bta-miR-2402 UHMK1 -0.52 bta-miR-6535 EPHA8 -0.59 bta-miR-2402 PPM1A -0.52 bta-miR-6535 ETV1 -0.52 bta-miR-2402 GALNT3 -0.52 bta-miR-6535 ETV6 -0.83 bta-miR-2402 PAPPA-AS1 -0.52 bta-miR-6535 FAM83F -0.61 bta-miR-2402 KLC4 -0.51 bta-miR-6535 FBRS -0.88 bta-miR-2402 BNIP2 -0.51 bta-miR-6535 FBXL19 -0.56 bta-miR-2402 FAM64A -0.51 bta-miR-6535 FGF14 -0.69 bta-miR-2402 PLD6 -0.51 bta-miR-6535 FOSB -0.99 bta-miR-2402 NPY2R -0.51 bta-miR-6535 FOXL2 -0.54 bta-miR-2402 SLAMF8 -0.5 bta-miR-6535 FOXN2 -0.53 bta-miR-2402 RP11-192H23.4 -0.5 bta-miR-6535 FXYD4 -0.53 bta-miR-2402 TAF13 -0.5 bta-miR-6535 FXYD7 -0.6 bta-miR-2898 PXK -1.53 bta-miR-6535 FZD4 -0.72 bta-miR-2898 CAPN6 -1.25 bta-miR-6535 GAL3ST1 -0.82 bta-miR-2898 C21orf67 -1.22 bta-miR-6535 GAS8 -0.95 bta-miR-2898 CTF1 -1.15 bta-miR-6535 GATS -0.75 bta-miR-2898 IGF2R -1.1 bta-miR-6535 GDF5 -0.52 bta-miR-2898 C15orf32 -1.09 bta-miR-6535 GIGYF1 -0.61 bta-miR-2898 SNURF -1.04 bta-miR-6535 GLO1 -0.69 bta-miR-2898 PRR18 -1.04 bta-miR-6535 GLYR1 -0.63 bta-miR-2898 PRB3 -1.02 bta-miR-6535 GPATCH2L -0.58 bta-miR-2898 KRTAP1-3 -1 bta-miR-6535 GPHA2 -0.72 bta-miR-2898 C17orf72 -1 bta-miR-6535 GPR17 -0.53 bta-miR-2898 GPR173 -0.99 bta-miR-6535 HM13 -0.88 bta-miR-2898 KRTAP21-2 -0.98

116

bta-miR-6535 HMGA1 -0.81 bta-miR-2898 CABP2 -0.93 bta-miR-6535 HOXB1 -0.83 bta-miR-2898 UBE2L3 -0.9 bta-miR-6535 HOXB3 -1.21 bta-miR-2898 TMEM30C -0.86 bta-miR-6535 HOXC4 -0.63 bta-miR-2898 AGFG2 -0.85 bta-miR-6535 HVCN1 -0.68 bta-miR-2898 CBLN3 -0.83 bta-miR-6535 IER3 -0.61 bta-miR-2898 ONECUT3 -0.82 bta-miR-6535 IGDCC4 -0.54 bta-miR-2898 LELP1 -0.8 bta-miR-6535 IGF2 -0.65 bta-miR-2898 PHLDA2 -0.79 bta-miR-6535 IL1RN -0.98 bta-miR-2898 SYNDIG1L -0.78 bta-miR-6535 IL2RG -0.8 bta-miR-2898 TXNL4A -0.76 bta-miR-6535 ITFG2 -0.5 bta-miR-2898 CCL11 -0.76 bta-miR-6535 ITIH5 -0.5 bta-miR-2898 CCDC86 -0.75 bta-miR-6535 ITPKB -0.79 bta-miR-2898 ASCL4 -0.73 bta-miR-6535 JMJD7-PLA2G4B -0.63 bta-miR-2898 C6orf25 -0.73 bta-miR-6535 JPH4 -0.86 bta-miR-2898 KRTAP4-8 -0.73 bta-miR-6535 KCNAB2 -0.61 bta-miR-2898 COMMD7 -0.72 bta-miR-6535 KCNC1 -0.5 bta-miR-2898 RP11-3B7.1 -0.72 bta-miR-6535 KCND3 -0.5 bta-miR-2898 PRSS37 -0.72 bta-miR-6535 KCNK3 -0.56 bta-miR-2898 FKBP9 -0.72 bta-miR-6535 KIAA0040 -0.72 bta-miR-2898 PIANP -0.71 bta-miR-6535 KIAA0391 -0.57 bta-miR-2898 GOLGA7B -0.71 bta-miR-6535 KIF21B -1.13 bta-miR-2898 ZNF488 -0.71 bta-miR-6535 KLF12 -0.75 bta-miR-2898 NICN1 -0.7 bta-miR-6535 KLF16 -0.57 bta-miR-2898 IGFL1 -0.7 bta-miR-6535 KLK6 -0.67 bta-miR-2898 SHISA7 -0.69 bta-miR-6535 KMT2D -0.64 bta-miR-2898 TMEM213 -0.69 bta-miR-6535 KRT16 -0.54 bta-miR-2898 RGS8 -0.69 bta-miR-6535 KSR2 -1.23 bta-miR-2898 NRSN2 -0.68 bta-miR-6535 LAMTOR1 -0.6 bta-miR-2898 GPX3 -0.68 bta-miR-6535 LASP1 -0.99 bta-miR-2898 DBNDD2 -0.66 bta-miR-6535 LAT -0.75 bta-miR-2898 C11orf82 -0.66 bta-miR-6535 LCE2B -0.53 bta-miR-2898 CDK6 -0.66 bta-miR-6535 LCE2D -0.9 bta-miR-2898 MRPS18B -0.65 bta-miR-6535 LEFTY2 -0.59 bta-miR-2898 PABPC1L -0.65 bta-miR-6535 LEPROT -0.74 bta-miR-2898 PRR24 -0.65 bta-miR-6535 LHB -0.54 bta-miR-2898 SCGN -0.64 bta-miR-6535 LIF -0.5 bta-miR-2898 NDUFAF2 -0.64 bta-miR-6535 LIMD2 -0.66 bta-miR-2898 ZBED3 -0.64 bta-miR-6535 LMTK3 -0.5 bta-miR-2898 GAPDH -0.64 bta-miR-6535 LRFN1 -0.61 bta-miR-2898 AL161915.1 -0.64

117

bta-miR-6535 LY6E -0.66 bta-miR-2898 AKR1B15 -0.63 bta-miR-6535 MAP2K7 -0.91 bta-miR-2898 DKKL1 -0.63 bta-miR-6535 MARCKSL1 -0.5 bta-miR-2898 PCGF2 -0.62 bta-miR-6535 MARK2 -0.54 bta-miR-2898 SIX2 -0.62 bta-miR-6535 MAX -0.59 bta-miR-2898 MRAP -0.62 bta-miR-6535 MAZ -0.61 bta-miR-2898 AKR1B10 -0.62 bta-miR-6535 MDGA1 -0.62 bta-miR-2898 TEX35 -0.61 bta-miR-6535 MED30 -0.5 bta-miR-2898 MYF5 -0.61 bta-miR-6535 METTL3 -0.53 bta-miR-2898 MOGAT2 -0.61 bta-miR-6535 MFAP2 -0.5 bta-miR-2898 DRAXIN -0.61 bta-miR-6535 MLN -0.53 bta-miR-2898 RBFOX3 -0.61 bta-miR-6535 MN1 -0.9 bta-miR-2898 BSND -0.61 bta-miR-6535 MON1A -0.57 bta-miR-2898 BCL2L1 -0.6 bta-miR-6535 MSI1 -0.88 bta-miR-2898 SLC8A2 -0.6 bta-miR-6535 MVB12B -0.53 bta-miR-2898 C5orf51 -0.6 bta-miR-6535 NAB2 -0.69 bta-miR-2898 CTPS2 -0.6 bta-miR-6535 NCR3 -0.86 bta-miR-2898 RSPO4 -0.6 bta-miR-6535 NDP -0.57 bta-miR-2898 PROK1 -0.59 bta-miR-6535 NEUROD2 -0.53 bta-miR-2898 SH2D4B -0.59 bta-miR-6535 NFAM1 -0.53 bta-miR-2898 KHDRBS3 -0.59 bta-miR-6535 NFATC2 -0.56 bta-miR-2898 HSPB8 -0.59 bta-miR-6535 NFIC -0.84 bta-miR-2898 TUBB4A -0.58 bta-miR-6535 NOTCH3 -0.73 bta-miR-2898 AC026310.1 -0.58 bta-miR-6535 NOVA2 -0.93 bta-miR-2898 SHISA6 -0.58 bta-miR-6535 NPTX1 -0.68 bta-miR-2898 FAM193B -0.58 bta-miR-6535 NPTXR -0.88 bta-miR-2898 TMEM178B -0.58 bta-miR-6535 NRG1 -0.7 bta-miR-2898 HOXC12 -0.58 bta-miR-6535 NXPH2 -0.67 bta-miR-2898 SLC10A3 -0.57 bta-miR-6535 ONECUT3 -0.87 bta-miR-2898 DLX6 -0.57 bta-miR-6535 OST4 -0.5 bta-miR-2898 AC019171.1 -0.57 bta-miR-6535 OXT -0.56 bta-miR-2898 CALN1 -0.57 bta-miR-6535 PACS1 -0.56 bta-miR-2898 S100A14 -0.56 bta-miR-6535 PACSIN1 -0.58 bta-miR-2898 SCN3B -0.56 bta-miR-6535 PAFAH1B2 -0.61 bta-miR-2898 VAMP2 -0.56 bta-miR-6535 PCBP4 -0.54 bta-miR-2898 HDGFRP3 -0.56 bta-miR-6535 PCDH1 -1.02 bta-miR-2898 TLCD1 -0.56 bta-miR-6535 PCSK2 -0.72 bta-miR-2898 CLPS -0.56 bta-miR-6535 PHF1 -0.54 bta-miR-2898 SASH3 -0.56 bta-miR-6535 PIANP -0.72 bta-miR-2898 ISM1 -0.56 bta-miR-6535 PLA2G6 -0.7 bta-miR-2898 C15orf57 -0.55

118

bta-miR-6535 PNMA6A -0.63 bta-miR-2898 DAB2IP -0.55 bta-miR-6535 POU2F2 -0.55 bta-miR-2898 RAB5C -0.55 bta-miR-6535 POU3F1 -0.6 bta-miR-2898 PURA -0.55 bta-miR-6535 PPARD -0.56 bta-miR-2898 SNAPC3 -0.55 bta-miR-6535 PPDPF -0.77 bta-miR-2898 IQCF3 -0.55 bta-miR-6535 PPP1R18 -0.87 bta-miR-2898 SRF -0.55 bta-miR-6535 PRKAG1 -0.5 bta-miR-2898 MC5R -0.54 bta-miR-6535 PRKCB -0.56 bta-miR-2898 ZNF213 -0.54 bta-miR-6535 PTGER4 -0.67 bta-miR-2898 CD247 -0.54 bta-miR-6535 PVRL1 -0.92 bta-miR-2898 TBC1D14 -0.54 bta-miR-6535 RAB11FIP5 -0.63 bta-miR-2898 FUK -0.54 bta-miR-6535 RAB15 -0.61 bta-miR-2898 XXcos-LUCA11.5 -0.54 bta-miR-6535 RAB35 -0.57 bta-miR-2898 ACADS -0.54 bta-miR-6535 RAD54B -0.57 bta-miR-2898 SNTA1 -0.54 bta-miR-6535 RARG -0.58 bta-miR-2898 TBL1X -0.54 bta-miR-6535 RASL10B -0.61 bta-miR-2898 CCDC25 -0.54 bta-miR-6535 REEP2 -0.65 bta-miR-2898 DEFB132 -0.54 bta-miR-6535 RHOB -0.67 bta-miR-2898 FAM155B -0.53 bta-miR-6535 RHOG -0.73 bta-miR-2898 TSPAN18 -0.53 bta-miR-6535 RNF141 -0.58 bta-miR-2898 EFNA3 -0.52 bta-miR-6535 RNF39 -0.51 bta-miR-2898 HSPB6 -0.52 bta-miR-6535 ROGDI -0.87 bta-miR-2898 ARMC10 -0.52 bta-miR-6535 RP11-111M22.2 -0.52 bta-miR-2898 CIR1 -0.52 bta-miR-6535 RP11-159G9.5 -0.63 bta-miR-2898 HMOX1 -0.52 bta-miR-6535 RP11-247C2.2 -0.57 bta-miR-2898 SARS -0.52 bta-miR-6535 RP6-24A23.6 -1.19 bta-miR-2898 AC005609.1 -0.52 bta-miR-6535 RPS15 -0.52 bta-miR-2898 EIF3L -0.52 bta-miR-6535 RPS6KA4 -0.83 bta-miR-2898 ACTN1 -0.52 bta-miR-6535 RPS9 -0.75 bta-miR-2898 LDOC1 -0.52 bta-miR-6535 RTBDN -0.77 bta-miR-2898 PCDH11X -0.52 bta-miR-6535 RUNX3 -0.92 bta-miR-2898 NAPA -0.52 bta-miR-6535 SAMD10 -0.68 bta-miR-2898 PHYHIP -0.51 bta-miR-6535 SAMD12 -0.5 bta-miR-2898 PPM1N -0.51 bta-miR-6535 SBK1 -0.9 bta-miR-2898 PYDC1 -0.51 bta-miR-6535 SCAMP5 -0.66 bta-miR-2898 TNS1 -0.51 bta-miR-6535 SCGN -0.72 bta-miR-2898 GBX2 -0.51 bta-miR-6535 SCRT2 -0.9 bta-miR-2898 PTMA -0.51 bta-miR-6535 SEPN1 -0.51 bta-miR-2898 MARCH9 -0.51 bta-miR-6535 SEPT6 -0.53 bta-miR-2898 AKR1E2 -0.51 bta-miR-6535 SF3B3 -0.54 bta-miR-2898 RANBP10 -0.51

119

bta-miR-6535 SHISA6 -0.55 bta-miR-2898 LHCGR -0.51 bta-miR-6535 SLC11A1 -0.63 bta-miR-2898 CNTD1 -0.5 bta-miR-6535 SLC25A23 -0.84 bta-miR-2898 CLPSL1 -0.5 bta-miR-6535 SLC31A2 -0.65 bta-miR-2898 NTM -0.5 bta-miR-6535 SLC39A13 -0.58 bta-miR-320a ST7-OT4 -0.73 bta-miR-6535 SLC7A1 -0.51 bta-miR-320a IFT27 -0.72 bta-miR-6535 SLC7A8 -0.64 bta-miR-320a PCDHA1 -0.6 bta-miR-6535 SLFN13 -0.56 bta-miR-320a PBX3 -0.55 bta-miR-6535 SMG6 -0.71 bta-miR-320a ST8SIA4 -0.55 bta-miR-6535 SNX33 -0.57 bta-miR-320a VKORC1L1 -0.54 bta-miR-6535 SOCS3 -0.65 bta-miR-320a TFRC -0.52 bta-miR-6535 SOX12 -0.81 bta-miR-320a RP11-160N1.10 -0.5 bta-miR-6535 SP2 -0.52 bta-miR-320a GTPBP8 -0.5 bta-miR-6535 SRF -0.73 bta-miR-320a CTPS1 -0.5 bta-miR-6535 ST3GAL5 -0.55 bta-miR-615 SOD3 -1.28 bta-miR-6535 STAT3 -0.56 bta-miR-615 HSPB7 -1.19 bta-miR-6535 STK35 -1.31 bta-miR-615 C4orf6 -1.15 bta-miR-6535 STX1B -0.51 bta-miR-615 SPRED3 -1.12 bta-miR-6535 STXBP1 -0.54 bta-miR-615 SLC25A23 -1.05 bta-miR-6535 SYNGAP1 -1.1 bta-miR-615 FOXP3 -1.04 bta-miR-6535 SYNGR1 -1.14 bta-miR-615 SRRM4 -0.99 bta-miR-6535 SYNGR4 -0.67 bta-miR-615 ASTN2 -0.94 bta-miR-6535 SYT5 -0.52 bta-miR-615 CERS1 -0.9 bta-miR-6535 TAF6 -0.7 bta-miR-615 DERL3 -0.86 bta-miR-6535 TBKBP1 -0.51 bta-miR-615 SHMT1 -0.86 bta-miR-6535 TBR1 -0.58 bta-miR-615 GIPC3 -0.86 bta-miR-6535 TCL1A -0.64 bta-miR-615 TSSK6 -0.85 bta-miR-6535 THRA -1.37 bta-miR-615 DGCR2 -0.81 bta-miR-6535 TMEM109 -0.5 bta-miR-615 LST1 -0.81 bta-miR-6535 TMEM127 -0.53 bta-miR-615 UPK3A -0.81 bta-miR-6535 TMEM132E -0.56 bta-miR-615 CTB-54O9.9 -0.8 bta-miR-6535 TMEM184B -0.51 bta-miR-615 LRRK1 -0.79 bta-miR-6535 TMEM229B -1.4 bta-miR-615 RP11-429E11.3 -0.78 bta-miR-6535 TMEM59L -0.64 bta-miR-615 PARP14 -0.78 bta-miR-6535 TMEM95 -0.54 bta-miR-615 CENPB -0.78 bta-miR-6535 TNFSF12 -0.58 bta-miR-615 CD3E -0.76 bta-miR-6535 TP53INP2 -0.57 bta-miR-615 CDH4 -0.76 bta-miR-6535 TPBGL -0.8 bta-miR-615 AC016722.1 -0.76 bta-miR-6535 TPPP3 -0.5 bta-miR-615 ADM2 -0.76 bta-miR-6535 TRIM46 -0.6 bta-miR-615 RPE -0.75

120

bta-miR-6535 TRIM7 -0.7 bta-miR-615 HSF1 -0.74 bta-miR-6535 TSPAN11 -0.77 bta-miR-615 LENEP -0.74 bta-miR-6535 TSPAN18 -0.74 bta-miR-615 NEDD8 -0.73 bta-miR-6535 TSPYL2 -0.88 bta-miR-615 IKBKB -0.72 bta-miR-6535 U2AF2 -0.6 bta-miR-615 HSD11B2 -0.72 bta-miR-6535 UBA1 -0.55 bta-miR-615 CTD-2207O23.12 -0.71 bta-miR-6535 UBALD1 -0.76 bta-miR-615 DMPK -0.71 bta-miR-6535 UBL5 -0.52 bta-miR-615 ENPP7 -0.71 bta-miR-6535 UBTF -0.72 bta-miR-615 CD19 -0.7 bta-miR-6535 UNC119B -0.53 bta-miR-615 CTDSP1 -0.7 bta-miR-6535 UNC13A -1.17 bta-miR-615 ANKRD63 -0.7 bta-miR-6535 VAMP1 -0.72 bta-miR-615 FGFBP2 -0.69 bta-miR-6535 VPS52 -0.66 bta-miR-615 AL138847.1 -0.68 bta-miR-6535 VPS72 -0.74 bta-miR-615 ELP5 -0.68 bta-miR-6535 VSTM2L -0.69 bta-miR-615 RHOC -0.67 bta-miR-6535 VSX2 -0.69 bta-miR-615 CTC1 -0.67 bta-miR-6535 VTI1A -0.53 bta-miR-615 BEAN1 -0.67 bta-miR-6535 WNT5A -0.66 bta-miR-615 PRKAR1B -0.67 bta-miR-6535 WWOX -0.57 bta-miR-615 LTB -0.67 bta-miR-6535 XKR7 -0.77 bta-miR-615 LILRB3 -0.67 bta-miR-6535 YBX2 -0.53 bta-miR-615 LAMA5 -0.67 bta-miR-6535 ZBTB16 -0.56 bta-miR-615 RERG -0.66 bta-miR-6535 ZBTB37 -0.67 bta-miR-615 HINT2 -0.66 bta-miR-6535 ZBTB7A -1.48 bta-miR-615 AC004899.1 -0.66 bta-miR-6535 ZC4H2 -0.66 bta-miR-615 P2RX6 -0.66 bta-miR-6535 ZDHHC6 -0.56 bta-miR-615 EDA -0.66 bta-miR-6535 ZFP91 -0.54 bta-miR-615 MAZ -0.66 bta-miR-6535 ZFR -0.57 bta-miR-615 ALDH16A1 -0.66 bta-miR-6535 ZNF30 -0.55 bta-miR-615 URAD -0.66 bta-miR-6535 ZNF385A -1.06 bta-miR-615 BHLHA15 -0.66 bta-miR-6535 ZNF395 -0.57 bta-miR-615 MSLN -0.66 bta-miR-6535 ZNF488 -0.53 bta-miR-615 PACSIN1 -0.66 bta-miR-6535 ZNF512 -0.77 bta-miR-615 ARIH2 -0.64 bta-miR-6535 ZNF557 -0.58 bta-miR-615 CLIP3 -0.64 bta-miR-6535 ZNF558 -0.63 bta-miR-615 FAM83A -0.64 bta-miR-6535 ZSWIM1 -0.72 bta-miR-615 CHST13 -0.64 bta-miR-760-5p AC004899.1 -0.73 bta-miR-615 PARP6 -0.64 bta-miR-760-5p AC016722.1 -0.99 bta-miR-615 SNAI1 -0.63 bta-miR-760-5p ACR -0.57 bta-miR-615 SYNGR1 -0.62 bta-miR-760-5p AL450307.1 -0.54 bta-miR-615 STX1A -0.62

121

bta-miR-760-5p APH1A -0.67 bta-miR-615 RAB24 -0.62 bta-miR-760-5p ARFRP1 -0.56 bta-miR-615 SYNGR2 -0.61 bta-miR-760-5p ARHGEF2 -0.5 bta-miR-615 ZNF410 -0.61 bta-miR-760-5p ASIC4 -0.5 bta-miR-615 ATCAY -0.61 bta-miR-760-5p BCL2L1 -0.5 bta-miR-615 ZBTB47 -0.61 bta-miR-760-5p BCL2L2 -0.56 bta-miR-615 TMEM119 -0.61 bta-miR-760-5p C12orf5 -0.56 bta-miR-615 CDC16 -0.6 bta-miR-760-5p C14orf132 -0.66 bta-miR-615 KCTD15 -0.6 bta-miR-760-5p C16orf91 -0.56 bta-miR-615 IFITM5 -0.6 bta-miR-760-5p C17orf64 -0.52 bta-miR-615 TCF19 -0.6 bta-miR-760-5p CACNG8 -0.54 bta-miR-615 ZDHHC24 -0.59 bta-miR-760-5p CD300LB -1.29 bta-miR-615 KCNQ3 -0.59 bta-miR-760-5p CDK6 -0.54 bta-miR-615 MEIS2 -0.59 bta-miR-760-5p CHP2 -0.56 bta-miR-615 PAPPA-AS1 -0.58 bta-miR-760-5p CT62 -0.98 bta-miR-615 ITPKB -0.58 bta-miR-760-5p CYHR1 -0.75 bta-miR-615 ECE1 -0.58 bta-miR-760-5p DAPP1 -0.52 bta-miR-615 C7orf50 -0.58 bta-miR-760-5p DDIT4 -0.67 bta-miR-615 PDXP -0.58 bta-miR-760-5p DLK2 -0.55 bta-miR-615 PODXL2 -0.58 bta-miR-760-5p DMKN -0.78 bta-miR-615 FAM189A1 -0.58 bta-miR-760-5p DNAJC15 -0.77 bta-miR-615 DDR1 -0.58 bta-miR-760-5p DOCK1 -0.82 bta-miR-615 ONECUT3 -0.57 bta-miR-760-5p ELAVL4 -0.67 bta-miR-615 LILRA6 -0.57 bta-miR-760-5p ENDOV -0.53 bta-miR-615 ECHDC3 -0.57 bta-miR-760-5p ESM1 -0.59 bta-miR-615 ZDHHC22 -0.57 bta-miR-760-5p FAM109A -0.66 bta-miR-615 RGMA -0.57 bta-miR-760-5p GDE1 -0.57 bta-miR-615 MAP2K3 -0.56 bta-miR-760-5p GEMIN2 -0.63 bta-miR-615 TNFRSF18 -0.56 bta-miR-760-5p GIMAP6 -0.53 bta-miR-615 SPATA8 -0.56 bta-miR-760-5p GLIPR2 -0.93 bta-miR-615 ANKRD35 -0.56 bta-miR-760-5p GORASP1 -1.27 bta-miR-615 FIZ1 -0.56 bta-miR-760-5p GOT1 -0.55 bta-miR-615 DND1 -0.56 bta-miR-760-5p HPDL -0.57 bta-miR-615 ALPP -0.56 bta-miR-760-5p ICAM2 -0.58 bta-miR-615 KY -0.56 bta-miR-760-5p ILF2 -0.58 bta-miR-615 FAM43B -0.56 bta-miR-760-5p JPH2 -0.52 bta-miR-615 INSRR -0.56 bta-miR-760-5p KCTD11 -0.72 bta-miR-615 GTF3C5 -0.55 bta-miR-760-5p KDM1A -0.5 bta-miR-615 PRR24 -0.55 bta-miR-760-5p KRTAP9-7 -0.65 bta-miR-615 ASPHD2 -0.55 bta-miR-760-5p LRP11 -0.76 bta-miR-615 SSTR5 -0.55

122

bta-miR-760-5p LTA -0.68 bta-miR-615 PRR18 -0.55 bta-miR-760-5p MLANA -0.51 bta-miR-615 ARMC5 -0.55 bta-miR-760-5p MRPS18B -0.53 bta-miR-615 C9orf141 -0.55 bta-miR-760-5p NAT9 -0.58 bta-miR-615 FBXL12 -0.54 bta-miR-760-5p OR10A4 -0.53 bta-miR-615 C17orf103 -0.54 bta-miR-760-5p OR1L3 -0.84 bta-miR-615 SGCA -0.54 bta-miR-760-5p OR4M2 -0.5 bta-miR-615 COLQ -0.54 bta-miR-760-5p OR5AU1 -0.85 bta-miR-615 HHLA2 -0.54 bta-miR-760-5p PAX8 -0.72 bta-miR-615 C10orf82 -0.54 bta-miR-760-5p PCBD2 -0.55 bta-miR-615 EDN3 -0.54 bta-miR-760-5p PNKD -0.84 bta-miR-615 DRAXIN -0.54 bta-miR-760-5p POLR2J2 -0.73 bta-miR-615 NECAB3 -0.54 bta-miR-760-5p PON2 -0.64 bta-miR-615 NCAM1 -0.54 bta-miR-760-5p POSTN -0.75 bta-miR-615 NKD1 -0.53 bta-miR-760-5p PRNT -0.5 bta-miR-615 CYP3A5 -0.53 bta-miR-760-5p PRPH2 -0.81 bta-miR-615 CAPN15 -0.53 bta-miR-760-5p RAD54B -0.54 bta-miR-615 WNT10B -0.53 bta-miR-760-5p RASSF2 -0.5 bta-miR-615 C10orf55 -0.52 bta-miR-760-5p RHOU -0.87 bta-miR-615 SH3PXD2A -0.52 bta-miR-760-5p RP11-144F15.1 -0.56 bta-miR-615 ASPA -0.52 bta-miR-760-5p RPGRIP1 -0.67 bta-miR-615 FAM105A -0.52 bta-miR-760-5p SEC13 -0.64 bta-miR-615 VKORC1 -0.52 bta-miR-760-5p SH2D2A -0.51 bta-miR-615 ADAM12 -0.51 bta-miR-760-5p SLC25A45 -0.74 bta-miR-615 KREMEN2 -0.51 bta-miR-760-5p SLC25A48 -0.62 bta-miR-615 PRICKLE2 -0.51 bta-miR-760-5p SLC26A10 -0.71 bta-miR-615 PRR15L -0.51 bta-miR-760-5p SLC29A2 -0.58 bta-miR-615 CCDC155 -0.51 bta-miR-760-5p SLC4A11 -0.9 bta-miR-615 C2orf48 -0.51 bta-miR-760-5p STMN4 -0.52 bta-miR-615 CDC14A -0.51 bta-miR-760-5p SYP -0.57 bta-miR-615 TPM3 -0.5 bta-miR-760-5p TBC1D10C -0.63 bta-miR-615 TMEM217 -0.5

bta-miR-760-5p TMED4 -0.73 bta-miR-615 FGD5 -0.5

123