The number of children born since the origin of Assisted Reproductive Technologies (ART) exceeds 5 million. The majority seem healthy, but a higher frequency of defects has been reported among ART-conceived infants, suggesting an epigenetic cost. We report the first whole-genome DNA methylation datasets from single pig blastocysts showing differences between in vivo and in vitro produced embryos. Blastocysts were produced in vitro either without (C-IVF) or in the presence of natural reproductive fluids (Natur-IVF). Natur-IVF embryos were of higher quality than C-IVF in terms of cell number and hatching ability. RNA-Seq and DNA methylation analyses showed that Natur-IVF embryos have expression and methylation patterns closer to in vivo blastocysts. Genes involved in reprogramming, imprinting and development were affected by culture, with fewer aberrations in Natur-IVF embryos. Methylation analysis detected methylated changes in C-IVF, but not in Natur-IVF, at genes whose methylation could be critical, such as IGF2R and NNAT.DOI:http://dx.doi.org/10.7554/eLife.23670.001 Infertility has become more common in many countries, particularly those where many people delay having children until later in life. To help individuals experiencing infertility conceive a child, scientists have developed treatments called assisted reproductive technologies (or ARTs for short). So far, more than 5 million children have been born with the help of these treatments. Most of the children seem healthy; however, birth defects are more common in ART-conceived babies than those conceived without treatment.The cause of these birth defects is not known, though scientists suspect it may have something to do with techniques used in ART. One possible culprit is the liquid that is used in the laboratory to help the parents’ sperm and egg come together for fertilization. This same liquid is also used to bathe the developing embryo for the first few days after fertilization before it is implanted into its mother’s womb. Some scientists wonder whether adding the fluids normally found in the reproductive tract of their mother to this liquid could reduce defects in children conceived via ART.Now, Canovas et al. have shown that fertilizing and growing pig embryos in liquids supplemented with fluid from the wombs of female pigs results in embryos that are closer to naturally conceived pig embryos than in non-supplemented liquids. In the experiments, naturally conceived embryos were compared to ART embryos exposed to the usual liquids and with ART embryos grown in liquids with fluid collected from the pig’s reproductive tract added. Cutting edge technologies were used to sequence the entire genomes of all of the embryos and compare which genes were active in each case. Canovas et al. also looked at chemical markers on the DNA – called epigenetic changes – that turn on or off the expression of genes without changing the DNA code itself.The analysis showed that ART-conceived embryos grown in the usual liquid had different patterns of gene expression and epigenetic changes compared to naturally conceived embryos. Gene expression and epigenetic changes in the ART embryos grown with the pig reproductive fluid was more similar to the naturally conceived embryos.These findings suggest that abnormal gene expression in the ART-liquid exposed embryos may lead to birth defects, and that using natural reproductive fluids may be safer. To confirm this, scientists will have to implant embryos conceived in these three different conditions into mother pigs and assess the health and gene expression patterns of the resulting piglets. If successful, these new insights might one day lead to improvements in ART techniques used to treat infertility in people.DOI:http://dx.doi.org/10.7554/eLife.23670.002 Only a few are trying to discern whether in vitro fertilization (IVF) leaves a subtle legacy in children. What will happen to these kids when they are middle-aged?” . In humaFor modeling ART-related disorders in human, swine could be a good candidate for several reasons: their genetic, anatomical and physiological similarities with human ; their sIn both mouse and human, accumulating evidence indicates that the embryo is sensitive to its very early environment and that culture media used in ART (as factors involved in technique-derived effects) may have long-lasting consequences . SeveralDNA and RNA sequencing have become affordable cutting-edge technologies that could help to understand the mechanisms underlying abnormalities observed in ART-derived offspring. However, so far, single blastocyst whole-genome DNA methylation profiles comparing in vivo and in vitro produced embryos have not been published for any mammalian species and we therefore aim to produce these in this study.We report here that modified swim-up protocols for the selection of spermatozoa in pigs and the use of reproductive secretions as additives in the culture media significantly increase the yield and quality of the blastocysts produced from a morphological, epigenetic and gene expression point of view. Using genome-wide analyses of gene expression by RNA-Seq and DNA methylation by Bisulfite-Seq in single blastocysts, we provide datasets of pig blastocysts produced in vitro with and without reproductive secretions as additives in the culture medium and show that the former are more similar to the in vivo specimens than the later. This suggests an alternative approach for conceiving healthier ART-derived children.http://embryocloud.com, and in previous references . The swim-up medium was supplemented either with bovine serum albumin (BSA) (Swim-up BSA group) or porcine oviductal fluid  collected at the late follicular (LF) phase of the estrous cycle  . All theferences .10.7554/vs 17.4 ± 4.1, vs 14.6 ± 0.1, vs 29.7 ± 0.2, Polyspermy after IVF is a major issue in the pig . With thvs 43.7 ± 0.1, p<0.05) and similar monospermy rate  for the Natur-IVF and C-IVF groups, respectively. Regarding embryo development, more than 40% of cleaved embryos reached the blastocyst stage in both groups  system (Natur-IVF) was developed, which included preincubation of oocytes in oviductal fluid (NaturARTs PIG OF-LF) and the presence of reproductive fluids as additives in the IVF and EC media  . Corresph groups . Howeverh groups  comparedh groups . Taken tIn vitro culture systems significantly alter embryonic gene expression as previously observed in pooled pig blastocysts . Here, tTop Canonical Pathways, Physiological Systems and Molecular and Cellular Functions related to DEGs were identified , embryo development , cell growth  or imprinting  were amongst the C-IVF-specific DEGs . For all six genes data were available for the corresponding knock-out mice or knock-down studies, which showed phenotypes of altered/abnormal growth/size, reproduction/fertility, mortality/aging, hematopoietic system, homeostasis/metabolism and other abnormalities  method . Three bPCA revealed a good level of clustering for In-vivo and Natur-IVF embryos but not for C-IVF embryos . In partThe low level of global methylation suggested that few differentially methylated regions (DMRs) could be found. For this reason, and to obtain an unbiased measure of differences in genome methylation, a fixed size of 150 CpGs was used for analysis, as this was found to give a modal tile size of around 3 kb with about 150 reads per tile for most individuals. To make the data comparable to enable the detection of DMRs, separately from the global changes, the tiles informative in all samples  were extracted and quantile normalized. To identify DMRs, the comparison was filtered to require a consistent ≥5% absolute methylation change between all replicates of the first and second condition, followed by a T-test (B-H adjusted p<0.05). Differences between the groups were observed with fewer than 4000 DMRs for each pair-wise comparison . GloballTo better characterize the changes in methylation exclusively affecting one of the groups (p<0.05 for both comparisons), the corresponding subsets of DMRs (‘exclusive’ DMRs for each group) were obtained by combining the previous lists , and theIGF2R, a gene directly related with the LOS in ruminants and mouse, was found in the subset of exclusive C-IVF DMRs  and Diseases and Bio Functions  are expected to maintain constant methylation in preimplantation embryos to ensure faithful imprinted expression of the associated genes throughout development. Therefore, they represent sites of methylation in preimplantation of clear biological significance. To identify putative igDMRs in the pig genome, all mouse igDMRs were lifted-over onto the pig genome. Where this was not possible, a gene-by-gene approach was taken to find the best possible fit for a candidate igDMR based on the known organization of the corresponding mouse imprinted gene. All the genomic regions were then inspected manually to confirm that the correct regions had been found , and there were a number of others that were excluded because the homologous pig region had no suggestion of a CpG island in the region equivalent to the igDMR in mouse . Comparison of methylation in the three groups of blastocysts for the resulting 14 candidate igDMRs (with sufficient read coverage) revealed differences for ZAC1 and PEG10, which were more methylated (p<0.05) in the C-IVF than in In vivo group, and PEG10 and NNAT, which were more methylated (p<0.05) in the C-IVF than in Natur-IVF and In vivo groups  than would be expected from somatic tissues. Furthermore, there were differences in the global mean methylation levels between different samples, ranging from 8.9% to 18.5%. Taken together, these observations suggest that the samples were collected during a time of global methylation reprogramming. The variability in global methylation levels would have confounded a direct comparison focussing on locus-specific methylation differences, so to account for this a quantile normalization was required to allow for a direct quantitative comparison of methylation levels.Given that these samples are undergoing active reprogramming, it is also not unreasonable to think that some previously reported DMRs may not be established yet, or that the strength of the DMRs would be reduced. Despite this, we were able to find candidate DMRs between the groups with a reasonable statistical significance, although the magnitude of the methylation differences was low. Considering that previous studies have shown extremely close correlations between qPCR and RNA-seq data  and thatZAC1, PEG10 and NNAT) with significantly different methylation compared to In vivo blastocysts, and two (PEG10 and NNAT) compared to Natur-IVF. Knock-out mice lacking PEG10 showed early embryonic lethality with placental defects, indicating the importance of this gene in embryonic development  or Silver-Russell syndrome (SRS), respectively, including those born following the use of ART  and TP53INP (up-regulated in Natur-IVF and C-IVF) and HSPA4L, HSP40B1, HSPH1, HSP90 (down-regulated only in C-IVF). Altered expression of these genes may limit the ability of the embryo to respond to DNA damage, such that in vitro culture may lead to dysregulation of such genes, thus affecting long-term embryo viability (SLC2A3 (Glut-3) and SLC2A2, which have been related to LOS  of a mammalian species and propose a new strategy for prevention of aberrant epigenetic and gene expression profiles induced by ART. This strategy, based on the addition of reproductive fluids in the culture media used during the ART procedures, can be applied in other animals as well as in humans, after safety concerns of transmission of diseases have been properly addressed. The design of new culture media containing all the proteins that are naturally present in the original biological fluid, represents not only a technical challenge but a biomedical responsibility that must be addressed to prevent future pathologies both in animals and humans. In addition, we offer a new protocol for the in vitro production of pig embryos with a significant improvement over the previous data published. Our study represents a new form of thinking in the field, far from the chemically defined culture media, and could help to face one of the biggest milestones of the current reproductive medicine: safer ART.Unless otherwise indicated, all chemicals and reagents were purchased from Sigma-Aldrich Quimica S.A. .g for 30 min at 4°C and the supernatant filtered through 0.22 µm diameter filter  and slaughtered in an abattoir belonging to a food industry  near the University of Murcia. For the collection of follicular fluid, ovaries from 6-month-old Large White animals weighing 100–110 kg were transported to the laboratory in saline containing 100 μg/ml kanamycin sulfate, washed once in 0.04% cetrimide solution  and twice in saline within 30 min of slaughter. The content of follicles between 3 and 6 mm diameter, from at least 50 ovaries , was quickly aspirated, centrifuged at 1800 r filter . One ml g for 10 min at 4°C to remove cellular debris. Then the supernatant was immediately stored at −80°C until use. Oviducts from animals at the late follicular phase (POF-LF) and at the early luteal phase (POF-EL) gave a mean volume of around 50 µl and 40 µl, respectively per oviduct. At the early luteal phase, approximately 10 ml of UF per uterine horn were collected each time. Aliquots of 50 µl OF and 50 ml UF of pooled samples from at least 20 animals for OF and five animals for UF were used. Only samples that passed quality controls  were used for experiments.For the collection of oviductal (OF) and uterine (UF) fluids, genital tracts from cyclic Large White sows (2–4 years old) were obtained at the abattoir and transported to the laboratory on ice within 30 min of slaughter. The cyclic stage of animals was assessed once in the laboratory, on the basis of ovarian morphology on both ovaries from the same female. Oviducts and uteri were classified as early follicular, late follicular, early luteal or late luteal phase . Both ov2 in air. Maturation medium was NCSU37 supplemented with 0.57 mM cysteine, 1 mM dibutyryl cAMP, 5 mg/ml insulin, 50 µM β-mercaptoethanol, 10 IU/ml equine chorionic gonadotropin , 10 IU/ml human chorionic gonadotropin , and 10% porcine follicular fluid (v/v). Only COCs with complete and dense cumulus oophorus were used for the experiments. Groups of 50 COCs were cultured in 500 µl maturation medium for 22 hr at 38.5°C under 5% CO2 in air. After culture, oocytes were washed twice in fresh maturation medium without dibutyryl cAMP, eCG and hCG and cultured for an additional period of 20–22 hr.Ovaries from 6 months old animals weighing 100–110 kg were transported to the laboratory in saline containing 100 µg/ml kanamycin sulfate at 38°C, washed once in 0.04% cetrimide solution and twice in saline within 30 min of slaughter. Cumulus–oocyte complexes (COCs) were collected from antral follicles (3–6 mm diameter), washed twice with Dulbecco’s PBS (DPBS) supplemented with 1 mg/ml polyvinyl alcohol (PVA) and 0.005 mg/ml red phenol, and twice more in maturation medium previously equilibrated for a minimum of 3 hr at 38.5°C under 5% CO2O, 0.5 mM MgCl2.6H2O, 0.35 mM NaH2PO4, 25.07 mM NaHCO3, 1.85 mM Na-lactate, 0.11 mM Na-pyruvate, 5 mM glucose, 2 mM caffeine, 1 mg/ml PVA and 0.17 mM kanamycin sulfate. Either 3 mg/ml BSA-FAF (A-6003) or 1% of NaturARTs POF-LF was included as additives in the IVF medium for the first 8 hr of coincubation . Ejaculated spermatozoa from boars of proven fertility (1–2 years old) were transported to the laboratory and 1 ml of semen was lay below 1 ml of NaturARTs PIG sperm swim up medium (http://embryocloud.com) at the bottom of a conical tube. After 20 min of incubation at 37°C (with the tube at a 45° angle), 0.75 ml from the top of the tube were aspirated and used for insemination of the IVF dishes (105 cells/ml) with the oocytes. For the density gradient group, aliquots of the semen samples (0.5 ml) were centrifuged  through a discontinuous Percoll  gradient (45% and 90% v/v) and the resultant sperm pellets were diluted in TALP medium and centrifuged again for 10 min at 100 g. Finally, the pellet was diluted in TALP and 250 μl of this suspension were added to the wells containing the oocytes, giving a final concentration of 105 cells/ml.Before IVF, mature oocytes were preincubated in 100% porcine oviductal fluid (POF) from the late follicular (LF) phase (NaturARTs POF-LF) for 30 min  and then2 for 8 hours. Later on, the putative zygotes were transferred to embryo culture medium. At this point, a sample of the putative zygotes from each group was collected, fixed and stained as previously described (Spermatozoa and oocytes were incubated at 38.5°C under 5% COescribed  to assesMedia for embryo culture were NCSU23 supplemented with sodium lactate (5 mM), pyruvate (0.5 mM) and non-essential amino acids  or NCSU23 supplemented with glucose (5.5 mM) and essential and non-essential amino acids . At 8 hr post insemination (hpi), putative zygotes were transferred to culture dishes containing NCSU23a medium and 0.4% BSA in the C-IVF group or 1% POF from the early luteal (EL) phase of the estrous cycle (NaturARTs POF-EL) in the Natur-IVF group. At 48 hpi, the cleavage was assessed under the stereomicroscope and the 2–4 cell stage embryos were transferred to NCSU23b with 0.4% BSA (C-IVF group) or 1% of porcine uterine fluid (PUF) from early luteal phase . On day 7.5 (180 hpi), blastocyst stage morphology was assessed under the stereomicroscope and later on a sample was fixed and stained  and the Data are presented as mean ± SEM, and all percentages were modeled according to the binomial model of variables and arcsin transformation to achieve normal distribution. The variables in all the experiments were analyzed by one-way or two-way ANOVA. When ANOVAs revealed a significant effect, values were compared by the Tukey test. A pvalue < 0.05 was taken to denote statistical significance.Ten sows 18-month old were weaned 21 days after second parturition and five days later showed signs of standing estrous. Animals were inseminated in the collaborative farm and slaughterhoused 7.5 days after. Genital tracts were collected and transported to the laboratory where uterine horns were briefly dissected and washed with PBS within 2 hr from slaughtering. Blastocysts were identified under the stereomicroscope, collected and immediately frozen as described for the in vitro produced embryos. A portion of these blastocysts was fixed in glutaraldehyde and stained with Hoechst for cell counting.C-IVF group (C-IVF): six blastocysts classified as 7A according to Bo and Mapletoft   and data were visualized with Seqmonk . RNA quality was assayed by Bioanalyzer and even though each sample came from a single blastocyst, RIN score was between 6.1–8.2.ARCTURUS PicoPure RNA Isolation Kit  was used to extract the RNA from individual blastocysts. RNA-Seq libraries were generated using Ovation RNA-Seq System V2  for low amount of starting material and further amplified with NEB Next DNA Library Prep Master Mix for Illumina . All steps were performed according to manufacture guidelines. iPCRTag reverse primer with individual index was used to generate three independent biological replicates from each condition. 100 bp single end reads were sequenced on Illumina HiSeq 1000. Sequencing data were processed. For RNA-Seq libraries, raw sequence reads were trimmed using Trim Galore to remove adapter contamination and reads with poor quality defined by low PHRED score. Mapping was performed using Tophat software  and globally normalized to the 75th percentile of the data. Significant effect sizes were selected using the Seqmonk intensity difference filter where the difference in expression in each gene was compared to the set of differences in the 1% of the data with the most similar average expression level as the gene being tested. Only genes with significantly higher changes (p<0.05 after Benjamini and Hochberg correction) were kept.et al. (An adaptation of whole genome bisulfite sequencing that involves post-bisulfite adapter tagging (PBAT)  was usedet al.  was usedet al.  with KAPFor the unbiased analysis, tiles were defined in SeqMonk using the read position tile generator tool and selecting one read count per position and 150 valid positions per window, in all the nine individual data sets . Then, the bisulphite quantitation pipeline was run over existing tiles, one minimum count to include position and 20 minimum observations to include feature. To remove the tiles without data, the filter on values for individual tiles was applied, where values had to be between 0 and 100 for exactly 9 of the nine selected data stores. Then, tiles with data for all the samples were obtained . Bisulphite quantitation pipeline was run again over the new tiles and data were normalized by the match distribution quantile normalization tool. Finally, every pair-wise comparison was filtered to require a consistent 5% change between all replicates of the first and second condition, and then replicate sets stats was applied where every comparison had a significance below 0.05 after Benjamini and Hochberg correction. For the targeted analysis of the candidate imprinted regions a Chi-Square test (p<0.05) was applied for every comparison. In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.Editors’ note: a previous version of this study was rejected after peer review, but the authors submitted for reconsideration. The first decision letter after peer review is shown below.]eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your work entitled "Epigenetic and gene expression changes derived from assisted reproduction can be mitigated by reproductive secretions" for consideration by eLife. We felt that you have undertaken a very important subject matter and there are many new and potentially significant advances in the study. However, all three reviewers expressed significant technical concerns regarding the quality of the material pre and post amplification, characteristics and quality of the natural fluid obtained from pigs, and data analysis. They also felt that the conclusions have not been justified by the data. We hope you will find the comments helpful as you revise your work. Our usual policy is that we reject papers unless technical concerns could be addressed within 1-2 months. We feel that the amount of revision work necessary for your study to be competitive would require more work than one could reasonably do in that time frame. If you do gather additional evidence down the road that could satisfy in full the reviewer concerns, we could potentially welcome the submission of a new manuscript for consideration.Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that we cannot accept your present manuscript for publication in Reviewer #1:The authors have undertaken a very important and difficult study. They are correct in emphasizing the need for data of this type. An enormous amount of work went into carrying out this work. There are some very impressive results, but in some areas, the authors may have attempted some interpretations that are beyond what the data can support.The data in The tables and figures related to DMRs are much weaker. All of the qualifications and problems described in the subsection “Three imprinted genes were differentially methylated in C-IVF, but not in Natur-IVF blastocysts, compared to in vivo blastocysts” raise questions about whether it is feasible to interpret the data in the way that is presented. Given concerns about gaps in the pig genome, manual inspection of all genome regions, and failure to detect prominent DMRs such as SNRPN, which is widely conserved across species, raise questions as to whether these analyses and the associated interpretations are meaningful. The legend for An attempt to reduce the manuscript to those data and interpretations that are most solid would be useful.Philosophically, this reviewer is not convinced that use of human reproductive fluids is the way of the future. The analogy to breast milk is valid in some ways but in most ways not. Reproductive fluids are overwhelmingly less accessible than breast milk, and are likely to vary from patient to patient especially in pathologic circumstances. Even for breast milk, which is far more accessible, babies whose mothers are unable to breastfeed do not usually get fed breast milk from other women, but rather receive artificial formulas of greater and greater sophistication. Wet nursing is unlikely to make a big comeback in the future. Rather proteomic analysis of various fluids should make it possible to prepare reproducibly better and better artificial reproductive fluids.Reviewer #2:The work by Canovas et al. titled: "Epigenetic and gene expression changes derived from assisted reproduction can be mitigated by reproductive secretions" explores the developmental, transcriptomic (RNA seq) and epigenetics (WGBS by using post-bisulfite adapter tagging- PBAT) effects of IVF. The authors tested single pig blastocysts generated by IVF using conventional media (C-IVF) or media with the addition of physiologic secretions obtained from the fallopian tubes  or the uterus . The control is provided by in vivo blastocysts flashed out of the uterus. Further they tested performance of a new swim up technique as opposed to the more conventional gradient centrifugation methods to select sperm for IVF.The authors found that:1) Swim up technique is superior to standard fertilization, resulting in higher blastocyst rateNature IVF compared to C-IVF resulted in2) higher blastocyst state with faster development (higher number of hatched/ hatching blastocysts3) higher total cell number, that was similar to in vivo blasts4) less gene expression changes (789 DEG vs 623)5) Less DNA methylation changes in CpGs (11% vs 15%) while in vivo embryos had an average of 12%. Further they indicate that more imprinted genes or genes involved in epigenetic controls are altered in C-IVF.The work has important translational value, given that more than 5 million children have been conceived by IVF and that epigenetic and developmental changes have been postulated in children and described in IVF offspring using animal models. Testing novel fertilization and culture techniques that could decrease the epigenetic and developmental problems in offspring is therefore valuable and important. Further, performing single blastocyst analysis is an important technical achievement.While the study appears to be well designed, there are few but significant technical concerns.1) First, it is not described in the paper (there is a reference to a website) the standard operating procedure utilized to obtain the physiologic follicular and uterine fluid from pigs and how the media was stored and controlled for contamination. This is a critical aspect of the paper that is missing. For example: is there evidence that late follicular fluid collected from live pigs is always the same, given that changes in nutrition/ temperature/ wellbeing of the animal could change the composition of the fluid? Further: is the fluid from a single animal or pooled from how many animals?2) Quality controls and validation of RNA seq and WGBS are missing. This is critical given that authors worked with extremely low amount of material. .3) Unsupervised clustering of the 9 samples for both RNA seq and WGBS of all genes  should be provided to truly confirm that samples cluster based on fertilization and culture methods.Reviewer #3:In the manuscript entitled "Epigenetic and gene expression changes derived from assisted reproduction can be mitigated by reproductive secretions", Canovas and colleagues investigate the transcriptome and methylome of in vivo-derived and in vitro produced pig blastocysts. To do this, the group first developed a swim-up method for isolating sperm. Following fertilization, in vitro-produced embryo was cultured in conventional conditions that include BSA, or conventional culture augmented with 1% oviductal fluid and uterine fluid. RNA seq and whole genome bisulfite seq were performed on three individual blastocysts per treatment group then data were pooled for analysis. Differential expressed genes and differentially methylated regions were identified between the various experimental groups. In this manuscript, the authors have produced a vast amount of data using novel approaches, including the production of pig embryos with reproductive secretions and single blastocyst methylation analyses. Overall, the paper is well written.1) Overinterpretation/misrepresentation of data.A) The manuscript concluded that "reproductive fluids improve the outcome of IVF and the quality of pig blastocysts produced in vitro", and that "remarkable findings were that Natur-IVF blastocysts attained a more advanced developmental stage". These conclusions are not readily discernible from the data. In B) The authors bias the presentation of the transcriptome and methylome data towards finding less difference in the Natur-IVF group. The major questions that should be asked are whether there are expression/methylation changes between the three experimental groups and then where do these differences exist. Furthermore, what is the variation between embryos in the same group? The authors should present PCA graphs to visualize the three embryos and three groups. This should then be followed by pairwise comparisons for all groups, with VENN diagrams shown for all comparisons, including Natur-IVF and C-IVF. In fact, when the comparison is made , only 29 differentially expressed genes were identified between Natur-IVF and C-IVF, demonstrating little difference in the cultured groups. Instead, as seen in 2) In this manuscript, the authors produced single blastocyst profiling then pooled the data. This suggests that only genes affected in all three embryos would be analyzed. Previous DNA methylation analyses have shown that losses and gains of DNA methylation appear to occur stochastically. Thus, pooling of the data may mask gene differences in response to culture. Is there enough coverage to perform analyses on single embryos? If not, can differences be assessed with 2/3 embryos showing the same differences? If not, the authors need to acknowledge that they have analyzed only the most common changes.3) The authors performed both transcriptome and methyome analyzes. Have they mapped the two data sets to determine whether DNA methylation changes are correlated with gene expression changed in neighboring genes?[Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]eLife. Your article has been reviewed by three peer reviewers, including Mellissa Mann (Reviewer #3), and the evaluation has been overseen Jessica Tyler as the Senior Editor and Reviewing Editor.Thank you for submitting your article "DNA methylation and gene expression changes derived from assisted reproduction can be decreased by reproductive fluids" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.In summary we feel that this is a valuable contribution to the field. There are numerous concerns but they can be addressed by changes to the text. One of the key concerns that needs to be addressed is the nature of the natural fluid used in the study, because if the same lot were used for all the experiments, this would be a problem. There are also a lot of major grammatical and editorial improvements that are essential, and this work must be edited by a scientist who speaks English as a first language before being sent back to us. I have included the full reviews here, so that you can make all of the necessary changes, as we consider them all essential and very easy to incorporate.Reviewer #1:The resubmission of the work by Canovas et al. has responded to the majority of comments. However there still remain several concerns. Overall the style of the new version is more difficult to read and will benefit from significant editing both from a language and spelling point of view.1) In particular the standard operating procedure utilized to obtain the physiologic follicular and uterine fluid from pigs and how the media was stored and controlled for contamination needs to be described in Materials and methods. Referring to other manuscripts is not helpful for readers. For example, the Carrasco 2008 manuscript is not even mentioned in the manuscript. The Coy 2008 paper includes only a short Methods section with a short description of the methods to collect samples from pigs, with no indication on how many pigs were pooled or the range of their age. As an example of lack of clarity, based on the Coy paper: for how many hours are the fallopian tubes at room temperature from sacrificing the animal to collecting the fluid? This is critical to the reader, as quality of the fluid could be compromised or lost during manipulation/transport at room temperature. More Details, including some included in the rebuttal to authors need to be included in Materials and methods section. Given the novelty of the method, this is absolutely needed.2) The fact that adding only 1% of oviduct fluid can obtain such remarkable results is impressive and this finding is surprisingly missing in the Discussion. If the authors were to add 5% or 10% of OF, would the result change? How did the authors come to the 1% dose? This needs to be commented on in the Discussion.3) A statement in the Discussion that validation of the RNA and DNA methylation results in independent samples was not performed need to be added as an important limitation of the paper.4) Please confirm and describe in the Methods section that the 3 biological replicates in the nature IVF group were generated from the addition of 3 independent sets of uterine and oviductal fluids , collected from different animals. This is a key experimental factor that is not described in the Methods section. In fact, adding the same OF to 3 different blastocysts would be expected to lead to similar/ consistent transcriptomic and epigenetic results in blastocysts. If this was not done, data from independent fluid donors should be provided.Reviewer #2:It was not possible to review the manuscript in much depth as was the case for the original submission. However, in general, the authors appear to have responded well to lengthy criticisms of three reviewers. The interpretations are more restricted. This reviewer continues to doubt that the future of the field of human ART lies in collecting biological fluids from humans or animals but rather that artificial fluids mimicking natural fluids more and more closely are the answer. However, the authors are entitled to their opinion. In general, the manuscript is an excellent contribution to the knowledge in this field.Reviewer #3:In the manuscript, Canovas and colleagues have investigated an important question in the ART field, for which they produced an impressive amount of data and novel findings. They have clarified and addressed all my concerns. [Editors’ note: the author responses to the first round of peer review follow.][…]Reviewer #1:The authors have undertaken a very important and difficult study. They are correct in emphasizing the need for data of this type. An enormous amount of work went into carrying out this work. There are some very impressive results, but in some areas, the authors may have attempted some interpretations that are beyond what the data can support.We acknowledge the encouraging comments from the reviewer and are glad to see he/she agrees with the need of this type of study. We have modified the manuscript according to his/her suggestions and those from the other reviewers and think that interpretations are now more adjusted to the data presented. So, we hope this version of our manuscript can be now considered acceptable for publication.The data in Thank you for the comment. We have included some images of blastocysts in The data in The amount of speculation has been restricted. In addition, a PCA analysis has been included showing how the three individuals of each group cluster together, thus legitimising the three pairwise comparisons made for exploring the gene expression data. Also, some figures have been modified and moved to Supplementary files.The tables and figures related to DMRs are much weaker. All of the qualifications and problems described in the subsection “Three imprinted genes were differentially methylated in C-IVF, but not in Natur-IVF blastocysts, compared to in vivo blastocysts” raise questions about whether it is feasible to interpret the data in the way that is presented. Given concerns about gaps in the pig genome, manual inspection of all genome regions, and failure to detect prominent DMRs such as SNRPN, which is widely conserved across species, raise questions as to whether these analyses and the associated interpretations are meaningful.The qualifications described in relation to the imprinted gene analysis are a legitimateand candid account of how we went about trying to identify candidate imprinted genes in the pig genome, based on the more extensive knowledge of imprinting in mouse and human, and in the face of some deficiencies in the pig genome assembly and the generally poor level of gene annotation. It is recognised that not all imprinted genes are conserved in their imprinted status between species and, indeed, our analysis of the genome organisation of some of these genessuggested that they are unlikely to be imprinted in pig. In the case of SNPRN, in human and mouse this is a very complex imprinted locus in which there are multiple alternative promoters and associated CpG islands that have arisen through a complex series of genomic duplication events  so, in the absence of further evidence, we did not feel it justified to attribute to any of possible CpG islands in the pig SNRPN locus the candidate igDMR. The fact that some imprinted genes could not be confidently identified based on comparative sequence organisation does not detract from the fact that we were able to identify 14 candidate igDMRs, which corresponds to more than half of the corresponding regions known from the mouse, and represents a very substantial subset and legitimises the analysis. Amongst these, we did detect significant differences in methylation at 3 igDMRs, and this finding is detailed in the text.Nonetheless, we would like to thank reviewer 1 for his/her comment because, while preparingthis answer, we have detected some mistakes in the writing that could have been confusing for the reader. We have re-written these paragraphs and hope now the methylation data and the associated interpretations in the manuscript are meaningful.The legend for An attempt to reduce the manuscript to those data and interpretations that are most solid would be useful.Legend for Philosophically, this reviewer is not convinced that use of human reproductive fluids is the way of the future. The analogy to breast milk is valid in some ways but in most ways not. Reproductive fluids are overwhelmingly less accessible than breast milk, and are likely to vary from patient to patient especially in pathologic circumstances. Even for breast milk, which is far more accessible, babies whose mothers are unable to breastfeed do not usually get fed breast milk from other women, but rather receive artificial formulas of greater and greater sophistication. Wet nursing is unlikely to make a big comeback in the future. Rather proteomic analysis of various fluids should make it possible to prepare reproducibly better and better artificial reproductive fluids.We acknowledge this comment and agree partially. Of course, human reproductive fluids are not easy to collect, although we have developed a system to do it and are now getting samples of around 50-150 μL per woman. The trick is to take the samples from healthy ovum donors attending the fertility centers. With the appropriate informed consents and legal permits, we are collecting these “health” samples simply by introducing a special catheter throughthe vagina to the uterus in these donors and characterizing the proteomic profiles as well as other parameters. Bearing in mind that the amount of fluids needed is very small  and that the volumes of culture medium in the human clinics are around 50 μL per embryo , each sample collected from only one woman could be potentially used as additive for around 200-400 drops of 50 μL. So, if the beneficial effect were proved, it would not be so difficult to replace the current rHBSA used as protein additive in the human culture media by the corresponding reproductive fluid .Regarding human milk, to use breastfeeding or artificial formula is a personal decision, butmedical benefits of the first one are very clear: “it contains nutrients necessary for infant's growth but also numerous bioactive factors contributing to beneficial effects on gastrointestinal maturation, host defence, infection, cardiovascular risks, metabolic disease, neurodevelopmental outcome as well as in infant's and mother's psychological well-being” . Human milk is the gold standard to provide nutritional support for all healthy and sick newborn infants including the very low birth weight (VLBW) infant (<1500 g)  When own mother's milk it is unavailable, donor human milk is recommended as the first alternative, which could be obtained through human milk biobanks that are growing worldwide (Updegrove KH. Breastfeed Med 2013). Similarly, the idea of creating reproductive fluids biobanks with fully characterized samples could provide some benefits and may be important in the future for treatment of specific pathologies.Reviewer #2:[…] While the study appears to be well designed, there are few but significant technical concerns.1) First, it is not described in the paper (there is a reference to a website) the standard operating procedure utilized to obtain the physiologic follicular and uterine fluid from pigs and how the media was stored and controlled for contamination. This is a critical aspect of the paper that is missing. For example: is there evidence that late follicular fluid collected from live pigs is always the same, given that changes in nutrition/ temperature/ wellbeing of the animal could change the composition of the fluid? Further: is the fluid from a single animal or pooled from how many animals?All the samples were pooled from commercial animals raised for human consumption (Large White x Landrace hybrids) from collaborative farms, fulfilling all the requirements for wellbeing, health and nutrition. The animals were slaughtered in an abattoir near the University of Murcia belonging to a big food industry . We have not included more details because all the procedures for fluid collection and storing were first described in 2008  and later on we have published different articles using these fluids and testing their effect or the effect of specific oviductal proteins on IVF, zona pellucida hardening, embryo development, embryo survival after cryopreservation, etc. These are the references published until now:Algarra B, Han L, Soriano-Úbeda C, Avilés M, Coy P, Jovine L, Jiménez-Movilla M. The C-Terminal Region Of Ovgp1 Remodelsthe Zona Pellucida1 And Modifies fertility parameters. Scientific Reports 2016. | 6:32556 | Doi: 10.1038/Srep32556Lopera-Vásquez R, Hamdi M, Maillo V, Lloreda V, Coy P, Gutiérrez-Adán A, Bermejo-Álvarez P, Rizos D..Effect Of Bovine Oviductal Fluid On Development And Quality Of Bovine Embryos in Vitro. Reproduction Fertility And Development 2015.Doi: 10.1071/Rd15238.Coy P, Yanagimachi R. Common And Species-Specific Roles Of Oviductal Proteins In Mammalian Fertilization And Embryo Development. Bioscience65:973-984.2015. (Doi:10.1093/Biosci/Biv119).Ballester L, Romero-Aguirregomezcorta J, Soriano-Úbeda C, Matás C, Romar R, Coy P. Timing Of Oviductal Fluid Collection, Steroid Concentrations And Sperm Preservation Method Affect in Vitro Fertilization Efficiency. Fertility And Sterility. 102:1762-1768. 2014 Http://Dx.Doi.Org/10.1016/J.Fertnstert.2014.08.009..Mondéjar I, Martínez I,Avilés M, Coy P. Identification Of Potential Oviductal Factors Responsible Of The Zona Pellucida Hardening And Monospermy During Fertilization In Mammals. Biology Of Reproduction 89 (3): 67, 1-8. 2013.Mondéjar I, Avilés M, Coy P. The Human Is An Exception To The Evolutionarily-Conserved Phenomenon Of Pre-Fertilization Zonapellucida Resistance To Proteolysis Induced By Oviductal Fluid. Human Reproduction28:718-728. 2013.Grullón La, Gadea J, Mondéjar I, Matás C, Romar R, Coy P. How Is Plasminogen/Plasmin System Contributing To Regulate Sperm Entry Into The Oocyte? Reproductive Sciences 20:1075-1082. 2013doi:10.1177/1933719112473657.Cebrián-Serrano A, Salvador I, Garcia-Roselló E, Pericuesta E, Pérez-Cerezales S, Gutierrez- Adán A, Coy P, Silvestre Ma. Influence Of Oviductal Fluid On in Vitro Fertilisation, Development And Gene Expression Of in Vitro Produced Bovine Blastocysts. Reproduction In Domestic Animals 48(2): 331-338. 2013Coy P, García-Vázquez Fa, Visconti P, Avilés M. Roles Of The Oviduct In Mammalian Fertilization. Reproduction144 649–660. 2012.Coy P, Jiménez-Movilla M, García-Vázquez Fa, Mondéjar I, Grullón L, Romar R. Oocytes Use Plasminogen- Plasmin System To Remove Supernumerary Spermatozoa. Human Reproduction 27(7):1985-1993. 2012.Mondéjar I, Acuña S, Izquierdo Rico Mj, Coy P, Avilés M. The Oviduct: Functional Genomic And Proteomic Approach. Reproduction In Domestic Animals 47(3): 22-29.2012Mondéjar I, Grullón La, García-Vázquez Fa, Romar R, Coy P. Fertilization Outcome Could Be Regulated By Binding Of Oviductal Plasminogen To Oocytes And By Releasing Of Plasminogen-Activators During Interplay Between Gametes. Fertility And Sterility2012 97(2):453-461.Avilés M, Gutiérrez-Adan A, Coy P. Oviductal Secretions: Will They Be Key Factors For The Future Arts?.Molecular Human Reproduction. 2010 16 (12): 896-906.Coy P, Avilés M. What Controls Polyspermy In Mammals, The Oviduct Or The Oocyte? Biological Reviews Cambphilos Soc. 2010;85(3):593-605.Coy P, Lloyd R, Romar R, Satake N, Matas C, Gadea J, Holt Wv. Effects Of Porcine Pre-Ovulatory Oviductal Fluid On Boar Sperm Function. Theriogenology74(4):632-642. 2010.Lloyd R, Romar R, Matas C, Gutiérrez-Adán A, Holt Wv, Coy P. Effects Of Oviductal Fluid On The Generation, Quality And Gene Expression Of Porcine Blastocyst Produced in Vitro.Reproduction 137:679-687. 2009.Coy P, Cánovas S, Romar R, Saavedra Md, Grullón L, Mondéjar I, Matás C, Avilés M. Oviduct-Specific Glycoprotein And Heparin Modulate Sperm-Zona Pellucida Interaction During Mammalian Fertilization. Proc Nat Acadsci Usa (Pnas), 105:15809–15814. 2008.Carrasco Lc, Coy P, Avilés M, Gadea J, Romar R.Glycosidase Determination In Bovine Oviduct Fluid At Follicular And Luteal Phases Of The Estrous Cycle. Reproduction, Fertility And Development20 1-10. 2008Carrasco Lc, Romar R, Avilés M, Gadea J, Coy P. Determination Of Glycosidase Activity In Porcine Oviduct Fluid At The Different Phases Of The Estrous Cycle. Reproduction136: 833–842. 2008.In the present study, apart from the website mentioned, we have cited in the Methods section our first article describing the beneficial effects of oviductal fluid on the IVF results in pig and cow and the procedures for collection of fluids , as well as two reviews  that summarize the information from the other articles and the procedures for the collection, standardization and quality control of the samples. However, parts of the procedures are patented by University of Murcia (ES-2532659B2) and for this reason we cannot give more details in the paper. Because we are directly involved in Embryocloud, we know that the biological activity of all the samples of fluids used for research purposes, as it was the case, was tested by their ability to induce zona pellucida resistance to proteolytic digestion . We have included part of this information in Materials and methods and could include the one that this reviewer considers necessary to avoid any technical concern.2) Quality controls and validation of RNA seq and WGBS are missing. This is critical given that authors worked with extremely low amount of material. , “there does not seem to be any justification to set a threshold at any specific RIN but rather it is important to be aware of the effects of low RIN and all samples should preferably be in close range in terms of quality.” In fact, this report showed the biggest effect in differential expression by comparing samples with RIN 10 vs RIN8 , while only 1% genes showed as differentially expressed when comparison was between samples with RIN 8 vs RIN6.In addition, quality control plots were obtained from SeqMonk, which indicated similar output inall samples and low presence of ribosomal RNA . Althoug.How many reads were obtained per sample?These are the numbers for RNASeq and PBAT,respectively:Regarding the quality of the PBAT data, we are using a low-cell protocol that is now wellestablished in our lab and was optimised for the analysis of a similar number of oocytes (~100) as the number of cells in the individual pig blastocysts  and which has proven to be very robust . The pig blastocyst PBAT libraries had slightly higher sequence duplication rates and lower overall complexity, which probably reflects a slightly lower number of cells the single embryos. The unique sequence alignment rate (table above) is somewhat lower than we typically obtain by mapping PBAT data from mouse or human samples (65-70%), and this likely reflects the gaps in the pig genome assembly.Further, validation of some key genes in independent samples, by RT PCR and bisulfite sequencing is missing).We had considered performing qPCR studies to ‘re-validate’ some of our gene- expression findings but there is little evidence that qPCR analyses from the same samples will add any extra utility to our data. Previous studies have shown extremely close correlations between qPCR and RNAseq data  Nat Methods 7: 843–847, Asmann YW, et al. (2009). BMC Genomics 10: 531, Wu AR, et al. (2014). Nat Methods 11: 41–46, Shi Y and He M (2014). Gene 538: 313–322). In contrast to microarrays, which always require qPCR validation, in RNAseq experiments probe bias, poor sensitivity and reduced linear range are not as problematic, since the entire transcript is assessed in a more or less unbiased manner  Nat Rev Genet 10: 57–63). In cases where there are discrepancies between RNAseq and qPCR, it would be most likely due to bias in the qPCR experiment (which has its own probe-bias based on what region of the cDNA is amplified). Ideally, we could re- validate our findings  in a separate cohort of samples collected for a new experiment including new animals for the in vivo embryos , but we would consider to perform it only if reviewers consider it critical to publish our results.The same reason would apply for PBAT DNA methylation analysis by sequencing.3) Unsupervised clustering of the 9 samples for both RNA seq and WGBS of all genes  should be provided to truly confirm that samples cluster based on fertilization and culture methods.For RNASeq, the PCA analysis gave the plot which has been included in the manuscript .For DNA methylation, we got the PCA plot which explains (32%) how in vivo and N-IVF are close each other and also shows the difference among the three C-IVF embryos .Reviewer #3:[…] 1) Overinterpretation/misrepresentation of data.A) The manuscript concluded that "reproductive fluids improve the outcome of IVF and the quality of pig blastocysts produced in vitro", and that "remarkable findings were that Natur-IVF blastocysts attained a more advanced developmental stage". These conclusions are not readily discernible from the data. In We have modified the tables to make them clearer, because from the reviewer´scomments we extracted that we were not able to explain it properly. In In B) The authors bias the presentation of the transcriptome and methylome data towards finding less difference in the Natur-IVF group. The major questions that should be asked are whether there are expression/methylation changes between the three experimental groups and then where do these differences exist. Furthermore, what is the variation between embryos in the same group? The authors should present PCA graphs to visualize the three embryos and three groups. This should then be followed by pairwise comparisons for all groups, with VENN diagrams shown for all comparisons, including Natur-IVF and C-IVF. In fact, when the comparison is made , only 29 differentially expressed genes were identified between Natur-IVF and C-IVF, demonstrating little difference in the cultured groups. Instead, as seen in Thank you for this comment. We have included PCA graphs to show how the three embryos from each group cluster together. As reviewer points, "the major questions that should be asked are whether there are expression/methylation changes between the three experimental groups and then where do these differences exist". That is exactly what we tried to do here. After checking the quality of the data and the clustering of the embryos (now we have added the PCA plot), we did pairwise comparisons and found that, for gene expression analysis, and despite of individual variability, both in vitro groups were more similar between them than any of them compared to in vivo. For this reason, we started analyzing differences between each in vitro group compared to the in vivo one  but continued looking at differences between the two in vitro groups . First conclusion was that all the groups were similar, having only 4%, 3.17% and 0.15% DEGs, respectively, for each pairwise comparison. However, we also found that the general tendency for the Natur-IVF DEGs was to show intermediate values between the in vivo and C-IVF ones. And, finally, despite the scarce differences, we found that some DEGs between C-IVF and Natur-IVF could be critical because the corresponding knock out or knock-down studies in mice showed phenotypes of altered/abnormal growth/size, reproduction/fertility, mortality/aging, hematopoietic system, homeostasis/metabolism and other abnormalities. Bearing in mind that the alteration of just one gene could have consequences in the normal development of an embryo, we thought it was important to highlight some of the most representative genes. Altogether, we think that our data "confirm that in vitro culture significantly alters embryonic gene expression”.For DNA methylation, there does not appear to be a significant difference between the three groups for total methylation levels or methylation levels over specific genomic features . Here agIn the context of demethylation that happens during early embryo development, withmean values of DNA methylation around 12% in vivo (12.33 ± 3.6) the observed differences (Natur-IVF: 11.09 ± 2.6 and C-IVF: 15.02 ± 3.3) could have a significant biological effect. In addition, specific DMRs, especially in the context of imprinting regions can also impact embryo development. We never hypothesized to find huge DNA methylation differences, because during the blastocyst stage there is an almost complete resetting of the methylome.For DNA methylation, the differences were, indeed, subtle, as shown in 2) In this manuscript, the authors produced single blastocyst profiling then pooled the data. This suggests that only genes affected in all three embryos would be analyzed. Previous DNA methylation analyses have shown that losses and gains of DNA methylation appear to occur stochastically. Thus, pooling of the data may mask gene differences in response to culture. Is there enough coverage to perform analyses on single embryos? If not, can differences be assessed with 2/3 embryos showing the same differences? If not, the authors need to acknowledge that they have analyzed only the most common changes.Thank you for the comment, we agree with you. We have indeed indicated that wechose the option of analyzing only the most common changes in this study, as above explained.3) The authors performed both transcriptome and methyome analyzes. Have they mapped the two data sets to determine whether DNA methylation changes are correlated with gene expression changed in neighboring genes?Yes, we did it and we did not found significant correlation between both changes.Although DNA methylation at the promoter/gene bodies is directly/indirectly correlated with gene expression, this is not strictly true during the periods of dramatic loss of DNA methylation, as during early embryo development or primordial germ cells (PGC) formation. For example, Gounktela et al., 2015 showed that during demethylation of PGCs there is a general uncoupling between DNA methylation and gene expression at some stages; literally they say: “Our data reveal a remarkable and pervasive loss of DNA methylation in human PGCs and AGCs during prenatal life that has almost no relationship to changes in gene expression”. We did correlation analysis and saw this lack of correlation between our methylation and gene expression data. In our opinion, at this stage of development and with this low level of methylation, this is not a surprising result.[Editors' note: the author responses to the re-review follow.][…] Reviewer #1:The resubmission of the work by Canova et al. has responded to the majority of comments. However there still remain several concerns. Overall the style of the new version is more difficult to read and will benefit from significant editing both from a language and spelling point of view.We acknowledge the encouraging comments from the reviewer and they have been very useful to improve the manuscript. We have modified it according to his/her suggestions and those from the other reviewers. An extensive review of the grammar and spelling has been done by Dr. Gavin Kelsey.1) In particular the standard operating procedure utilized to obtain the physiologic follicular and uterine fluid from pigs and how the media was stored and controlled for contamination needs to be described in Materials and methods. Referring to other manuscripts is not helpful for readers. For example, the Carrasco 2008 manuscript is not even mentioned in the manuscript. The Coy 2008 paper includes only a short Methods section with a short description of the methods to collect samples from pigs, with no indication on how many pigs were pooled or the range of their age. As an example of lack of clarity, based on the Coy paper: for how many hours are the fallopian tubes at room temperature from sacrificing the animal to collecting the fluid? This is critical to the reader, as quality of the fluid could be compromised or lost during manipulation/transport at room temperature. More Details, including some included in the rebuttal to authors need to be included in Materials and methods section. Given the novelty of the method, this is absolutely needed.We apologize for the lack of clarity about the operating procedure used to obtain the follicular, oviductal and uterine fluids from pigs. We tried to summarize the more relevant information and to avoid an extensive Materials and methods section. However, following reviewer´s comment we have now included detailed information in this section:“Collection and processing of follicular, oviductal and uterine fluidsFluids were obtained from animals raised at a commercial farm  and slaughtered in an abattoir belonging to a food industry  near the University of Murcia. […] One ml follicular fluid (FF) aliquots were stored at -80°C until their use as additives for the IVM medium.”This is the standard protocol used for porcine IVM in the last 25 years in our laboratory following Naito et al. indications :289-95) and has been proven to provide a beneficial microenvironment for further development of the immature oocytes .For the collection of oviductal (OF) and uterine (UF) fluids, genital tracts from cyclic Large White sows (2-4 years old) were obtained at the abattoir and transported to the laboratory on ice within 30 min of slaughter. The cyclic stage of animals was assessed once in the laboratory, on the basis of ovarian morphology on both ovaries from the same female. Oviducts and uteri were classified as early follicular, late follicular, early luteal or late luteal phase . Both oviducts and uteri coming from the same genital tract were classified as in the same stage of the cycle. Once classified, oviducts and uteri were separated and quickly washed once with 0.4% v/v cetrimide solution and twice in saline. Oviducts and uteri were dissected on Petri dishes or trays, respectively, sitting on ice. Once dissected, OF were collected by aspiration with an automatic pipette by introducing a 200µl pipette tip into the ampulla and manually making an increasing pressure gradient from the isthmus to the ampulla. The UF was collected by making a manual increasing pressure gradient from the proximal end to the distal end  of the uterine horn and letting the fluid drop into a sterile 50 ml Falcon tube. Once recovered, samples (OF and UF) were centrifuged twice at 7000 g for 10 min at 4°C to remove cellular debris. Then the supernatant was immediately stored at -80°C until use. Oviducts from animals at the late follicular phase (POF-LF) and at the early luteal phase (POF-EL) gave a mean volume of around 50µl and 40µl, respectively per oviduct. At the early luteal phase, approximately 10 ml of UF per uterine horn were collected each time. Aliquots of 50µl OF and 50ml UF of pooled samples from at least 20 animals for OF and 5 animals for UF were used. Only samples that passed quality controls  were used for experiments.2) The fact that adding only 1% of oviduct fluid can obtain such remarkable results is impressive and this finding is surprisingly missing in the Discussion. If the authors were to add 5% or 10% of OF, would the result change? How did the authors come to the 1% dose? This needs to be commented on in the Discussion.We acknowledge the encouraging comment from the reviewer.The use of 1% of fluid was established after testing a large number of experimental conditions in our laboratory where cleavage rate, monospermy, morphology and blastocyst development were assayed . Similarly, our collaborator Dr. Rizos, after testing different OF concentrations in the cow model, published that OF concentrations (1.25% and 0.625%) supported embryo development until Day 9 (27.5%) and produced higher-quality blastocysts .We agree that this finding is significant and it has been included in the Discussion.3) A statement in the Discussion that validation of the RNA and DNA methylation results in independent samples was not performed need to be added as an important limitation of the paper.Following this request, we have introduced the following paragraph in the Discussion section:“Considering that previous studies have shown extremely close correlations between qPCR and RNA-seq data  Nat Methods 7: 843–847; Asmann YW et al. (2009) BMC Genomics 10: 531; Wu AR, et al. (2014). Nat Methods 11: 41–46) and that validation by qPCR has its own probe-bias based on what region of the cDNA is amplified, we deem, in contrast to microarrays data, that there is not solid evidence that validation of the RNA-Seq and DNA methylation results by qPCR will provide extra significance to our results. For this reason, we did not perform qPCR validation in this study”.4) Please confirm and describe in the Methods section that the 3 biological replicates in the nature IVF group were generated from the addition of 3 independent sets of uterine and oviductal fluids , collected from different animals. This is a key experimental factor that is not described in the Methods section. In fact, adding the same OF to 3 different blastocysts would be expected to lead to similar/ consistent transcriptomic and epigenetic results in blastocysts. If this was not done, data from independent fluid donors should be provided.Thank you for the comment. It has helped us to clarify in the manuscript that the observed effect in the Natur-IVF group is not the result of adding reproductive fluid from one specific animal, which would be not representative. As described in the new Materials and methods section reproductive fluids were pooled from different animals , collected simultaneously to form a large batch/lot of fluid that was homogenous for the different experiments. This experimental design aims to follow the very well established and routine practice for cell cultures or IVF-EC assays that promotes the use of serum from the same lot/batch for a set of experiments. Otherwise, assuming that some variability could exist between animals  individual effect and treatment effect could be masked or confused.We had to use different batches of fluids for the oocyte preincubation step during the in vitro production of embryos because of the volume of fluid needed :289-95) and it has been demonstrated to provide a beneficial microenvironment for further development of the immature oocytes . Similarly, the use of large and homogenous batches of OF has been proved to be consistently beneficial by our group when added during IVF : 1762-1768). Finally, the addition of UF, despite being used in this article for the first time, has been checked in our lab with different batches and always has shown an improvement compared with conventional protocols (e.g. BSA).Reviewer #2:It was not possible to review the manuscript in much depth as was the case for the original submission. However, in general, the authors appear to have responded well to lengthy criticisms of three reviewers. The interpretations are more restricted. This reviewer continues to doubt that the future of the field of human ART lies in collecting biological fluids from humans or animals but rather that artificial fluids mimicking natural fluids more and more closely are the answer. However, the authors are entitled to their opinion. In general, the manuscript is an excellent contribution to the knowledge in this field.We really appreciate the reviewer´s comments, including the scepticism about the future of the field of human ART. Only the future will reveal the right answer. We are glad to see he/she recognizes our work as “an excellent contribution to the knowledge in this field”.


Therefore, this test allows us to accurately identify prostate cancer patients likely to experience future recurrent disease immediately following removal of the primary tumor.Prostate cancer is the most common form of non-dermatological cancer among US men, with an increasing incidence due to the aging population. Patients diagnosed with clinically localized disease identified as intermediate or high-risk are often treated by radical prostatectomy. Approximately 33% of these patients will suffer recurrence after surgery. Identifying patients likely to experience recurrence after radical prostatectomy would lead to improved clinical outcomes, as these patients could receive adjuvant radiotherapy. Here, we report a new tool for prediction of prostate cancer recurrence based on the expression pattern of a small set of cooperation response genes (CRGs). CRGs are a group of genes downstream of cooperating oncogenic mutations previously identified in a colon cancer model that are critical to the cancer phenotype. We show that systemic dysregulation of CRGs is also found in prostate cancer, including a 4-gene signature  capable of differentiating recurrent from non-recurrent prostate cancer. To develop a suitable diagnostic tool to predict disease outcomes in individual patients, multiple algorithms and data handling strategies were evaluated on a training set using leave-one-out cross-validation (LOOCV). The best-performing algorithm, when used in combination with a predictive nomogram based on clinical staging, predicted recurrent and non-recurrent disease outcomes in a blinded validation set with 83% accuracy, outperforming previous methods. Disease-free survival times between the cohort of prostate cancers predicted to recur and predicted not to recur differed significantly (p = 1.38×10 Prostate cancer is the most common non-dermatological cancer among men in the United States, with an estimated 220,000 new diagnoses in 2015 . These pMost prediction models for prostate cancer recurrence are based on clinical features alone . Gene exCooperation Response Genes (CRGs) are a group of genes synergistically dysregulated in response to cooperating oncogenic mutations. They are critical to the cancer phenotype at about 50% frequency . Due to In this study, we analyzed CRG expression in a set of frozen prostate cancer samples. A gene signature differentially expressed in prostate cancer that later recurred was identified, and various predictive algorithms were evaluated using this signature. The best algorithm was then combined with a surgical nomogram that further increased predictive power. This was capable of predicting clinical outcomes of an independent blinded validation set with 83% accuracy, outperforming previous methods.Tissue from prostate cancer and benign prostate was collected from patients (n = 55) who underwent radical prostatectomy between 1990 and 2002. All patients had newly-diagnosed, clinically localized prostate cancer . Data coDifferences in CRG expression between prostate cancer and benign prostate were assessed post-normalization. 64% of CRGs were significantly dysregulated in prostate cancer compared to benign prostate  , and expThe samples were separated into training (n = 32) and validation (n = 23) sets, and disease outcomes were blinded in the validation set. The strategy was to test multiple methods of making predictions using the training set, identify the method that generated the most accurate predictions via cross-validation, and use this method to make predictions using the validation set.R.To make predictions on patient outcomes, we first normalized our gene expression data. In addition, a newly developed method for imputing missing gene expression values caused by PCR amplification failure was implemented . Two-taicentroid algorithm resulted in the most accurate predictions, with an accuracy of 75% averaged across all data handling conditions and p-value cutoffs  on the training set, while the nearest neighbor algorithm was inferior (accuracy = 69%)  using the training data set. Each combination consists of a data handling method, a p-value cutoff for inclusion in gene signature, and a prediction algorithm. Of the data-handling strategies we tested, predictions made using data normalized to BECN1 and then imputed to restore missing values caused by PCR amplification failure performed best, with accuracy of 86% averaged across the three algorithmic predictive methods and p-value cutoffs Figure . Predicts Figure . The dis) Figure . The hig) Figure . This cu% Figure . The lacOur chosen conditions accurately predicted 70% of the samples in our validation set, outperforming or equaling two sets of predictions made utilizing different cutoffs of Gleason score  and predictions made based on surgical stage (64%) Figure . For ourReceiver operating characteristic (ROC) curves were created to evaluate the sensitivity and specificity of predictions made using different discrimination thresholds for recurrence or non-recurrence. ROC curves generated using CRG-based predictions resulted in an area under the curve (AUC) of 0.67 Figure . When sa-6, log-rank test).Kaplan-Meier survival curves were created to visualize recurrences in our predicted high risk and low risk cohorts Figure . Of the To control for the possibility that the predictive power of the gene signature reported here may be affected by variation in stromal content of the prostate cancer specimens, we assessed the mRNA expression of two stromal markers, smooth muscle alpha-actin and vimentin, in our validation set. The two mRNAs are highly expressed in smooth muscle, fibroblasts, and myofibroblasts. Neither vimentin nor alpha-actin was significantly differentially expressed in recurrent vs non-recurrent cohorts using both non-normalized expression values   and exprAn estimated 50,000 radical prostatectomies are performed each year in the US, and about 15,000 of these patients will experience biochemical recurrence. Here we show that a four-gene signature based on HBEGF, HOXC13, IGFBP2, and SATB1 was able to identify patients whose prostate cancer recurred. The prediction algorithm that incorporated surgical information provided accurate prognostic information on patient outcomes with 83% accuracy. Most patients undergoing radical prostatectomy receive no additional treatment beyond regular monitoring of PSA. Adjuvant radiotherapy administered immediately after radical prostatectomy could improve outcomes for patients identified using the CRG signature to be at high risk for recurrence.in vitro, including the prostate cancer line PC-3ML [The four genes identified in our signature, HBEGF, HOXC13, IGFBP2, and SATB1, all play significant roles in the modification of cancer phenotypes. HOXC13, a homeobox-family transcription factor known to control cell proliferation and differentiation, was found to be upregulated in our recurrent prostate cancer samples. HOXC13 has been reported to be upregulated in metastatic melanoma compared to primary tumor tissue . Furthere PC-3ML . SATB1 ie PC-3ML . Publicle PC-3ML . Althouge PC-3ML . The stre PC-3ML . Finallye PC-3ML , 26, 27.e PC-3ML .The focus for this study was biochemical recurrence after radical prostatectomy. Biochemical recurrence after prostatectomy almost always requires the primary tumor to already have escaped the prostatic capsule, either invading local tissue or metastasizing to regional lymph nodes or distant organs. These behaviors are hallmarks of tumor aggressiveness, and suggest that the CRG signature may provide valuable information for assessing patient outcomes even in patients who have not undergone radical prostatectomy. Currently the best test to determine risk of aggressive disease is the Gleason score, a test with limited predictive power in many cases. Several groups have already identified gene signatures that are hallmarks of either indolent or aggressive disease , 28, 29.Irshad et al.  identifiThe success of this model to predict prostate cancer recurrence speaks to the importance of the CRGs in regulating human cancer behavior. As the CRGs were originally identified in a colon cancer background, it stands to reason that CRG expression may provide valuable prognostic information for colon cancer as well. Early-stage colon cancer is treated primarily with surgical resection, and assessing the likelihood of recurrence is an important clinical question. Future studies of the predictive potential of CRGs may provide greater insight into the likelihood of different disease outcomes, thus permitting better-informed decisions about treatment.17 All tissue samples were reviewed by a board certified anatomic pathologist to verify the diagnosis of prostatic adenocarcinoma and to estimate the percent neoplastic tumor nuclei. Data collected for each patient included standard prognostic variables, such as Gleason score, clinical (cTNM) and pathological (pTNM) stage, and PSA. All patients had at least 3 years of follow-up data. Biochemical progression was defined by the AUA guidelines of serum PSA of 0.2 ng/mL or greater (obtained 6 weeks - 3 months postoperatively), with a second confirmatory level of PSA greater than 0.2 ng/mL.All tissue samples were collected from radical prostatectomy specimens by the RPCI Pathology Resource Network with IRB approval. Patient demographic, clinical, pathology, and outcome data were collected through the Clinical Data Network, another shared resource at RPCI. All tissue specimens were immediately processed and snap frozen in liquid nitrogen within 30 minutes of prostatectomy by the Department of Urology.Total RNA was harvested from frozen tissue sections using a standard Trizol  application. Tissues were homogenized with Trizol reagent. RNA was precipitated from the aqueous phase using isopropanol and rehydrated using DEPC water. An RNA aliquot was run on an Agilent 2100 bioanalyzer to confirm RNA integrity by generating a RNA Integrity Number (RIN) value. RNA was converted to cDNA and quantified via Taq-Man Low Density Array (TLDA) RT-PCR.18 Statistical assessment of differential expression was performed using a t-test based on maximum likelihood estimates (MLEs) of the within group means and variances generated by the development version of the nondetects package (manuscript in preparation).Ct values were normalized to Becn1. Non-detects were imputed based on estimation of a non-random missing data mechanism using the nondetects R/Bioconductor package.Two-tailed t-tests were performed on normalized data to identify genes differentially regulated between biochemically recurrent and non-recurrent cohorts. Multiple p-value cutoffs were tested to assess the relative success of different sizes of gene signature. Three prediction algorithms based on clustering techniques were generated and software was written to implement each in R. Algorithms incorporated a gene signature of size N and evaluated samples as points in N-dimensional space. The “distance” algorithm generates recurrence and non-recurrence scores by comparing the Euclidian distance between the sample point and all points in the recurrent and non-recurrent groups respectively. The “centroid” algorithm generates recurrence and non-recurrence scores by comparing the distance between the sample point and the centroids of the recurrent and non-recurrent group. The “nearest neighbor” algorithm generates recurrence and non-recurrence scores by comparing the distance between the sample point and the closest member in both the recurrent and non-recurrent groups. Predictions are made by selecting the lower of either the recurrence or non-recurrence scores.recurrence scorenon-recurrence scoreI = # genes in signatureN = # samples in training setn(i) = expression of the ith gene in the signature in the nth recurrent sample in the training setRn(i) = expression of the ith gene in the signature in the nth non-recurrent sample in the training setNRi = expression of the ith gene in the signature in the current sample in the test setXrecurrence scorenon-recurrence scoreI = # genes in signatureN = # samples in training setn(i) = average expression of the ith gene in the signature in the nth recurrent sample in the training setRn(i) = average expression of the ith gene in the signature in the nth non-recurrent sample in the training setNRi = expression of the ith gene in the signature in the current sample in the test setXrecurrence scorenon-recurrence scoreI = # genes in signaturen(i) = expression of the ith gene in the signature in the closest recurrent sample in the training setRn(i) = expression of the ith gene in the signature in the closest non-recurrent sample in the training setNRi = expression of the ith gene in the signature in the current sample in the test setXPredictions were made on a 32-sample training set with 16 biochemically recurrent and 16 non-recurrent tumors. Each permutation of normalization method, p-value cutoff for gene signature, and prediction algorithm was evaluated using Leave-one-out cross-validation (LOOCV).The centroid algorithm was used to make predictions about a 23-sample validation set using the imputed and BECN1-normalized data with a CRG signature generated using a p-value cutoff of < 0.01.Surgical information was incorporated into the prediction decision procedure to improve specificity. Tumors with clinical stage of T2bN0M0 or below, with negative surgical margins were classified as non-recurrent. All other predictions were generated algorithmically as before.ROC curves were created using predictions made by varying the discrimination threshold between a prediction of recurrence vs non-recurrence. The model is altered by adding a modifier to the recurrence score before making predictions. When a modifier of -10 is added, all samples were predicted to be non-recurrent; when a modifier of +10 is added, all samples were predicted to be recurrent. Sensitivity and specificity of predictions were measured and plotted for each value of the modifier at which a prediction changes.Kaplan-Meier survival curves were taken by plotting time from prostatectomy to biochemical recurrence for patients predicted to recur and patients predicted not to recur respectively. Statistical analysis was done via log-rank test using the R package “survdiff”.


Data from patients with rare diseases is often produced using different platforms and probe sets because patients are widely distributed in space and time. Aggregating such data requires a method of normalization that makes patient records comparable.This paper proposed DBNorm, implemented as an R package, is an algorithm that normalizes arbitrarily distributed data to a common, comparable form. Specifically, DBNorm merges data distributions by fitting functions to each of them, and using the probability of each element drawn from the fitted distribution to merge it into a global distribution. DBNorm contains state-of-the-art fitting functions including Polynomial, Fourier and Gaussian distributions, and also allows users to define their own fitting functions if required.The performance of DBNorm is compared with z-score, average difference, quantile normalization and ComBat on a set of datasets, including several that are publically available. The performance of these normalization methods are compared using statistics, visualization, and classification when class labels are known based on a number of self-generated and public microarray datasets. The experimental results show that DBNorm achieves better normalization results than conventional methods. Finally, the approach has the potential to be applicable outside bioinformatics analysis.The online version of this article (doi:10.1186/s12859-017-1912-5) contains supplementary material, which is available to authorized users. Personalised or precision medicine aims to find specific therapeutics best-suited for individuals based on their genomic data . It is cAlthough next-generation sequencing and RNA sequencing are increasingly popular, oligonucleotide microarray technology is still in use, and a great deal of microarray data is available. However, it is difficult to compare the results derived from different cohorts  , or fromNormalization is essential in microarray data analysis to enable accurate comparison of expression levels between and within samples . A numbeThe performance of such methods depends on good choices of distribution features. They are often inefficient since multiple features must be aligned simultaneously. Most popular software packages for gene expression data analysis and research such as IRON , MAAMD , AGA 4]4] and BaWe propose an efficient, distribution-based normalization method, DBNorm that works on microarray data from multiple sources regardless of the distributions of the data in each. Because it scales data from different sources into the same distribution they will necessarily also have similar mean, variance and distribution features such as quantiles. We first compare the performance of DBNorm with four state-of-the-art methods: z-score, average difference (AvgDiff) , quantilThis section describes the main principle of DBNorm, followed by the workflow used to normalize two microarray data samples.Distribution-based normalization transforms values from one scale to another and keeps the order of the magnitudes of the original values unchanged. The probeset and sample value in the dataset that was the minimum before normalisation will remain the minimum after DBNorm. However, the value itself will be different. Similarly, the probeset sample value that was the maximum before normalisation will be the one that is the maximum after DBNorm, albeit taking a different value. In summary, the order of probeset sample values remains the same before and after normalisation. However, the values may differ. Our distribution-based normalization (DBNorm) method is based on this natural principle and is achieved by mapping the probabilities of values in one distribution to another. The fitting functions supported in the DBNorm package are polynomial, Fourier and Gaussian fitting. It also supports fitting distributions using user-defined functions.M1 and M2 representing intensities of the same probesets, but with different distributions. Suppose that their fitted probability density functions are f(∙) and g(∙) respectively. Given an element m1 ∈ M1, the probability of m1 is The target, desired, distribution might be known in particular contexts, but it is more typical to choose the dataset with the largest number of rows as the best target, transforming the other, smaller datasets to match. DBNorm fits functions to the values of each probeset by regarding them as probability density functions. The values in each column are then scaled to the desired distribution, also regarded as a probability distribution. Consider two columns of microarrays m’1 in M2 such that the probability of m1 in M1 is equal to the probability of m’1 in M2. This can be expressed in the following equation:We want an element M1 can be scaled to the same distribution as column M2 via Eq.  and source 2 (DS2). Merged together without normalization, they tend to form two clusters with each cluster from one data source. This can be seen in Fig. f(t) and g(t). The element m1 is mapped to m’1 via Eq. (DBNorm generates distribution information (visible in a histogram). In this example, the distributions DS1 and DS2 are fitted by polynomial functions denoted by g(t) to be a standard probability density function, e.g. a normal distribution. Code for this example, and an example of normalizing a data source to a standard distribution are included in the user manual of the DBNorm package.DBNorm can also rescale microarray data to a standard distribution by choosing This section describes the datasets used for evaluating the proposed normalization (DBNorm) method.The acute lymphoblastic leukaemia (ALL) dataset in this study was collected using Affymetrix microarray data of the diagnostic bone marrow of 146 childhood ALL patients collected by The Children’s Hospital at Westmead, Sydney, AustraliaTable We also use a set of public domain datasets whose details are listed in Table The spike-in dataset  is generThe public acute lymphoblastic leukemia (ALL) dataset contains 20 samples of the MLL subtype across two Affymetrix microarray platforms: HG-U133A and HG-U133B. The dataset is published in  and can As different platforms have different probesets Table , only thTable We choose platform U133Plus2 as the target and transform the other datasets to match Fig. . This shWe use the Kullback–Leibler Divergence to calculate the distribution similarities for each platform before and after normalization; the results are given in Table We further compare these standard normalization techniques: z-score, Average Difference (AvgDiff), ComBat and Quantile normalization with DBNorm. First, we compare the statistical properties of the distributions produced by these techniques Table . All norVisualization provides another way to assess the quality of normalizations. We compare the results of z-score normalization, AffyDiff, Quantile, ComBat and DBNorm, and visualize them using principal component analysis (PCA)  in two dThe result of applying z-score normalization only is illustrated in Fig. relapse. We use a soft margin Support Vector Machine (SVM) [p-value with confidence interval as 0.95  clusters. We compare prediction accuracy before and after normalization for the Westmead dataset, which is the only dataset for which we have a class label, ne (SVM)  with a Gne (SVM)  to evalu95 Table . Table 6Table We now compare the performance of the normalization methods on several public-domain datasets. The performance of these normalized methods is evaluated from statistical level including distributions and MA plot due to lack of class labels. MA plot visualizes the differences between measures taken into two selected samples by transforming the data onto M (log ratio) and A (mean average) scales which is considered as a major way to compare the difference of two gene-expression sources.For this experiment, we generate a dataset from both source A and source B. Specifically, we choose 201,800 Affymetrix microarray features. The results of normalization are illustrated in Fig. The dilution effect is clear in Fig. Z-score normalization narrows the gap between the red and blue lines as illustrated in Fig. AvgDiff works better than z-score. From MA plot Fig. , we can Intuitively, both Quantile and DBNorm normalization can minimize the gap between the red and blue lines Figs.  in the M−6, while the divergence of the distributions normalized by Quantile normalization is 5.788 × 10−5.The distributions of normalized data from DBNorm are more similar than those from Quantile normalization. The KL divergence of the distributions normalized by DBNorm is 2.283 × 10We further compare the performance of normalization on a dataset with 5184 common Affymetrix microarray features from platforms HGU133 and HGU95 to determine the ability of DBNorm on an atypical dataset that represents an extreme example of data distributions. The results of normalization are illustrated in Fig. From Fig. During normalization by Z-score, AvgDiff and Quantile, the spiked in gene populations are resistant to their normalization effect as shown in Figs. Finally, we compare the performance of normalization on a public MLL dataset generated from Affymetrix platforms HG-U133A and HG-U133B. The results of normalization are illustrated in Fig. Microarray data from heterogeneous sources is common, but has been underexploited because of the difficulty of matching data from different platforms at different times. Normalization is the key to data integration and enables consistent downstream analysis. DBNorm outperforms the other three methods investigated, based on statistical properties, KL divergence, and classification.Additional file 1: DBNorm user manual. A formal R package user manual which describes all functions contained in DBNorm package and examples. (PDF 104 kb)Additional file 2: DBNorm testing document. Results of how we test DBNorm package with built-in datasets. (DOCX 575 kb)Additional file 3: DBNorm test script. Code of how we test DBNorm package. (TXT 2 kb)Additional file 4: DBNorm installation. Describes how to install DBNorm via devtools in R. (TXT 4 kb)