# Fine-tuned adaptation of embryo-endometrium pairs at implantation revealed by gene regulatory networks
## Tailored conceptus-maternal communication at implantation

Fernando H. Biase, Isabelle Hue, Sarah E. Dickinson, Florence Jaffrezic, Denis Laloë, Harris Lewin, Olivier Sandra

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Fernando H. Biase, Isabelle Hue, Sarah E. Dickinson, Florence Jaffrezic, Denis Laloë, et al. Fine-tuned adaptation of embryo-endometrium pairs at implantation revealed by gene regulatory networks Tailored conceptus-maternal communication at implantation. 2023. hal-03336078

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1 Fine-tuned adaptation of embryo-endometrium pairs at implantation revealed by gene regulatory networks

2 Tailored conceptus-maternal communication at implantation

3

4 Fernando H. Biasea*, Isabelle Hueb, Sarah E. Dickinsona, Florence Jaffrezicc, Denis Laloec, Harris Lewind,

5 Olivier Sandrab*

6 a Department of Animal Sciences, Auburn University, Auburn, AL, USA 36839

7 b INRA, UMR 1198 Biologie du Développement et Reproduction, Ecole Nationale Vétérinaire d'Alfort, F-

8 78350 Jouy-en-Josas, France 78352

9 c UMR Génétique Animale et Biologie Intégrative, Institut National de la Recherche Agronomique, Jouy en

10 Josas, France 78352

11 d Department of Evolution and Ecology, University of California, Davis, CA, USA 95616

12 *Corresponding authors:

13 Fernando Biase; 559 Devall Drive, Auburn, AL, USA, 36839; email: fbiase@auburn.edu; phone: 334-844-

14 1680; fax: 334-844-1519

15 Olivier Sandra; UMR 1198 INRA-ENVA Biologie du Développement et Reproduction, Domaine de Vilvert,

16 bâtiment 230-231, Jouy-en-Josas, France 78352; email: olivier.sandra@inra.fr; phone: 00 33 1 34 65 23

17 43; fax: 00 33 1 34 65 23 64

18 Keywords: early pregnancy, system biology, embryo, cattle
19 ABSTRACT

Interactions between embryo and endometrium at implantation are critical for the progression and the issue of pregnancy. These reciprocal actions involve exchange of paracrine signals that govern implantation and placentation. However, it remains unknown how these interactions between the conceptus and the endometrium are coordinated at the level of an individual pregnancy. Under the hypothesis that gene expression of endometrium is dependent on gene expression of extraembryonic tissues, we performed an integrative analysis of transcriptome profiles of paired conceptuses and endometria obtained from pregnancies initiated by artificial insemination. We quantified strong dependence (|r|>0.95, eFDR<0.01) in transcript abundance of genes expressed in the extraembryonic tissues and genes expressed in the endometrium. The profiles of connectivity revealed distinct co-expression patterns of extraembryonic tissues with caruncular and intercaruncular areas of the endometrium. Notably, a subset of highly co-expressed genes between conceptus (n=229) and caruncular areas of the endometrium (n=218, r>0.9999, eFDR<0.001) revealed a blueprint of gene expression specific to each pregnancy. Functional analyses of genes co-expressed between conceptus and endometrium revealed significantly enriched functional modules with critical contribution for implantation and placentation, including "in utero embryonic development", "placenta development" and "regulation of transcription". Functional modules were remarkably specific to caruncular or intercaruncular areas of the endometrium. The quantitative and functional association between genes expressed in conceptus and endometrium emphasize a coordinated communication between these two entities in mammals. To our knowledge, we provide first evidence that implantation in mammalian pregnancy relies on the ability of the conceptus and the endometrium to develop a fine-tuned adaptive response characteristic of each pregnancy.
40 INTRODUCTION

In mammals, pregnancy recognition requires a tightly synchronized exchange of signals between the competent embryo and the receptive endometrium. The initiation of this signaling is triggered by key factors produced by the conceptus (1, 2) which are translated by the endometrial cells into actions that will condition the trajectory of embryo development as well as progeny phenotype. In mammalian species, including human, rodents and ruminants, the delicate balance in embryo-maternal communication is affected by the way the embryos are generated (natural mating, artificial insemination, *in vitro* fertilization somatic cell nuclear transfer) and by the sensor-driver properties of the endometrium defined by intrinsic maternal factors (i.e.: maternal metabolism, ageing) and environmental perturbations (i.e.: pathogens, nutrition) (3-5). The concept of sensor property applied to the mammalian endometrium was first proposed in a pioneer paper as was suggested the notion of endometrial plasticity (6). This property was recently confirmed *in vitro* with an aberrant responsiveness of human endometrial stromal cultured cells in the context of recurrent pregnancy loss (7). Nevertheless, it remains unaddressed whether the mammalian endometrium is able to develop an adaptive embryo-tailored response in a normal pregnancy.

In mammalian reproduction, sheep and cattle are research models that have relevantly contributed key insights in the understanding of molecular and physiological pregnancy-associated mechanisms, including the deciphering of embryo-endometrium interactions (8, 9). In the bovine species, by gestation days 7-8, the blastocyst enters the uterine lumen. After hatching by days 8-9, the outer monolayer of trophectoderm cells establishes direct contact with the luminal epithelium of the endometrium (10). On gestation days 12-13, the blastocyst is ovoid in shape (~2-5 mm) and transitions into a tubular shape by days 14-15. By day ~15, rapidly proliferating trophectoderm cells of the extra-embryonic tissues synthesize and release IFNT (11-15), which is the major pregnancy recognition signal in ruminants (1, 9, 16, 17). The disrupted release of the oxytocin-dependent pulses of prostaglandin F2 alpha (18) allows maintenance of progesterone production by a functional corpus luteum (18), which is critical for the establishment and progression of pregnancy (1, 4, 9, 12, 14, 15, 19). IFNT actions include induction of numerous classical and non-classical IFN-stimulated genes and stimulation of progesterone-induced genes that encode proteins involved in conceptus elongation and implantation (4). IFNT-regulated genes have diverse actions in the endometrium
that are essential for conceptus survival and pregnancy establishment (12). Other paracrine signals such as prostaglandins and cortisol have regulatory effects on conceptus elongation and endometrium remodeling (20). More recently, the identification of potential ligand-receptor interactions between the conceptus and endometrium (21) and the secretion of proteins and RNAs through exosomes (22, 23) have expanded the field of possibilities by which the conceptus and endometrium interact prior to and during implantation.

The cross-talk between the conceptus and the endometrium is associated with the expression and regulation of a wealth of genes in each entity (24, 25). The nature of the conceptus modifies gene expression of the endometrium in cattle (6, 26, 27) and decidualizing human endometrial stromal cells (28). Similarly, the endometrium from dams with different fertility potentials (29) or metabolic status (30) influences the gene expression of the conceptus. Despite the growing evidence of the interactions between conceptus and endometrium at the level of gene regulation, the pathways and the functions that result from this interaction have yet to be unveiled. Furthermore, the lack of integrated analysis between paired conceptus and endometrium has made it challenging to advance our understanding of the functional interactions between these two entities in normal pregnancies.

Here, we hypothesized that gene expression of extraembryonic tissue is not independent from gene expression of endometrium. In the present study, we carried out an integrative analysis of transcriptome profiles of paired conceptuses and endometria at the onset of implantation aiming at the identification of regulatory pathways that have coordinated expression between the conceptus and endometrium in normal pregnancies. Surprisingly, our results show that at gestation day 18 in cattle, several hundred genes have an expression profile in conceptus and caruncular areas of the endometrium that is unique to each pregnancy. Analyses of genes co-expressed between the conceptus and the paired-associated endometrium revealed significantly enriched functional modules with critical contribution for implantation and placentation. Our data provide evidence that successful implantation in mammalian pregnancy relies on the ability of the endometrium to elicit a fine-tuned adaptive response to the conceptus.

## 92 RESULTS
93 **Data overview**

94 We analyzed the RNA-sequencing data that consisted of samples collected from five cattle pregnancies
95 terminated at gestation day 18 (GSE74152 (26)). The conceptus was dissected, and transcriptome data
96 was generated for extraembryonic tissue; whereas the endometrium was dissected into caruncular (gland-
97 free) and intercaruncular (containing endometrial glands) areas, and transcriptome data was generated
98 from both regions of the endometrium (Fig 1A). Therefore, the dataset analyzed was comprised of three
99 samples collected from each pregnancy: extraembryonic, caruncular, and intercaruncular tissues (Fig 1B).
100 Alignment of the sequences to the *Bos taurus* genome (UMD 3.1) resulted into an average of 22, 31.4, and
101 34.6 million uniquely mapped reads for extraembryonic (n = 5), caruncular (n = 5), and intercaruncular (n =
102 5) tissues, respectively. We quantified the transcript abundance of 9548, 13047, and 13051 genes in
103 extraembryonic, caruncular, and intercaruncular tissues, respectively (Fig 1C). Unsupervised clustering of
104 the samples based on their transcriptome data separated the samples obtained from the conceptus from
105 the endometrial samples and further distinguished caruncular from intercaruncular endometrial samples
106 (Fig 1D).

107

108 Fig 1. Transcriptome profiling of conceptus and endometrium collected from gestation day 18. (A)
109 Representative images of pregnant uterus and micrograph identifying the tissues from which RNA-seq data
110 were used in this study. (B) Data structure used in this study. Data on genome-wide transcript abundance
111 was obtained from extraembryonic tissue (EET) and endometrium (caruncular (CAR), intercaruncular
112 (ICAR) tissues) from five pregnant uteri. (C) Number of genes with transcript abundance quantified in each
113 sample. (D) Dimensionality reduction of the RNA-seq data for special visualization of the sample
114 distribution.

115

116 **Correlated gene expression between conceptus and endometrium**
The associated expression between two genes can be assessed by correlative metrics (31) within (32, 33) or between tissues (33, 34). Thus, we calculated Pearson's coefficient of correlation (r (35)) to test whether there is association between the transcript abundance of genes expressed in extraembryonic tissue and endometrium (caruncular or intercaruncular tissues). We reasoned that under a null hypothesis, the abundance of a gene expressed in extraembryonic tissue (Gᵢ) would have no association with the abundance of a gene expressed in endometrium (Gₖ, or Gᵢ), for example: H₀: r(rᵢ, Gₖ) ≈ 0. On the other hand, under the alternative hypothesis (H₁: r(rᵢ, Gₖ) ≠ 0), two genes display co-expression (35).

The distribution of correlation coefficients for all pairs of genes expressed in extraembryonic and caruncular tissues averaged 0.13 (Fig 2A), and the equivalent distribution obtained for all pairs of genes expressed in extraembryonic and intercaruncular tissues averaged 0.03 (Fig 2B). Both distributions deviated significantly from a distribution obtained from shuffled data that disrupted the pairing of the conceptus and endometrium (P < 2.2⁻¹⁶, S1 Fig). We calculated the empirical FDR (eFDR) and noted that absolute correlation coefficients in both distributions were highly significant when greater than 0.95 (eFDR < 0.007, S2 Fig, S1 Table). Of note, S3 Fig and S4 Fig present examples of pairs of genes we identified with the highest positive and negative correlation coefficients, which fit the alternative hypothesis (H₁: r(rᵢ, Gₖ) ≠ 0) and examples of pairs of genes that show correlation coefficients close to zero fitting the null hypothesis (H₀: r(rᵢ, Gₖ) ≈ 0).

Fig 2. Co-expression analysis between extraembryonic tissue and endometrium. Distribution of the correlation coefficients for genes expressed in extraembryonic (EET) and caruncular (CAR) tissues (A) or intercaruncular (ICAR) tissues (B). (C) Number of genes expressed in either EET, CAR or ICAR that participate in significant correlation connections involving conceptus and endometrium. (D) Tanglengram of EET and CAR tissues formed by genes with strong co-expression. (E) Scatterplot of pairs of genes expressed in EET and CAR with at least one gene involved in "mRNA processing" or "chromatin organization". (F) Scatterplot of pairs of genes expressed in EET and CAR and highly correlated with at least one gene involved in "RNA transport pathway".
142 Notably, all 9548 genes expressed in extraembryonic tissue where positively ($r > 0.95$) and negatively ($-0.95 > r$) correlated with genes expressed in caruncular or intercaruncular tissues (Fig 1C). Eighty percent and 95% of the genes expressed in caruncle tissues were negatively and positively correlated with genes expressed in extraembryonic tissue. Similarly, 83% and 88% of the genes expressed in intercaruncular tissues were negatively and positively correlated with genes expressed in extraembryonic tissue (Fig 1C). The distribution of degrees of connectivity for significant correlations ($|r| > 0.95$, eFDR < 0.01) between extraembryonic and caruncular tissues was not equivalent to the distribution observed between extraembryonic and intercaruncular tissues ($P < 2.2^{-16}$). The genes expressed in extraembryonic tissue were significantly correlated with 295 genes expressed in caruncular tissues on average (median = 101). Eleven genes were significantly correlated with over 2300 genes in caruncular tissues (i.e. *AREG*, *EGR1*, *PEX3*, *GAN*, S5A Fig). The genes expressed in extraembryonic tissue were significantly correlated with 266 genes expressed in intercaruncular tissues on average (median = 252). Eight genes were significantly correlated with over 750 genes in intercaruncular tissues (i.e.: *WNT5B*, *WNT7B*, *ROR2*, *DPEP1*, *GJB3*, S4B Fig). These results strongly suggest different patterns of gene co-expression between extraembryonic and caruncular or intercaruncular tissues.

158 We then examined if genes co-expressed in extraembryonic tissue and endometrium have expression 159 patterns that are unique to pregnancies. We identified 229 and 218 genes expressed in extraembryonic 160 and caruncular tissues, respectively ($|r| > 0.9999$, eFDR < 0.001, S1 Table), whose expression profiles 161 produced equivalent dendrograms for extraembryonic and caruncular tissues independently ($P = 0.008$, 162 Fig 2D). Functional investigation of these 441 genes identified significant enrichment in the biological 163 processes "mRNA processing" (GEMIN6, PRPF4B, RBM39, SMNDC1, SUPT4H1, U2AF1L4, FDR = 0.13, 164 Fig 2E), "chromatin organization" (CDAN1, NUP133, SUPT4H1, FDR = 0.13, Fig 2E), and "protein 165 autoubiquitination" (CNOT4, MARCH5, UHRF1). We also interrogated the KEGG pathways database and 166 identified an enrichment for the "RNA transport" pathway (EIF4E, GEMIN6, KPNB1, MAGOHB, NUP133, 167 NUP54, PYM1, SENP2, SUMO1, THOC1, FDR = 0.06, Fig 2F). We did not identify groups of genes co- 168 expressed in extraembryonic and intercaruncular tissues capable of producing dendrograms that mirrored
each other. These results demonstrate that genes highly co-expressed between extraembryonic and caruncular tissues form a signature that independently distinguishes pregnancies in an equivalent manner.

## Visualization of co-expressed networks in extraembryonic tissue and endometrium

Our analysis was not an exhaustive evaluation of all potential co-expression networks that exist between conceptus and endometrium. Thus, we developed a web interface for dynamic and interactive data visualization based on the co-expression analysis conducted in the present study (36, 37) (https://biaselab.shinyapps.io/eet_endo/). The public access to this web application allows a user to produce networks for genes of their choosing. Furthermore, each network is accompanied by supporting data such as scatter plots and heatmaps of the gene expression values. The raw data and codes for reproduction of this interface can be downloaded from a GitHub repository (https://github.com/BiaseLab/eet_endo_gene_intersection).

## Functional networks between extraembryonic and caruncular tissues

We investigated the transcriptome-wide interactions between extraembryonic and caruncular or intercaruncular tissues independently. The clustering of genes based on co-expression is a powerful means to understand coordinated gene functions (38), thus we used the matrix with correlation coefficients to cluster extraembryonic, caruncular, and intercaruncular tissues independently.

The heatmap resultant of clustering the two datasets (extraembryonic and caruncular tissues) showed the formation of an organized co-expression network between the genes expressed in extraembryonic and caruncular tissues (Fig 3A). We identified 36 clusters formed by the genes expressed in extraembryonic tissue that presented enrichment for several biological processes (FDR < 0.2, Fig 3B), where we identified several genes expressed in extraembryonic tissue significantly co-expressed with genes expressed in caruncular tissues (see S1 Data for a list of genes). For instance, 142 genes associated with regulation of transcription were identified across clusters 1, 12, 30, 38, and 54. Eighty-two genes were associated with signal transduction across clusters 1, 21, 27, and 71. Interestingly, 26 genes associated with "in utero embryonic development" were identified in cluster 1.
Fig 3. Functional analysis of co-expressed genes between extraembryonic (EET) and caruncular (CAR) tissues. (A) Heatmap produced by the correlation coefficients and independent clustering of EET and CAR. (B) Gene ontology analysis of the cluster formed. Only significant coefficients of correlation are shown (|r| > 0.95, eFDR<0.01). The colored bar on the right of the heatmap indicates clusters of genes expressed in EET for which biological processes were significant. The colored bar on bottom of the heatmap indicates clusters of genes expressed in caruncle for which biological processes were significant. The colored squares at the bottom of the image identify the cluster number with the color observed on the bars. See S1 Data and S2 Data for details on the cluster identification, biological processes and genes. (C,D) Model of functional co-expression networks possibly formed between EET and CAR.

The clustering of genes expressed in caruncular tissues according to their co-expression with extraembryonic tissue genes resulted in the identification of 32 clusters presenting enrichment (FDR<0.2) for several Biological processes (Fig 3A, S2 Data). Among the genes forming significant co-expression with extraembryonic tissue, we identified 96 genes in cluster 3 associated with "intracellular protein transport", as well as 111 and four genes associated with regulation of transcription in clusters 4 and 5, respectively. Notably, ten genes on cluster 15 were associated with "defense response to virus", and the annotated genes are known to be stimulated by interferon-tau (IFIT1, IFIT3, IFIT5, ISG15, MX1, MX2, OAS1Y, RSAD2, S2 Data).

Next, we intersected the results of gene ontology enrichment obtained from clustering extraembryonic and caruncular tissues. We identified several biological processes on both datasets with co-expressing genes expressed in extraembryonic and caruncular tissues (S3 Data). Based on the number of genes and direction of connections, two pairs of biological processes are noteworthy. First, five genes associated with "positive regulation of cell proliferation" in extraembryonic tissue form negative co-expression connections ($\bar{x}_r = -0.96, n = 22$) with 14 genes associated with "regulation of transcription, DNA-templated" expressed in caruncle (Fig 3C). Second, ten genes associated with "transmembrane transport" in extraembryonic
tissue form positive co-expression connections ($\bar{x}_r = 0.97$, n = 22) with 12 genes associated with "regulation of transcription, DNA-templated" expressed in caruncle (Fig 3D). These results are coherent with a co-expression between genes expressed in extraembryonic and caruncular tissues, with functional implications to conceptus attachment and implantation.

## Functional networks between extraembryonic and intercaruncular tissues

The independent clustering of the correlation coefficients obtained from the genes expressed in extraembryonic and intercaruncular tissues also evidenced an organized co-expression network between the two tissues (Fig 4A). Twelve clusters formed by genes expressed in the extraembryonic tissue presented enrichment for biological processes (FDR < 0.2, Fig 4B, see S4 Data for a list of genes). Interestingly, there were 85 and 27 genes associated with "mRNA processing" and "stem cell population maintenance", respectively on cluster 3. On cluster five, we identified 12 genes associated with "negative regulation of cell proliferation" and seven genes associated with "regulation of receptor activity". On cluster eight, 5 genes were associated with "placenta development" (ADA, CCNF, DLX3, PHLDA2, RXRA). On cluster 17, eight genes were associated with "regulation of transcription, DNA-templated".

Fig 4. Functional analysis of co-expressed genes between extraembryonic (EET) and intercaruncular (ICAR) tissues. (A) Heatmap produced by the correlation coefficients and independent clustering of EET and ICAR. (B) Gene ontology analysis of the cluster formed. Only significant coefficients of correlation are shown ($|r| > 0.95$, eFDR<0.01). The colored bar on the right of the heatmap indicates clusters of genes expressed in EET for which biological processes were significant. The colored bar on bottom of the heatmap indicates clusters of genes expressed in intercaruncle for which biological processes were significant. The colored squares at the bottom of the image identify the cluster number with the color observed on the bars. See S4 Data and S5 Data for details on the cluster identification, biological processes and genes. (C,D) Model of functional co-expression networks possibly formed between EET and ICAR. See S6 fig for an enlarged version of panel C.
245
246 The clusters formed by intercaruncular genes co-expressed with extraembryonic tissue genes also
247 highlighted significant enrichment of biological processes (FDR < 0.2, Fig 4A, see S5 Data for a list of
248 genes). For instance, clusters one and six contained 145 and 63 genes associated with regulation of
249 transcription, respectively. Interestingly, on cluster two, there were 149, 23, 22, and 16 genes associated
250 with "oxidation-reduction process", "cell redox homeostasis", "electron transport chain", and "tricarboxylic
251 acid cycle". Cluster four contained 63 genes associated with "regulation of transcription", and cluster seven
252 contained 11 genes associated with "fatty acid beta-oxidation".

253 The intersection of the genes identified in enriched biological processes in clusters formed by
254 extraembryonic and intercaruncular tissues revealed several potential functional co-expression networks
255 between these two tissues (S6 Data). Notably, several of the intersecting categories involved processes
256 associated with regulation of transcription or oxidation-reduction on the intercaruncular side. For instance,
257 28 genes associated with "stem cell population maintenance" and expressed in extraembryonic tissue
258 presented positive co-expression ($\bar{x}_r$ = 0.97, n = 305) with 83 genes associated with "regulation of
259 transcription" and expressed in intercaruncular tissues (Fig 4D). Five genes associated with "placenta
260 development" and expressed in extraembryonic tissue presented negative co-expression ($\bar{x}_r$ = -0.97, n =
261 88) with 41 genes associated with "oxidation-reduction process" and expressed in intercaruncular tissues
262 (Fig 4E).

263 DISCUSSION

264 In mammals and particularly in the bovine species, a large body of gene expression data was produced at
265 various steps of early pregnancy derived from *in vitro* or *in vivo* produced embryos (6, 26, 27, 39), varied
266 physiological status of the dam (40), and fertility classified heifers (29). Altogether, results based on groups
267 analyses (conceptus or endometrium) have demonstrated different degrees of interactions between the
268 conceptus and endometrium at the initial phases of implantation. In the present study, our objective was to
269 shed light on the subtle interactions between the extraembryonic tissue of a conceptus and the endometrial
270 tissue of the uterus hosting this conceptus in normal pregnancy using paired co-expression analyses of
271 gene transcript abundances. Our analyses were carried out using biological material collected from the
272 single conceptus and the endometrium from the same pregnancy, a critical aspect to determine the cross-
273 talk during implantation at the level of one individual pregnant female.

274 Our analyses of transcriptome data from conceptus and endometrium pairs identified key signatures of
275 gene expression that are likely to be linked to the success of pregnancy recognition and implantation. A
276 large proportion of all genes quantified in extraembryonic tissue and endometrium have transcript
277 abundances that were not independent. Furthermore, the dependency observed for the abundance of
278 transcripts between extraembryonic tissue and endometrium varied with morphologically and
279 physiologically distinct areas of the endometrium, namely caruncular and intercaruncular tissues. For
280 instance, there were twice as many highly positive (r > 0.95) and approximately half the number of highly
281 negative (r < -0.95) co-expressing connections between extraembryonic and caruncular tissues compared
282 to extraembryonic and intercaruncular tissues. These results greatly expand previous findings that the
283 conceptus triggers distinct molecular responses in caruncular and intercaruncular tissues (6, 26, 41, 42).

284 During the elongation phase, the mural trophoblast proliferates rapidly (12, 25, 43) while maintaining its
285 pluripotency (44). This period of development is modulated by dynamic regulation of gene expression (43)
286 whereby metabolically active trophoblastic cells (45, 46) rely on the uptake of nutrients from the uterine
287 luminal fluid (47). Our results show that caruncular and intercaruncular tissues have an active role in the
288 programing of those functions, as several genes related with gene regulation, signal transduction, cellular
289 proliferation, maintenance of stem cell population, and transmembrane transport are also co-expressed
290 with genes expressed in the endometrium. The importance of gene co-regulation between extraembryonic
291 tissue and endometrium was further supported by the identification of 26 genes associated with "in utero
292 embryonic development" and five genes associated with "placenta development" co-regulated with genes
293 expressed in caruncle and intercaruncle, respectively.

294 Among the genes expressed in caruncular or intercaruncular tissues that were co-expressed with
295 extraembryonic tissues, it was noticeable that several genes were associated with regulation of gene
296 expression. This finding is in line with former publications reporting that the regulatory network needed for
endometrial remodeling (48) during attachment is conceptus-dependent. In the caruncular tissue, we specifically identified 15 genes associated with "defense response to virus", of which eight genes had their expression modulated by interferon-tau, produced by the trophoblast between gestation days 9 and 25 (49). This result provide additional knowledge on the biological actions of interferon-tau and other conceptus-originated signaling on the remodeling of the caruncle (50).

Our findings identified genes with high levels of co-expression ($|r| > 0.9999$) between extraembryonic tissue (n = 229) and endometrial caruncular tissues (n = 218) whose transcript profiles independently produced equivalent discrimination of the pregnancies. Functional interrogation of these 444 genes revealed that highly co-expressed genes between extraembryonic and caruncular tissues are involved in regulatory functions at the chromatin, mRNA processing, and protein levels; which is a strong indication of a coordinated reprograming of tissues driven by multiple layers of cell regulation during the conceptus-maternal recognition. These data prompt the need for additional investigation to better define the coordinated interactions between extra-embryonic tissues and endometrium at the level of tissue layer including luminal epithelium, stroma and glandular epithelium.

In the intercaruncular tissues, our analyses identified a list of genes related with "oxidation-reduction process", a finding consistent with a recent publication reporting that proteins associated with oxidation-reduction are enriched in the uterine luminal fluid on gestation day 16 in cattle (51). Oxidative stress is a consequence of altered oxidation-reduction state (52) and transcriptional regulation of factors involved in the regulation of oxidative stress has been reported in the bovine endometrium during oestrous cycle and early pregnancy (41, 53). Furthermore, a significant increase in oxidation-reduction potential was observed in the endometrium of mice prior to implantation (54). The results show evidence that the maintenance of oxidation-reduction status permissive to the conceptus health (55) and implantation is strongly linked to genes regulated in the glandular area of the endometrium in cattle.

The analyses carried out in this study have provided novel insights into the molecular contribution of extraembryonic, caruncular, and intercaruncular tissues to conceptus elongation, uterine receptivity, and implantation, summarized in Fig 5. Gene products expressed by the extraembryonic tissue impact the
endometrial function by regulating diverse cell functions including oxidative stress, chromatin remodeling, gene transcription, mRNA processing and translation. The endometrium also exerts key regulatory roles on the extraembryonic tissue cells by modulating chromatin remodeling, gene transcription, cell proliferation, translation, metabolism, and signaling (Fig 5). Collectively, our data have shown that endometrial plasticity, a notion first suggested in cattle (6), allows unique adaptive and coordinated conceptus-matched interactions at implantation in non-pathological pregnancies.

Fig 5. Working model of most prominent biological functions modulated by co-expression between extraembryonic tissue and endometrium. The arrows indicate probable direction of interaction.

To our knowledge, this study presents the first analysis of paired conceptus and endometrium in a mammalian species, using and integrative systems biology approach. Our results provide strong evidence that implantation in mammalian pregnancy relies on the ability of the endometrium to elicit a fine-tuned adaptive response to the conceptus in normal pregnancy. This finding opens new venues for the development of strategies to improve term pregnancy rates when artificial reproductive technologies are used. Since the endometrial response is embryo-specific, it would be valuable to develop approaches aiming at selection of the competent embryo better suitable for the establishment of a successful cross-talk with the recipient uterus of the female considered for transfer.

## MATERIAL AND METHODS

All analytical procedures were carried out in R software (36). The files and codes for full reproducibility of the results are listed on the S1 Code.

Data analyzed and estimation of gene expression levels
The appropriated approval from institutional committees of ethical oversight for animal use in research was obtained as reported previously (26). Briefly, all five cattle gestations were initiated by artificial insemination using semen from a single bull, and later terminated on gestation day 18 for sample collection. We analyzed RNA-seq generated from samples obtained from cattle gestations interrupted at day 18 (n = 5, GSE74152). The samples were extraembryonic tissue (n = 5), caruncle (n = 5), and intercaruncle (n = 5) regions from the endometrium.

The reads were aligned to the bovine genome (Bos taurus, UMD 3.1) using STAR aligner (56). Reads that aligned at one location of the genome with less than four mismatches were retained for elimination of duplicates. Non-duplicated reads were used for estimation of fragments per kilobase per million reads (FPKM) using Cufflinks (v.2.2.1 (57)) and Ensembl gene models (58). Genes were retained for downstream analyses is FPKM > 1 in ≥ 4 samples. We employed the t-Distributed Stochastic Neighbor Embedding approach (59) to assess the relatedness of the tissues.

## Calculation of correlation of gene expression between tissues

Three samples were collected from the same pregnancy, thus the data structure (Fig 1B) allowed us to quantify the association between genes expressed in extraembryonic tissue and endometrium (caruncular and intercaruncular tissues). We utilized Pearson's coefficient of correlation due to its sensitivity to outliers(60) to calculate $r_{(G_j, G_k)}$ and $r_{(G_j, G_l)}$, where $G_j, G_k$, and $G_l$ are the transcript abundance of a gene expressed in extraembryonic tissue caruncle and intercaruncle respectively. Empirical FDR was calculated by permuting the pregnancy index ($i = 1,...,5$) for the extraembryonic tissue samples thereby breaking the pairing of conceptus and endometrium obtained per pregnancy (100 permutations) and using the formulas described elsewhere to calculate the proportion of resulting correlation resulted from the scrambled data that was greater than a specific threshold (33, 61, 62).

## Testing the resemblance of two distance matrices

We calculated distance matrices for extraembryonic tissue and caruncle passed on the Pearson's coefficient of correlation of the expressed genes within tissues. The correlation matrix was subtracted from
370 one to obtain a distance matrix which was used as input for clustering using the method 'complete'. We
371 used the Mantel statistic test implemented in the 'mantel' package to assess the correlation between the
372 two dissimilarity matrices. The significance of the Mantel statistic was assessed by a permutation approach.

373 **Clustering of samples, heat maps, and network visualization**

374 We clustered samples using 'flashClust' package (63); we used the ComplexHeatmaps package (64) to
375 draw annotated heatmaps and Cytoscape software (65) to visualize the networks.

376 **Testing for enrichment of gene ontology terms or KEGG pathways**

377 We tested for enrichment of gene ontology (66) categories and KEGG pathways (67) using the 'goseq'
378 package (68). Subsets of genes were defined according to appropriate thresholds and defined as 'test
379 genes'; the genes expressed in the corresponding tissue were then used as background for the calculation
380 of significance values (69). Significance values were then adjusted for FDR according to the Benjamini and
381 Hochberg method (70).

382 **ACKNOWLEDGEMENTS**

383 The authors would like to thank the staff of INRA experimental units (Leudeville, France; Saint-Genes-
384 Chamanelle, France; Nouzilly, France) for animal management as well as past and present members of
385 UMR BDR including Dr. H. Jammes, M. Guillomot, A Chaulot Talmon and Corinne Giraud-Delville for help
386 in sample collection and preparation.

387

388 **REFERENCES**

389 1. Raheem KA. An insight into maternal recognition of pregnancy in mammalian species.

390 Journal of the Saudi Society of Agricultural Sciences. 2017;16(1):1-6.

391 2. Bazer FW. History of Maternal Recognition of Pregnancy. Adv Anat Embryol Cell Biol.

392 2015;216:5-25.
393 3. Macklon NS, Brosens JJ. The human endometrium as a sensor of embryo quality. Biol Reprod. 2014;91(4):98.

395 4. Sandra O, Charpigny G, Galio L, Hue I. Preattachment Embryos of Domestic Animals: Insights into Development and Paracrine Secretions. Annu Rev Anim Biosci. 2017;5:205-28.

397 5. Sandra O, Mansouri-Attia N, Lea RG. Novel aspects of endometrial function: a biological sensor of embryo quality and driver of pregnancy success. Reprod Fertil Dev. 2011;24(1):68-79.

400 6. Mansouri-Attia N, Sandra O, Aubert J, Degrelle S, Everts RE, Giraud-Delville C, et al. Endometrium as an early sensor of in vitro embryo manipulation technologies. P Natl Acad Sci USA. 2009;106(14):5687-92.

403 7. Lucas ES, Dyer NP, Murakami K, Lee YH, Chan YW, Grimaldi G, et al. Loss of Endometrial Plasticity in Recurrent Pregnancy Loss. Stem Cells. 2016;34(2):346-56.

405 8. Lee KY, DeMayo FJ. Animal models of implantation. Reproduction. 2004;128(6):679-95.

407 9. Bazer FW. Pregnancy recognition signaling mechanisms in ruminants and pigs. J Anim Sci Biotechnol. 2013;4(1):23.

409 10. Carson DD, Bagchi I, Dey SK, Enders AC, Fazleabas AT, Lessey BA, et al. Embryo implantation. Dev Biol. 2000;223(2):217-37.

411 11. Robinson RS, Hammond AJ, Wathes DC, Hunter MG, Mann GE. Corpus luteum-endometrium-embryo interactions in the dairy cow: underlying mechanisms and clinical relevance. Reprod Domest Anim. 2008;43 Suppl 2:104-12.

414 12. Spencer TE, Hansen TR. Implantation and Establishment of Pregnancy in Ruminants. Adv Anat Embryol Cell Biol. 2015;216:105-35.

416 13. Roberts RM. Interferon-tau, a Type 1 interferon involved in maternal recognition of pregnancy. Cytokine Growth Factor Rev. 2007;18(5-6):403-8.

418 14. Imakawa K, Bai R, Nakamura K, Kusama K. Thirty years of interferon-tau research; Past, present and future perspective. Anim Sci J. 2017;88(7):927-36.

420 15. Martal J, Chene N, Camous S, Huynh L, Lantier F, Hermier P, et al. Recent developments and potentialities for reducing embryo mortality in ruminants: the role of IFN-tau and other cytokines in early pregnancy. Reprod Fertil Dev. 1997;9(3):355-80.

423 16. Spencer TE, Burghardt RC, Johnson GA, Bazer FW. Conceptus signals for establishment and maintenance of pregnancy. Anim Reprod Sci. 2004;82-83:537-50.

425 17. Wooding P, Burton G. Comparative Placentation: Structures, Functions and Evolution. 2008:301.

427 18. Meyer MD, Hansen PJ, Thatcher WW, Drost M, Badinga L, Roberts RM, et al. Extension of Corpus Luteum Lifespan and Reduction of Uterine Secretion of Prostaglandin F2α of Cows in Response to Recombinant Interferon-τ. J Dairy Sci. 1995;78(9):1921-31.

430 19. Roberts RM, Chen Y, Ezashi T, Walker AM. Interferons and the maternal-conceptus dialog in mammals. Semin Cell Dev Biol. 2008;19(2):170-7.

432 20. Brooks K, Burns G, Spencer TE. Conceptus elongation in ruminants: roles of progesterone, prostaglandin, interferon tau and cortisol. J Anim Sci Biotechnol. 2014;5(1):53.

434 21. Mamo S, Mehta JP, Forde N, McGettigan P, Lonergan P. Conceptus-endometrium crosstalk during maternal recognition of pregnancy in cattle. Biol Reprod. 2012;87(1):6, 1-9.

436 22. Burns G, Brooks K, Wildung M, Navakanitworakul R, Christenson LK, Spencer TE. Extracellular vesicles in luminal fluid of the ovine uterus. PLoS One. 2014;9(3):e90913.
23. Burns GW, Brooks KE, Spencer TE. Extracellular Vesicles Originate from the Conceptus and Uterus During Early Pregnancy in Sheep. *Biol Reprod.* 2016;94(3):56.

24. Bazer FW, Wu G, Spencer TE, Johnson GA, Burghardt RC, Bayless K. Novel pathways for implantation and establishment and maintenance of pregnancy in mammals. *Mol Hum Reprod.* 2010;16(3):135-52.

25. Spencer TE, Forde N, Lonergan P. Insights into conceptus elongation and establishment of pregnancy in ruminants. *Reprod Fertil Dev.* 2016;29(1):84-100.

26. Biase FH, Rabel C, Guillomot M, Hue I, Andropolis K, Olmstead CA, et al. Massive dysregulation of genes involved in cell signaling and placental development in cloned cattle conceptus and maternal endometrium. *P Natl Acad Sci USA.* 2016;113(51):14492-501.

27. Bauersachs S, Ulbrich SE, Zakhartchenko V, Minten M, Reichenbach M, Reichenbach HD, et al. The endometrium responds differently to cloned versus fertilized embryos. *P Natl Acad Sci USA.* 2009;106(14):5681-6.

28. Brosens JJ, Salker MS, Teklenburg G, Nautiyal J, Salter S, Lucas ES, et al. Uterine selection of human embryos at implantation. *Sci Rep.* 2014;4:3894.

29. Moraes JGN, Behura SK, Geary TW, Hansen PJ, Neibergs HL, Spencer TE. Uterine influences on conceptus development in fertility-classified animals. *P Natl Acad Sci USA.* 2018;115(8):E1749-E58.

30. Forde N, Simintiras CA, Sturmey RG, Graf A, Wolf E, Blum H, et al. Effect of lactation on conceptus-maternal interactions at the initiation of implantation in cattle: I. Effects on the conceptus transcriptome and amino acid composition of the uterine luminal fluid. *Biol Reprod.* 2017;97(6):798-809.

31. de Siqueira Santos S, Takahashi DY, Nakata A, Fujita A. A comparative study of statistical methods used to identify dependencies between gene expression signals. *Brief Bioinform.* 2014;15(6):906-18.

32. Obayashi T, Hayashi S, Shibaoka M, Saeki M, Ohta H, Kinoshita K. COXPRESdb: a database of coexpressed gene networks in mammals. *Nucleic Acids Res.* 2008;36 part 2:2748-57.

33. Biase FH, Kimble KM. Functional signaling and gene regulatory networks between the oocyte and the surrounding cumulus cells. *BMC Genomics.* 2018;19(1):351.

34. Kogelman LJA, Fu JY, Franke L, Greve JW, Hofker M, Rensen SS, et al. Inter-Tissue Gene Co-Expression Networks between Metabolically Healthy and Unhealthy Obese Individuals. *Plos One.* 2016;11(12).

35. Song L, Langfelder P, Horvath S. Comparison of co-expression measures: mutual information, correlation, and model based indices. *BMC Bioinformatics.* 2012;13:328.

36. Ihaka R, Gentleman. R: A Language and Environment for Statistical Computing. *J Comput Graph Stat.* 1995;5:299-14.

37. Chang W, Cheng J, Allaire J, Xie Y, McPherson J. shiny: Web Application Framework for R. R package version 1.0.5 https://CRAN.R-project.org/package=shiny2017 [ ]

38. Swift S, Tucker A, Vinciotti V, Martin N, Orengo C, Liu X, et al. Consensus clustering and functional interpretation of gene-expression data. *Genome Biol.* 2004;5(11):R94.

39. Biase FH, Rabel C, Guillomot M, Sandra O, Andropolis K, Olmstead C, et al. Changes in WNT signaling-related gene expression associated with development and cloning in bovine extra-embryonic and endometrial tissues during the peri-implantation period. *Mol Reprod Dev.* 2013;80(12):977-87.
483 40. Valour D, Degrelle SA, Ponter AA, Giraud-Delville C, Campion E, Guyader-Joly C, et al. Energy and lipid metabolism gene expression of D18 embryos in dairy cows is related to dam physiological status. Physiol Genomics. 2014;46(2):39-56.

486 41. Mansouri-Attia N, Aubert J, Reinaud P, Giraud-Delville C, Taghouti G, Galio L, et al. Gene expression profiles of bovine caruncular and intercaruncular endometrium at implantation. Physiol Genomics. 2009;39(1):14-27.

489 42. Walker CG, Meier S, Littlejohn MD, Lehnert K, Roche JR, Mitchell MD. Modulation of the maternal immune system by the pre-implantation embryo. BMC Genomics. 2010;11:474.

491 43. Blomberg L, Hashizume K, Viebahn C. Blastocyst elongation, trophoblastic differentiation, and embryonic pattern formation. Reproduction. 2008;135(2):181-95.

493 44. Pfeffer PL, Pearton DJ. Trophoblast development. Reproduction. 2012;143(3):231-46.

494 45. Houghton FD. Energy metabolism of the inner cell mass and trophectoderm of the mouse blastocyst. Differentiation. 2006;74(1):11-8.

496 46. Bax BE, Bloxam DL. Energy metabolism and glycolysis in human placental trophoblast cells during differentiation. Biochim Biophys Acta. 1997;1319(2-3):283-92.

498 47. Bazer FW, Johnson GA, Wu G. Amino acids and conceptus development during the peri-implantation period of pregnancy. Adv Exp Med Biol. 2015;843:23-52.

500 48. King GJ, Atkinson BA, Robertson HA. Development of the bovine placentome from days 20 to 29 of gestation. J Reprod Fertil. 1980;59(1):95-100.

502 49. Kimura K, Spate LD, Green MP, Murphy CN, Seidel GE, Jr., Roberts RM. Sexual dimorphism in interferon-tau production by in vivo-derived bovine embryos. Mol Reprod Dev. 2004;67(2):193-9.

505 50. Atkinson BA, King GJ, Amoroso EC. Development of the caruncular and intercaruncular regions in the bovine endometrium. Biol Reprod. 1984;30(3):763-74.

507 51. Forde N, McGettigan PA, Mehta JP, O'Hara L, Mamo S, Bazer FW, et al. Proteomic analysis of uterine fluid during the pre-implantation period of pregnancy in cattle. Reproduction. 2014;147(5):575-87.

510 52. Sies H. Oxidative stress: a concept in redox biology and medicine. Redox Biol. 2015;4:180-3.

512 53. Lesage-Padilla A, Forde N, Poiree M, Healey GD, Giraud-Delville C, Reinaud P, et al. Maternal metabolism affects endometrial expression of oxidative stress and FOXL2 genes in cattle. PLoS One. 2017;12(12):e0189942.

515 54. Nakamura H, Hosono T, Kumasawa K, Kimura T. Prospective evaluation of uterine receptivity in mice. Reprod Fertil Dev. 2017.

517 55. Yoon SB, Choi SA, Sim BW, Kim JS, Mun SE, Jeong PS, et al. Developmental Competence of Bovine Early Embryos Depends on the Coupled Response Between Oxidative and Endoplasmic Reticulum Stress. Biol Reprod. 2014;90(5):104.

520 56. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15-21.

522 57. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012;7(3):562-78.

525 58. Flicek P, Amode MR, Barrell D, Beal K, Billis K, Brent S, et al. Ensembl 2014. Nucleic Acids Res. 2014;42 part 1:000001-000015.

527 59. van der Maaten LJP, Hinton GE. Visualizing High-Dimensional Data Using t-SNE. J Mach Learn Res. 2008;9:2579-605.
529 60. Serin EA, Nijveen H, Hilhorst HW, Ligterink W. Learning from Co-expression

530 Networks: Possibilities and Challenges. Front Plant Sci. 2016;7:444.

531 61. Storey JD, Tibshirani R. Statistical significance for genomewide studies. P Natl Acad Sci

532 USA. 2003;100(16):9440-5.

533 62. Sham PC, Purcell SM. Statistical power and significance testing in large-scale genetic

534 studies. Nat Rev Genet. 2014;15(5):335-46.

535 63. Langfelder P, Horvath S. Fast R Functions for Robust Correlations and Hierarchical

536 Clustering. J Stat Softw. 2012;46(11).

537 64. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in

538 multidimensional genomic data. Bioinformatics. 2016;32(18):2847-9.

539 65. Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, et al. Integration

540 of biological networks and gene expression data using Cytoscape. Nat Protoc. 2007;2(10):2366-

541 82.

542 66. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology:

543 tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25(1):25-9.

544 67. Du J, Yuan Z, Ma Z, Song J, Xie X, Chen Y. KEGG-PATH: Kyoto encyclopedia of

545 genes and genomes-based pathway analysis using a path analysis model. Mol Biosyst.

546 2014;10(9):2441-7.

547 68. Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq:

548 accounting for selection bias. Genome Biol. 2010;11(2):R14.

549 69. Timmons JA, Szkop KJ, Gallagher IJ. Multiple sources of bias confound functional

550 enrichment analysis of global -omics data. Genome Biol. 2015;16:186.

551 70. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate - a Practical and

552 Powerful Approach to Multiple Testing. J Roy Stat Soc B Met. 1995;57(1):289-300.

553
A Pregnancies GD 18

<figure><img src="image_3.png" /></figure>

bioRxiv preprint doi: https://doi.org/10.1101/427310; this version posted September 25, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

Tissues sampled

<figure><img src="image_4.png" /></figure>

B

Data structure

<table><thead><tr><th>Pregnancy</th><th>EET</th><th>ICAR</th><th>CAR</th></tr></thead><tbody><tr><td>1</td><td>G<sub>1j</sub></td><td>G<sub>1k</sub></td><td>G<sub>1l</sub></td></tr><tr><td>2</td><td>G<sub>2j</sub></td><td>G<sub>2k</sub></td><td>G<sub>2l</sub></td></tr><tr><td>3</td><td>G<sub>3j</sub></td><td>G<sub>3k</sub></td><td>G<sub>3l</sub></td></tr><tr><td>4</td><td>G<sub>4j</sub></td><td>G<sub>4k</sub></td><td>G<sub>4l</sub></td></tr><tr><td>5</td><td>G<sub>5j</sub></td><td>G<sub>5k</sub></td><td>G<sub>5l</sub></td></tr></tbody></table>

C

N genes

<figure><img src="image_5.png" /></figure>

D

<figure><img src="image_6.png" /></figure>
<figure><img src="image_7.png" /></figure>
<figure><img src="image_8.png" /></figure>

EET genes

CAR genes

<figure><img src="image_9.png" /></figure>

CAR genes

-0.95 > r > 0.95

eFDR<0.01

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>
<figure><img src="image_14.png" /></figure>
## EXTRA-EMBRYONIC TISSUE

<figure><img src="image_15.png" /></figure>

mRNA processing

translation

stem cell population maintenance

cell proliferation

ion transport

signal transduction