# Impact of negative energy balance on transcriptomic profiles of three endometrial cell types isolated by laser capture microdissection in postpartum dairy cows

Wiruntita Chankeaw, Sandra Lignier, Christophe Richard, Theodoros

Ntallaris, Mariam Raliou, Yongzhi Guo, Damien Plassard, Claudia

Bevilacqua, Olivier Sandra, Goran Andersson, et al.

► To cite this version:

Wiruntita Chankeaw, Sandra Lignier, Christophe Richard, Theodoros Ntallaris, Mariam Raliou, et al. Impact of negative energy balance on transcriptomic profiles of three endometrial cell types isolated by laser capture microdissection in postpartum dairy cows. 2024. hal-04446339

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# Impact of negative energy balance on transcriptomic profiles of three endometrial cell types isolated by laser capture microdissection in postpartum dairy cows

**Wiruntita Chankeaw**

Swedish University of Agricultural Sciences

**Sandra Lignier**

Université Paris-Saclay, UVSQ, INRAE, BREED, 78350, Jouy-en Josas

**Christophe Richard**

Université Paris-Saclay, UVSQ, INRAE, BREED, 78350, Jouy-en-Josas

**Theodoros Ntallaris**

Department of clinical Sciences, Swedish University of Agricultural Sciences, SLU, PO Box 7054, 750 07 Uppsala

**Mariam Raliou**

Université Paris-Saclay, UVSQ, INRAE, BREED, 78350, Jouy-en-Josas

**Yongzhi Guo**

Department of Clinical Sciences, Swedish University of Agricultural Sciences, SLU, PO Box 7054, 750 07 Uppsala

**Damien Plassard**

GenomEast Platform CERBM GIE, IGBMC 67404 Illkirch cedex

**Claudia Bevilacqua**

Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas

**Olivier Sandra**

Université Paris-Saclay, UVSQ, INRAE, BREED, 78350, Jouy-en-Josas

**Goran Andersson**

Department of Clinical Sciences, Swedish University of Agricultural Sciences, SLU, PO, Box 7054, 750 07 Uppsala

**Patrice Humblot**

Department of Clinical Sciences, Swedish University of Agricultural Sciences, SLU, PO Box 7054, 750 07 Uppsala

**Gilles Charpigny** (✉ gilles.charpigny@inrae.fr)

INRA https://orcid.org/0000-0003-3954-7663
# Impact of negative energy balance on transcriptomic profiles of three endometrial cell types isolated by laser capture microdissection in postpartum dairy cows

**Wiruntita Chankeaw**

Swedish University of Agricultural Sciences

**Sandra Lignier**

Université Paris-Saclay, UVSQ, INRAE, BREED, 78350, Jouy-en Josas

**Christophe Richard**

Université Paris-Saclay, UVSQ, INRAE, BREED, 78350, Jouy-en-Josas

**Theodoros Ntallaris**

Department of clinical Sciences, Swedish University of Agricultural Sciences, SLU, PO Box 7054, 750 07 Uppsala

**Mariam Raliou**

Université Paris-Saclay, UVSQ, INRAE, BREED, 78350, Jouy-en-Josas

**Yongzhi Guo**

Department of Clinical Sciences, Swedish University of Agricultural Sciences, SLU, PO Box 7054, 750 07 Uppsala

**Damien Plassard**

GenomEast Platform CERBM GIE, IGBMC 67404 Illkirch cedex

**Claudia Bevilacqua**

Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas

**Olivier Sandra**

Université Paris-Saclay, UVSQ, INRAE, BREED, 78350, Jouy-en-Josas

**Goran Andersson**

Department of Clinical Sciences, Swedish University of Agricultural Sciences, SLU, PO, Box 7054, 750 07 Uppsala

**Patrice Humblot**

Department of Clinical Sciences, Swedish University of Agricultural Sciences, SLU, PO Box 7054, 750 07 Uppsala

**Gilles Charpigny** (✉ gilles.charpigny@inrae.fr)

INRA https://orcid.org/0000-0003-3954-7663
Research article

**Keywords:** negative energy balance, endometrial cells, transcriptome, laser microdissection, 4 inflammation

**Posted Date:** June 29th, 2020

DOI: https://doi.org/10.21203/rs.3.rs-36108/v1

**License:** © 🌐 This work is licensed under a Creative Commons Attribution 4.0 International License.

Read Full License
Research article

**Keywords:** negative energy balance, endometrial cells, transcriptome, laser microdissection, 4 inflammation

**Posted Date:** June 29th, 2020

DOI: https://doi.org/10.21203/rs.3.rs-36108/v1

**License:** © Ⓟ This work is licensed under a Creative Commons Attribution 4.0 International License.

Read Full License
1

Impact of negative energy balance on transcriptomic profiles of three
endometrial cell types isolated by laser capture microdissection in
postpartum dairy cows

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Wiruntita Chankeaw¹,⁵, Sandra Lignier², Christophe Richard², Theodoros Ntallaris¹, Mariam Raliou²,

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Yongzhi Guo¹, Damien Plassard⁵, Claudia Bevilacqua³, Olivier Sandra², Göran Andersson⁴, Patrice

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Humblot¹, Gilles Charpigny²,⁷

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¹ Department of Clinical Sciences, Swedish University of Agricultural Sciences, SLU, PO Box 7054,
750 07 Uppsala, Sweden

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² Université Paris-Saclay, UVSQ, INRAE, BREED, 78350, Jouy-en-Josas, France

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³ Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy en Josas, France

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⁴ Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, SLU,
PO Box 7023, 750 07 Uppsala, Sweden

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⁵ Faculty of Veterinary Science, Rajamangala University of Technology Srivijaya (RUTS), Thungyai,
Nakhon si Thammarat, 80240 Thailand

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⁶ GenomEast Platform CERBM GIE, IGBMC 67404 Illkirch Cedex France

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⁷ Corresponding author: gilles.charpigny@inrae.fr

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Impact of negative energy balance on transcriptomic profiles of three
endometrial cell types isolated by laser capture microdissection in
postpartum dairy cows

Wiruntita Chankeaw¹,⁵, Sandra Lignier², Christophe Richard², Theodoros Ntallaris¹, Mariam Raliou²,

Yongzhi Guo¹, Damien Plassard⁵, Claudia Bevilacqua³, Olivier Sandra², Göran Andersson⁴, Patrice

Humblot¹, Gilles Charpigny²,⁷

¹ Department of Clinical Sciences, Swedish University of Agricultural Sciences, SLU, PO Box 7054,
750 07 Uppsala, Sweden

² Université Paris-Saclay, UVSQ, INRAE, BREED, 78350, Jouy-en-Josas, France

³ Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy en Josas, France

⁴ Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, SLU,
PO Box 7023, 750 07 Uppsala, Sweden

⁵ Faculty of Veterinary Science, Rajamangala University of Technology Srivijaya (RUTS), Thungyai,
Nakhon si Thammarat, 80240 Thailand

⁶ GenomEast Platform CERBM GIE, IGBMC 67404 Illkirch Cedex France

⁷ Corresponding author: gilles.charpigny@inrae.fr
1 Abstract

2 **Background:** In postpartum dairy cows, the energy needs to satisfy high milk production induces a
3 more or less pronounced Negative Energy Balance (NEB) status. NEB associated with fat
4 mobilization impairs reproductive function. This study investigated the specific impact of NEB on
5 gene expression in the three main types of endometrial cells at time planned for insemination and
6 implantation. Endometrial cell types (stromal, glandular and luminal epithelial cells) were isolated by
7 laser micro-dissection allowing the study of constitutive gene expression and their specific response to
8 NEB.

9 **Methods:** Nine Swedish Red cows receiving a control diet or a mild restricted diet to induce differences of energy balance were categorized into mild (MNEB, n = 5) and severe negative energy balance (SNEB, n = 4). The three endometrial cell types: luminal (LE), glandular (GE) epithelium and stroma (ST) were collected by laser microdissection from endometrial biopsies performed at 80 days postpartum.

14 **Results:** Transcriptome profiles obtained by RNA sequencing revealed differences in constitutive gene expression between the three cells types and also differences in specific responses related to the severity of NEB. Number of differentially expressed genes between SNEB and MNEB cows was higher in ST than in LE and GE, respectively. SNEB was associated with differential expression of genes related to metabolic processes and embryo-maternal interactions in ST. Under-expression of genes related to cell structure was found in GE whereas genes related to pro-inflammatory pathways were over-expressed. Genes associated to adaptive immunity were under-expressed in LE.

21 **Conclusion:** The three different main cells types of the endometrium, have very different patterns of gene expression. The severity of NEB after calving is associated with changes in gene expression at time of breeding. Specific alterations in GEs are associated with activation of pro-inflammatory mechanisms. Concomitantly, changes in the expression of genes related to cell to cell interactions and maternal recognition of pregnancy takes place in ST. The combination of these effects possibly
1 Abstract

2 **Background:** In postpartum dairy cows, the energy needs to satisfy high milk production induces a
3 more or less pronounced Negative Energy Balance (NEB) status. NEB associated with fat
4 mobilization impairs reproductive function. This study investigated the specific impact of NEB on
5 gene expression in the three main types of endometrial cells at time planned for insemination and
6 implantation. Endometrial cell types (stromal, glandular and luminal epithelial cells) were isolated by
7 laser micro-dissection allowing the study of constitutive gene expression and their specific response to
8 NEB.

9 **Methods:** Nine Swedish Red cows receiving a control diet or a mild restricted diet to induce differences of energy balance were categorized into mild (MNEB, n = 5) and severe negative energy balance (SNEB, n = 4). The three endometrial cell types: luminal (LE), glandular (GE) epithelium and stroma (ST) were collected by laser microdissection from endometrial biopsies performed at 80 days postpartum.

14 **Results:** Transcriptome profiles obtained by RNA sequencing revealed differences in constitutive gene expression between the three cells types and also differences in specific responses related to the severity of NEB. Number of differentially expressed genes between SNEB and MNEB cows was higher in ST than in LE and GE, respectively. SNEB was associated with differential expression of genes related to metabolic processes and embryo-maternal interactions in ST. Under-expression of genes related to cell structure was found in GE whereas genes related to pro-inflammatory pathways were over-expressed. Genes associated to adaptive immunity were under-expressed in LE.

21 **Conclusion:** The three different main cells types of the endometrium, have very different patterns of gene expression. The severity of NEB after calving is associated with changes in gene expression at time of breeding. Specific alterations in GEs are associated with activation of pro-inflammatory mechanisms. Concomitantly, changes in the expression of genes related to cell to cell interactions and maternal recognition of pregnancy takes place in ST. The combination of these effects possibly
1 altering the uterine environment and embryo maternal interactions may negatively influence the
2 establishment of pregnancy.

3 **Keywords:** negative energy balance, endometrial cells, transcriptome, laser microdissection, inflammation

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1 altering the uterine environment and embryo maternal interactions may negatively influence the
2 establishment of pregnancy.

3 **Keywords:** negative energy balance, endometrial cells, transcriptome, laser microdissection, inflammation

5
# 1 Background

The existence of common genetic and epigenetic factors that influence metabolic imbalance, milk production and reproductive performance have been raised for long [1] and are still an important topic in dairy cow industry [2]. A significant decrease in fertility due to genetic improvement for increasing milk production has been reported for decades in dairy cows [3, 4]. Despite a more balanced selection is applied nowadays [5], high milk-yield cows still meet strong negative energy balance (NEB) during the early postpartum period due to the high nutrient and energy demand for body metabolism, milk production, and body weight maintenance [6]. Energy deficiency and excessive lipid mobilization during the postpartum period have been reported to be the cause of unfavorable reproductive performances such as delayed ovarian activity [7], prolonged uterine involution period [8], retained placenta [9], endometritis [10], increased early embryonic losses and decreased conception rates [11].

Previous studies also showed the impacts of metabolic imbalance on gene expression in the endometrium during the early postpartum period [12, 13]. However, these studies were based on RNA prepared from biopsies taken from endometrial tissue sections without discriminating between different cell types. To our knowledge, constitutive gene expression and possible effects of metabolic imbalance on the response of specific endometrial cell types at time of conception remains to be deciphered.

The uterus is the site of intensive tissue remodeling during the estrous cycle, at time of implantation and placental development in response to the developing embryo [14]. Reciprocally, the control of the endometrium on embryo development steps has been recently documented in mice [15]. In the cow, histology of the endometrium shows a complex association of heterogeneous structures mainly consisting of luminal epithelial cells (LE), glandular epithelial cells (GE) as well as fibroblast-like stromal cells (ST) found in different proportions in caruncular and intercaruncular tissues [16]. These three cell types are functionally responsible for the embryo implantation process under the control of steroid hormones and act in different ways [17]. For instance, bovine uterine stromal cells synthesize and release prostaglandin E-2 (PGE-2), involved in maternal recognition of pregnancy, whereas epithelial cells contribute less to such changes in prostaglandin levels [18]. Uterine epithelial cells
# 1 Background

The existence of common genetic and epigenetic factors that influence metabolic imbalance, milk production and reproductive performance have been raised for long [1] and are still an important topic in dairy cow industry [2]. A significant decrease in fertility due to genetic improvement for increasing milk production has been reported for decades in dairy cows [3, 4]. Despite a more balanced selection is applied nowadays [5], high milk-yield cows still meet strong negative energy balance (NEB) during the early postpartum period due to the high nutrient and energy demand for body metabolism, milk production, and body weight maintenance [6]. Energy deficiency and excessive lipid mobilization during the postpartum period have been reported to be the cause of unfavorable reproductive performances such as delayed ovarian activity [7], prolonged uterine involution period [8], retained placenta [9], endometritis [10], increased early embryonic losses and decreased conception rates [11].

Previous studies also showed the impacts of metabolic imbalance on gene expression in the endometrium during the early postpartum period [12, 13]. However, these studies were based on RNA prepared from biopsies taken from endometrial tissue sections without discriminating between different cell types. To our knowledge, constitutive gene expression and possible effects of metabolic imbalance on the response of specific endometrial cell types at time of conception remains to be deciphered.

The uterus is the site of intensive tissue remodeling during the estrous cycle, at time of implantation and placental development in response to the developing embryo [14]. Reciprocally, the control of the endometrium on embryo development steps has been recently documented in mice [15]. In the cow, histology of the endometrium shows a complex association of heterogeneous structures mainly consisting of luminal epithelial cells (LE), glandular epithelial cells (GE) as well as fibroblast-like stromal cells (ST) found in different proportions in caruncular and intercaruncular tissues [16]. These three cell types are functionally responsible for the embryo implantation process under the control of steroid hormones and act in different ways [17]. For instance, bovine uterine stromal cells synthesize and release prostaglandin E-2 (PGE-2), involved in maternal recognition of pregnancy, whereas epithelial cells contribute less to such changes in prostaglandin levels [18]. Uterine epithelial cells
play key roles for the establishment and maintenance of pregnancy through activation of the innate immune system and secretion of chemokines [19] that support the recruitment and activation of immune cells directed against pathogens. Moreover, LE and GE exhibit unique molecular signatures having cooperative roles at time of establishment of pregnancy [16, 20, 21]. Their morphology [22] and biochemical activity [23] differs at time of implantation. RNA-sequencing of the complete transcriptome for the three cell types has been described for equine cells [24]. Laser capture microdissection (LCM) has also been successfully used to retrieve two different uterine epithelial cell types to define the transcriptome and proteomic analysis of the ovine and porcine endometrium, respectively [25, 26]. However, to our knowledge the transcriptomic profile of bovine endometrial cells has not yet been documented. Previously published research, regarding the impact of NEB on uterine function and endometrial transcriptome, suggests that NEB associated with elevated non-esterified fatty acids (NEFAs) concentrations induces infertility in postpartum cows through dysregulation of immune pathways [12]. However, the understanding of molecular changes induced by NEB from entire endometrial tissues is still unclear and difficult to interpret functionally as responses may be affected by other cell types such as endothelial cells, smooth muscle cells and leukocytes [27]. *In vitro* studies have clearly shown that NEFAs stimulate pro-inflammatory cytokine production and lipid accumulation of endometrial cells [28] and oviductal epithelial cells [29] but the results from these *in vitro* models need to be confirmed *in vivo*.

We hypothesized here that NEB may differentially influence the physiology of three endometrial cell types. The objectives of the present study were i) to investigate transcriptomic profiles of luminal epithelial cells, glandular epithelial cells and stromal cells which were harvested by LCM, and ii) identify possible differences in the profiles between cows diagnosed with either mild or severe NEB during the postpartum period. The collection of endometrial biopsies was performed at time of planned AI and the observed changes in gene expression suggest the existence of long-term impacts of NEB that are cell type-specific.
play key roles for the establishment and maintenance of pregnancy through activation of the innate immune system and secretion of chemokines [19] that support the recruitment and activation of immune cells directed against pathogens. Moreover, LE and GE exhibit unique molecular signatures having cooperative roles at time of establishment of pregnancy [16, 20, 21]. Their morphology [22] and biochemical activity [23] differs at time of implantation. RNA-sequencing of the complete transcriptome for the three cell types has been described for equine cells [24]. Laser capture microdissection (LCM) has also been successfully used to retrieve two different uterine epithelial cell types to define the transcriptome and proteomic analysis of the ovine and porcine endometrium, respectively [25, 26]. However, to our knowledge the transcriptomic profile of bovine endometrial cells has not yet been documented. Previously published research, regarding the impact of NEB on uterine function and endometrial transcriptome, suggests that NEB associated with elevated non-esterified fatty acids (NEFAs) concentrations induces infertility in postpartum cows through dysregulation of immune pathways [12]. However, the understanding of molecular changes induced by NEB from entire endometrial tissues is still unclear and difficult to interpret functionally as responses may be affected by other cell types such as endothelial cells, smooth muscle cells and leukocytes [27]. *In vitro* studies have clearly shown that NEFAs stimulate pro-inflammatory cytokine production and lipid accumulation of endometrial cells [28] and oviductal epithelial cells [29] but the results from these *in vitro* models need to be confirmed *in vivo*.

We hypothesized here that NEB may differentially influence the physiology of three endometrial cell types. The objectives of the present study were i) to investigate transcriptomic profiles of luminal epithelial cells, glandular epithelial cells and stromal cells which were harvested by LCM, and ii) identify possible differences in the profiles between cows diagnosed with either mild or severe NEB during the postpartum period. The collection of endometrial biopsies was performed at time of planned AI and the observed changes in gene expression suggest the existence of long-term impacts of NEB that are cell type-specific.
# 1 Results

# 2 Body condition score (BCS) and plasma NEFA concentrations.

The evolution of residual feed intake with post-partum time in the two groups of cows is presented in (Figure 1A). Throughout the full experimental period, the BCS of SRB cows in both NEB groups tended to decrease (p = 0.08). Mean BCS was 3.65 ± 0.25 at start of the experiment and 3.05 ± 0.22 at 120 days postpartum. However, NEB did not have a significant effect on BCS (data not shown).

Plasma NEFA concentrations did not differ between NEB groups over the full experimental period.

However, SNEB cows presented higher NEFA plasma concentrations compared to MNEB cows at Day 14 pre-partum and Day 14 post-partum (p < 0.05) (Figure.1B). BCS loss from 30 days pre-calving and 60 days post-calving was associated with the energy balance nadir (r = -0.68, p < 0.05).

NEFA concentrations tended to be significantly associated with the residual feed intake values (r = -0.28, p = 0.06).

# 13 RNA-Sequencing of cell type-specific samples collected by LCM.

The sequencing depth of RNA-seq libraries was in the range of 60 to 100 million reads per sample for each endometrial cell type. A total of 22915 transcripts with a unique Identifier were found. Salmon's method provides both read counts and TPM (transcripts per million), and the latter expression is more appropriate when comparing relative abundance between different cell types or tissues [30]. Before comparing the differences in gene expression between the endometrial cell types, transcripts whose average value computed from biological replicates were less than 10 TPM were regarded as biological background noise, partly independent of transcription regulation and discarded. The number of expressed genes detected (higher than 10 TPM) was 6622, 7814 and 8242 for luminal epithelial cells (LE), glandular epithelium (GE) and stromal cells (ST), respectively (Figure 2A). In the RNA-Seq analysis, the highest number of detectable expressed genes (8242) in the LCM datasets was obtained for ST and the lowest number of detectable genes (6622) was observed for LE. As displayed on the Venn diagram (Figure 2A), 5672 genes were expressed by all the three cell types. A total of 1236 genes were expressed exclusively by ST cells, which represents 15% of all genes expressed by this
# 1 Results

# 2 Body condition score (BCS) and plasma NEFA concentrations.

The evolution of residual feed intake with post-partum time in the two groups of cows is presented in (Figure 1A). Throughout the full experimental period, the BCS of SRB cows in both NEB groups tended to decrease (p = 0.08). Mean BCS was 3.65 ± 0.25 at start of the experiment and 3.05 ± 0.22 at 120 days postpartum. However, NEB did not have a significant effect on BCS (data not shown).

Plasma NEFA concentrations did not differ between NEB groups over the full experimental period.

However, SNEB cows presented higher NEFA plasma concentrations compared to MNEB cows at Day 14 pre-partum and Day 14 post-partum (p < 0.05) (Figure.1B). BCS loss from 30 days pre-calving and 60 days post-calving was associated with the energy balance nadir (r = -0.68, p < 0.05).

NEFA concentrations tended to be significantly associated with the residual feed intake values (r = -0.28, p = 0.06).

# 13 RNA-Sequencing of cell type-specific samples collected by LCM.

The sequencing depth of RNA-seq libraries was in the range of 60 to 100 million reads per sample for each endometrial cell type. A total of 22915 transcripts with a unique Identifier were found. Salmon's method provides both read counts and TPM (transcripts per million), and the latter expression is more appropriate when comparing relative abundance between different cell types or tissues [30]. Before comparing the differences in gene expression between the endometrial cell types, transcripts whose average value computed from biological replicates were less than 10 TPM were regarded as biological background noise, partly independent of transcription regulation and discarded. The number of expressed genes detected (higher than 10 TPM) was 6622, 7814 and 8242 for luminal epithelial cells (LE), glandular epithelium (GE) and stromal cells (ST), respectively (Figure 2A). In the RNA-Seq analysis, the highest number of detectable expressed genes (8242) in the LCM datasets was obtained for ST and the lowest number of detectable genes (6622) was observed for LE. As displayed on the Venn diagram (Figure 2A), 5672 genes were expressed by all the three cell types. A total of 1236 genes were expressed exclusively by ST cells, which represents 15% of all genes expressed by this
type of cell, while only 551 (7% of all genes expressed) transcripts were specific to the GE cells and 330 (5%) transcripts specific to LE cell. The lists of genes specifically expressed by each cell type are provided in additional file (TableS1_TS1-LE_TS2-GE_TS3-ST.xlsx). An overview of the GO terms associated to genes specifically expressed by each cellular type is visualized in Figure 3. The list of 5672 genes expressed in common between the three cell types was used as a reference list for PANTHER overrepresentation tests. Over and under-represented GO terms for biological process were visualized using REVIGO algorithm to reduce term redundancy (corresponding tables of GO terms are provided in additional file (TableS2_GO-REVIGO05_TS1-TS2-TS3.xlsx)). Respectively 97, 14 and 13 clusters of GO terms were over-represented in ST, GE and LE cells whereas 45, 11 and 8 were under-represented. Numerous metabolic processes were under-represented in the three lists of genes specifically expressed by each cell type which means that the genes involved in metabolism are shared genes. For ST, over-represented biological processes included many regulation processes and response to stimulus, cell communication and cell adhesion, extracellular matrix organization as well as developmental process and wound healing. For GE, cilium organization, cilium movement, protein localization to cilium and microtubule-based process were only the four main biological processes enriched. For LE, over-represented biological processes were enzyme linked receptor protein signalling pathway, cell-substrate adhesion, circulatory system process and activation of adenylate cyclase activity.

Heatmap (Figure 2B) illustrates hierarchical clustering obtained with samples and genes. The clustering unambiguously joins samples of each cell-type. The most expressed genes for each cell type are highlighted and framed by boxes (Figure 2B). The corresponding statistical analyses revealed that 8360 genes were differentially expressed (adjusted p value < 0.05) between GE and LE cells (2666 genes greater expressed in GE vs. 5694 in LE). The expression of 10761 genes differs between ST and LE (4298 genes more expressed in ST vs. 6463 more expressed in LE). The level of expression of 10003 genes differs between GE and ST (2900 genes more expressed in GE vs. 7103 in ST).
type of cell, while only 551 (7% of all genes expressed) transcripts were specific to the GE cells and 330 (5%) transcripts specific to LE cell. The lists of genes specifically expressed by each cell type are provided in additional file (TableS1_TS1-LE_TS2-GE_TS3-ST.xlsx). An overview of the GO terms associated to genes specifically expressed by each cellular type is visualized in Figure 3. The list of 5672 genes expressed in common between the three cell types was used as a reference list for PANTHER overrepresentation tests. Over and under-represented GO terms for biological process were visualized using REVIGO algorithm to reduce term redundancy (corresponding tables of GO terms are provided in additional file (TableS2_GO-REVIGO05_TS1-TS2-TS3.xlsx)). Respectively 97, 14 and 13 clusters of GO terms were over-represented in ST, GE and LE cells whereas 45, 11 and 8 were under-represented. Numerous metabolic processes were under-represented in the three lists of genes specifically expressed by each cell type which means that the genes involved in metabolism are shared genes. For ST, over-represented biological processes included many regulation processes and response to stimulus, cell communication and cell adhesion, extracellular matrix organization as well as developmental process and wound healing. For GE, cilium organization, cilium movement, protein localization to cilium and microtubule-based process were only the four main biological processes enriched. For LE, over-represented biological processes were enzyme linked receptor protein signalling pathway, cell-substrate adhesion, circulatory system process and activation of adenylate cyclase activity.

Heatmap (Figure 2B) illustrates hierarchical clustering obtained with samples and genes. The clustering unambiguously joins samples of each cell-type. The most expressed genes for each cell type are highlighted and framed by boxes (Figure 2B). The corresponding statistical analyses revealed that 8360 genes were differentially expressed (adjusted p value < 0.05) between GE and LE cells (2666 genes greater expressed in GE vs. 5694 in LE). The expression of 10761 genes differs between ST and LE (4298 genes more expressed in ST vs. 6463 more expressed in LE). The level of expression of 10003 genes differs between GE and ST (2900 genes more expressed in GE vs. 7103 in ST).
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The principal component analysis also reveals a clear separation of the samples from the three cell types (Figure 2C). The first two dimensions explain 80% of the variability. The first dimension distinguishes epithelial cells from ST whereas the variation associated to the second dimension relates to differences of expression between GE and LE. Supplementary tables (Table_S3_PCA_tables.xlsx; sheets TS4 and TS5 for the first dimension, sheets TS6 and TS7 for the second dimension) show the most characteristic genes according to each dimension (correlation coefficient >|0.9| at p<0.01 for dimension 1 and >|0.8| at p<0.01 for dimension-2).

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Dimension-1 corresponds to a significant over-representation in ST of genes involved in extracellular matrix organization (GO: 0043062) and in integrin signalling pathway (P00034). These genes encode proteins that are compounds of the extracellular region (GO: 0005576) and are represented by an important group of collagen coding genes (*COL1A2*, *COL1A1*, *COL16A1*, *COL5A2*, *COL3A1*) and by *SULF1* and *ECM2*. Genes encoding proteins involved in protein binding, *CDH11*, *ADAMTS1*, *FAP*, *SERPING1* and *SFRP1*, are also associated to ST. Finally, a set of metallopeptidases and other proteases coding genes (such as *ARHGAP10*, *MMP9*, *MMP19*, *C1R* and *C1S*) that are complementary to the previous ones for hydrolase activity (GO: 0016787) and tissue remodeling are also more expressed in ST. Both GE and LE are characterized by an over-representation for a first group of genes involved in cell junction (GO: 0030054) including *EPCAM* (epithelial adhesion molecule), *CDH1* (cadherin-1), *ITGB6* (integrin beta 6), *DSP* (desmoplakin) and *MYO5B* (myosin-VB). Other genes encoding proteins involved in binding are associated with the epithelial type (*RHPN2*, *rhophilin-2*, *DYNC1I1*). Numerous genes over-expressed in epithelial cells are also closely associated to cellular response to stimulus (GO: 0051715) and signal transduction (GO: 0007165; *RAB25*, *F2RL1*, *ITGB6*, *LPAR3*, *KSR2* and *ERBB3*). In addition, a large number of genes are involved in catalytic activity (GO: 0003824) such as enzymes of metabolism *GPT2*, *PLA2G4A*, *AKR1B* and *IDH1*. Others genes are associated to EGF signalling pathway (P00018), cell proliferation (*MAPK13*, *PEBP4*, *ERBB3*, *CCNA1* and *RAB25*) and transcription regulator activity (GO: 0140110; *DLX5*, *IRF6*, *KLF5*, *OCLN*, *HNF1B* and *EHF*).

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The principal component analysis also reveals a clear separation of the samples from the three cell types (Figure 2C). The first two dimensions explain 80% of the variability. The first dimension distinguishes epithelial cells from ST whereas the variation associated to the second dimension relates to differences of expression between GE and LE. Supplementary tables (Table_S3_PCA_tables.xlsx; sheets TS4 and TS5 for the first dimension, sheets TS6 and TS7 for the second dimension) show the most characteristic genes according to each dimension (correlation coefficient >|0.9| at p<0.01 for dimension 1 and >|0.8| at p<0.01 for dimension-2).

Dimension-1 corresponds to a significant over-representation in ST of genes involved in extracellular matrix organization (GO: 0043062) and in integrin signalling pathway (P00034). These genes encode proteins that are compounds of the extracellular region (GO: 0005576) and are represented by an important group of collagen coding genes (*COL1A2*, *COL1A1*, *COL16A1*, *COL5A2*, *COL3A1*) and by *SULF1* and *ECM2*. Genes encoding proteins involved in protein binding, *CDH11*, *ADAMTS1*, *FAP*, *SERPING1* and *SFRP1*, are also associated to ST. Finally, a set of metallopeptidases and other proteases coding genes (such as *ARHGAP10*, *MMP9*, *MMP19*, *C1R* and *C1S*) that are complementary to the previous ones for hydrolase activity (GO: 0016787) and tissue remodeling are also more expressed in ST. Both GE and LE are characterized by an over-representation for a first group of genes involved in cell junction (GO: 0030054) including *EPCAM* (epithelial adhesion molecule), *CDH1* (cadherin-1), *ITGB6* (integrin beta 6), *DSP* (desmoplakin) and *MYO5B* (myosin-VB). Other genes encoding proteins involved in binding are associated with the epithelial type (*RHPN2*, *rhophilin-2*, *DYNC1I1*). Numerous genes over-expressed in epithelial cells are also closely associated to cellular response to stimulus (GO: 0051715) and signal transduction (GO: 0007165; *RAB25*, *F2RL1*, *ITGB6*, *LPAR3*, *KSR2* and *ERBB3*). In addition, a large number of genes are involved in catalytic activity (GO: 0003824) such as enzymes of metabolism *GPT2*, *PLA2G4A*, *AKR1B* and *IDH1*. Others genes are associated to EGF signalling pathway (P00018), cell proliferation (*MAPK13*, *PEBP4*, *ERBB3*, *CCNA1* and *RAB25*) and transcription regulator activity (GO: 0140110; *DLX5*, *IRF6*, *KLF5*, *OCLN*, *HNF1B* and *EHF*).
When analyzing differences in expression between types of cells related to the second dimension, a set of 69 genes is over-represented in GE vs LE. An important part of these genes associates to structural cell organization. This includes genes such as actin-binding *VIL1* (villin-1) and numerous other encoding proteins involved in microtubule organization (GO: 0007017) including members of the dynein complex *DNAH5* (dynein heavy chain 5), *WDR63* (wd repeat containing protein 63), *CCDC65* (dynein regulatory complex subunit), *DRC1* (dynein regulatory complex protein 1) and *RSPH4A* (radial spoke head protein 4). In this category, one gap junction (*GJB5*) and 2 tight junctions (*CLDN10* and *CLDN8*) are specifically over-expressed in glandular epithelial cells. A complementary set of genes over-represented in GE relates also to binding (GO: 0005488) including protein binding (GO: 0005515), signalling receptor binding (GO: 00051102) and calcium binding (*IHH*), *WIF1* and *S100B*. Relatively few genes were more expressed in LE, the majority of them coding for proteins with catalytic activity (GO: 0003824) including hydrolase (*BACE2*, *RCAN1*, *TINAGL* and *LCAT*) and transferase (*GPCRC5A* and *LCAT*) activities. LE are also enriched in specific receptor related G-protein such as *HCRT1* and *GPRC5A* (G-protein coupled receptors for orexin and retinoic acid) and receptor *SFRP4* which modulates Wnt signalling.

## Differential gene expression between the three endometrial cell types in NEB cows

The principal component analysis reveals differences in gene expression patterns in MNEB and SNEB cows for the three cell types (Figure 4A). A clear separation between samples issued from the two groups of cows is observed in ST, whereas overlapping gene expression patterns appears in GE and LE. The numbers of differentially expressed genes between MNEB and SNEB cows for each endometrial cell type are given in Table 1 and in the Venn diagram (Figure 4B). The total number of DEGs in ST, GE and LE when comparing SNEB cows to MNEB cows were 1049, 24 and 52.
1 When analyzing differences in expression between types of cells related to the second dimension, a
2 set of 69 genes is over-represented in GE vs LE. An important part of these genes associates to
3 structural cell organization. This includes genes such as actin-binding VIL1 (villin-1) and numerous
4 other encoding proteins involved in microtubule organization (GO: 0007017) including members of
5 the dynein complex DNAH5 (dynein heavy chain 5), WDR63 (wd repeat containing protein 63),
6 CCDC65 (dynein regulatory complex subunit), DRC1 (dynein regulatory complex protein 1) and
7 RSPH4A (radial spoke head protein 4). In this category, one gap junction (GJB5) and 2 tight junctions
8 (CLDN10 and CLDN8) are specifically over-expressed in glandular epithelial cells. A complementary
9 set of genes over-represented in GE relates also to binding (GO: 0005488) including protein binding
10 (GO: 0005515), signalling receptor binding (GO: 00051102) and calcium binding (IHH), WIF1 and
11 S100B. Relatively few genes were more expressed in LE, the majority of them coding for proteins
12 with catalytic activity (GO: 0003824) including hydrolase (BACE2, RCAN1, TINAGL and LCAT) and
13 transferase (GPCRC5A and LCAT) activities. LE are also enriched in specific receptor related G-
14 protein such as HCRTR1 and GPRC5A (G-protein coupled receptors for orexin and retinoic acid) and
15 receptor SFRP4 which modulates Wnt signalling.

16 Differential gene expression between the three endometrial cell types in NEB cows

17 The principal component analysis reveals differences in gene expression patterns in MNEB and
18 SNEB cows for the three cell types (Figure 4A). A clear separation between samples issued from the
19 two groups of cows is observed in ST, whereas overlapping gene expression patterns appears in GE
20 and LE. The numbers of differentially expressed genes between MNEB and SNEB cows for each
21 endometrial cell type are given in Table 1 and in the Venn diagram (Figure 4B). The total number of
22 DEGs in ST, GE and LE when comparing SNEB cows to MNEB cows were 1049, 24 and 52.

23

24

25
<table><caption>**Table 1: Number of DEGs, which were identified as being over- or under-expressed, presented in specific endometrial cell types (ST, GE and LE) of SNEB cows when compared to MNEB cows**</caption><thead><tr><th rowspan="2">Expression</th><th colspan="3">Cell types</th></tr><tr><th>ST</th><th>GE</th><th>LE</th></tr></thead><tbody><tr><td>Over</td><td>751</td><td>15</td><td>1</td></tr><tr><td>Under</td><td>298</td><td>9</td><td>51</td></tr><tr><td>Total</td><td>1049</td><td>24</td><td>52</td></tr></tbody></table>

Seven DEGs are found as common in ST and GE: BTG Anti-Proliferation Factor 2 (BTG2), Lymphocyte Antigen 6 Family Member G6C (LY6G6C), C-C Motif Chemokine Ligand 4 (CCL4) and JunB Proto-Oncogene, AP-1 Transcription Factor Subunit (JUNB), chemokine (C-C motif) ligand 3 (CCL3), chromobox protein homolog 1 and one pseudogene (ENSBTAG00000047824). Three DEGs are common between ST and LE: CRK Proto-Oncogene, Adaptor Protein (CRK), Plexin Domain Containing 1 (PLXDC) and Myotubularin related protein 10 (MTMR10). None of the genes are common to all three cell types. The list of over- and under-expressed mRNAs in ST, GE and LE are given in sheets S8, S9 and S10 respectively of the additional file (TableS4_TS8_TS9_TS10_DEG-SNEBvsMNEB.xlsx). In SNEB animals, a large proportion of DEGs were identified as over-expressed in ST (72%) and GE (63%) whereas almost all DEGs were under-expressed in LE (98%) (Table 1). An overview of the differential patterns of gene expression in ST, GE, and LE obtained by LCM between SNEB and MNEB cows are illustrated in volcano plots (Figure 5A to 5C).

**Under-expressed genes in ST (Table 2 and supplemental TableS5_david_ST-underexpressed.pdf)**

Either by using the statistical over-representation test from PANTHER with reactome pathways annotation or by browsing pathways ontology classification, the analysis detected four main significant pathways from the 298 under-expressed genes. A first group of genes encode proteins that are involved in the regulation of interferon signalling as well as in inflammation mediated by chemokine and cytokine (P00031) (RAPGEF1, MX1, EIF2AK2, UBA7, ISG15, PTPN2, MX2,
<table><caption>**Table 1: Number of DEGs, which were identified as being over- or under-expressed, presented in specific endometrial cell types (ST, GE and LE) of SNEB cows when compared to MNEB cows**</caption><thead><tr><th rowspan="2">Expression</th><th colspan="3">Cell types</th></tr><tr><th>ST</th><th>GE</th><th>LE</th></tr></thead><tbody><tr><td>Over</td><td>751</td><td>15</td><td>1</td></tr><tr><td>Under</td><td>298</td><td>9</td><td>51</td></tr><tr><td>Total</td><td>1049</td><td>24</td><td>52</td></tr></tbody></table>

Seven DEGs are found as common in ST and GE: BTG Anti-Proliferation Factor 2 (BTG2), Lymphocyte Antigen 6 Family Member G6C (LY6G6C), C-C Motif Chemokine Ligand 4 (CCL4) and JunB Proto-Oncogene, AP-1 Transcription Factor Subunit (JUNB), chemokine (C-C motif) ligand 3 (CCL3), chromobox protein homolog 1 and one pseudogene (ENSBTAG00000047824). Three DEGs are common between ST and LE: CRK Proto-Oncogene, Adaptor Protein (CRK), Plexin Domain Containing 1 (PLXDC) and Myotubularin related protein 10 (MTMR10). None of the genes are common to all three cell types. The list of over- and under-expressed mRNAs in ST, GE and LE are given in sheets S8, S9 and S10 respectively of the additional file (TableS4_TS8_TS9_TS10_DEG-SNEBvsMNEB.xlsx). In SNEB animals, a large proportion of DEGs were identified as over-expressed in ST (72%) and GE (63%) whereas almost all DEGs were under-expressed in LE (98%) (Table 1). An overview of the differential patterns of gene expression in ST, GE, and LE obtained by LCM between SNEB and MNEB cows are illustrated in volcano plots (Figure 5A to 5C).

**Under-expressed genes in ST (Table 2 and supplemental TableS5_david_ST-underexpressed.pdf)**

Either by using the statistical over-representation test from PANTHER with reactome pathways annotation or by browsing pathways ontology classification, the analysis detected four main significant pathways from the 298 under-expressed genes. A first group of genes encode proteins that are involved in the regulation of interferon signalling as well as in inflammation mediated by chemokine and cytokine (P00031) (RAPGEF1, MX1, EIF2AK2, UBA7, ISG15, PTPN2, MX2,
1 DDX58, IL1RAP, IL16, CRK, IFIT1, STAT1, IFNGR2, JAK1, STX3, NFATC1 and ALOX12). A
2 second important group of under-expressed genes code for proteins with functions associated with the
3 extracellular matrix and its degradation (KLK1, TPSB1, COL4A4, COL2A1, MMP19, NID1, COL6A6,
4 COL4A3 and COL26A1). A third group of genes code for proteins related to Wnt signalling pathway
5 (P00057) (CDH11, TLE4, LEF1, NFATC1, PRKCH, SMARCD2 and FBXW7). In addition, genes of
6 integrin signalling pathway (P00034) are over-represented including ITGA5, ITGA10, RAPGEF1,
7 MAP3K5 and CRK. Around 10% of under-expressed genes in ST from SNEB animals are genes
8 involved in signal transduction (GO: 0007165) and cellular response to stimulus (GO: 0051716).

9

<table><thead><tr><th></th><th>annotation terms</th><th>genes (number)</th></tr></thead><tbody><tr><td rowspan="10">PANTHER pathways</td><td>Regulation of IFNG signaling (R-BTA-877312)</td><td></td></tr><tr><td>Cytokine Signaling in Immune system (R-BTA-1280215)</td><td>16*</td></tr><tr><td>Antiviral mechanism by IFN-stimulated genes (R-BTA-1169410)</td><td></td></tr><tr><td>Extracellular matrix organisation (R-BTA-1474244)</td><td>9*</td></tr><tr><td>Collagen chain trimerization (R-BTA-8948216)</td><td></td></tr><tr><td>Wnt signaling pathway (P00057)</td><td>8</td></tr><tr><td>Integrin signaling pathway (P00034)</td><td>9</td></tr><tr><td>Cadherin signaling pathway (P00012)</td><td>6</td></tr><tr><td>Apoptosis signaling pathway (P00006)</td><td>4</td></tr><tr><td rowspan="10">PANTHER molecular function</td><td>Binding (GO:0005488)</td><td>80</td></tr><tr><td>&gt; protein binding (GO: 0005515)</td><td>53</td></tr><tr><td>&gt;cytoskeletal protein binding (GO: 0008092)</td><td>10</td></tr><tr><td>&gt;signaling receptor binding (GO: 0005102)</td><td>9</td></tr><tr><td>&gt;enzyme binding (GO: 0019899)</td><td>7</td></tr><tr><td>&gt;cell adhesion molecule binding (GO: 0050839)</td><td>5</td></tr><tr><td>Catalytic activity (GO:0003824)</td><td>83</td></tr><tr><td>&gt;transferase activity (GO: 0016740)</td><td>36</td></tr><tr><td>&gt;hydrolase activity (GO: 0016787)</td><td>35</td></tr><tr><td rowspan="3">DAVID (6.8) clusters</td><td>cluster 1</td><td>GO:0005887, integral component of plasma membrane<br>G-protein coupled receptor</td><td>9</td></tr><tr><td>cluster 2</td><td>Immunoglobulin-like domain</td><td>6</td></tr><tr><td>cluster 3</td><td>GO: 0016021 integral component of membrane</td><td>27</td></tr></tbody></table>

10

<caption>Table 2: Gene Functional Classification Result (PANTHER 14.1 and DAVID 6.8) of under-expressed genes in ST cells from SNEB animals. Main pathways and ontology annotation groups are shown. Asterix * indicates the significant (FDR P<0.05) over-representation statistical test.</caption>

14
1 DDX58, IL1RAP, IL16, CRK, IFIT1, STAT1, IFNGR2, JAK1, STX3, NFATC1 and ALOX12). A
2 second important group of under-expressed genes code for proteins with functions associated with the
3 extracellular matrix and its degradation (KLK1, TPSB1, COL4A4, COL2A1, MMP19, NID1, COL6A6,
4 COL4A3 and COL26A1). A third group of genes code for proteins related to Wnt signalling pathway
5 (P00057) (CDH11, TLE4, LEF1, NFATC1, PRKCH, SMARCD2 and FBXW7). In addition, genes of
6 integrin signalling pathway (P00034) are over-represented including ITGA5, ITGA10, RAPGEF1,
7 MAP3K5 and CRK. Around 10% of under-expressed genes in ST from SNEB animals are genes
8 involved in signal transduction (GO: 0007165) and cellular response to stimulus (GO: 0051716).

9

<table><thead><tr><th></th><th>annotation terms</th><th>genes (number)</th></tr></thead><tbody><tr><td rowspan="10">PANTHER pathways</td><td>Regulation of IFNG signaling (R-BTA-877312)</td><td></td></tr><tr><td>Cytokine Signaling in Immune system (R-BTA-1280215)</td><td>16*</td></tr><tr><td>Antiviral mechanism by IFN-stimulated genes (R-BTA-1169410)</td><td></td></tr><tr><td>Extracellular matrix organisation (R-BTA-1474244)</td><td></td></tr><tr><td>Collagen chain trimerization (R-BTA-8948216)</td><td>9*</td></tr><tr><td>Wnt signaling pathway (P00057)</td><td>8</td></tr><tr><td>Integrin signaling pathway (P00034)</td><td>9</td></tr><tr><td>Cadherin signaling pathway (P00012)</td><td>6</td></tr><tr><td>Apoptosis signaling pathway (P00006)</td><td>4</td></tr><tr><td rowspan="10">PANTHER molecular function</td><td>Binding (GO:0005488)</td><td>80</td></tr><tr><td>&gt; protein binding (GO: 0005515)</td><td>53</td></tr><tr><td>&gt;cytoskeletal protein binding (GO: 0008092)</td><td>10</td></tr><tr><td>&gt;signaling receptor binding (GO: 0005102)</td><td>9</td></tr><tr><td>&gt;enzyme binding (GO: 0019899)</td><td>7</td></tr><tr><td>&gt;cell adhesion molecule binding (GO: 0050839)</td><td>5</td></tr><tr><td>Catalytic activity (GO:0003824)</td><td>83</td></tr><tr><td>&gt;transferase activity (GO: 0016740)</td><td>36</td></tr><tr><td>&gt;hydrolase activity (GO: 0016787)</td><td>35</td></tr><tr><td rowspan="3">DAVID (6.8) clusters</td><td>cluster 1</td><td>GO:0005887, integral component of plasma membrane<br>G-protein coupled receptor</td><td>9</td></tr><tr><td>cluster 2</td><td>Immunoglobulin-like domain</td><td>6</td></tr><tr><td>cluster 3</td><td>GO: 0016021 integral component of membrane</td><td>27</td></tr></tbody></table>

10

<caption>Table 2: Gene Functional Classification Result (PANTHER 14.1 and DAVID 6.8) of under-expressed genes in ST cells from SNEB animals. Main pathways and ontology annotation groups are shown. Asterix * indicates the significant (FDR P<0.05) over-representation statistical test.</caption>

14
1

Functional classification using DAVID identifies also a first cluster of nine genes encoding proteins including mainly G-protein coupled receptors (GO: 0005887; integral component of plasma membrane), which were under-expressed in ST from SNEB. Six genes encoding membrane proteins with immunoglobulin-like domains and related to cytokine are part of a second cluster and a last group includes 28 genes coding for component of membrane.

2

3

4

5

6

Over-expressed genes in ST (Table 3 and additional TableS6_david_ST-overexpressed.pdf)

7

8

9

10

11

12

13

14

15

16

17

18

The analysis from the GO molecular function annotation of PANTHER database indicates that 50% of the over-expressed genes from SNEB ST samples are distributed in three main categories: binding (GO:0005488) (n=186), catalytic activity (GO: 0003824) (n=130) and transporter activity (GO:0005215) (n=52). Binding categories includes cytoskeletal protein binding (GO: 0008092) (n=17), enzyme binding (GO: 0019899) (n=24) and signalling receptor binding (GO: 0005102)(n=21). Catalytic activity class includes genes involved in hydrolase activity (GO: 0016787) (n=57) and transferase activity (GO: 0016740) (n=47). In the transporter activity category 92% of genes are related to transmembrane transporter activity (GO: 0022857) and 8% to lipid transporter activity (GO: 0005319). Considering the PANTHER classification based on biological process annotation, the most frequently reported GO terms are cellular process (GO: 0009987; n=230), cell proliferation (GO: 0008283; n=105), metabolic process (GO: 0008152; n=101) and localization (GO: 0051179; n=72).

19

20

21

22

23

24

25

The analysis from PANTHER pathways revealed that genes from three significant pathways are over-represented in ST from SNEB vs MNEB cows including: (i) genes related to inflammation mediated by chemokine and cytokine signalling pathway (P00031; *CAMK2B*, *PLCB4*, *PRKCZ*, *PAK4*, *MYH14*, *JUNB*, *ACTA1*, *MYH11*, *CCL4*, *CCL3*, *ITPR2*, *PLCH1* and *CCL11*), (ii) genes involved in Wnt signalling pathway (P00057; *FZD5*, *PLCB4*, *CDH3*, *PRKCZ*, *CDH1*, *ACTA1*, *CTBP2*, *ITPR2*, *FRZB*, and *ANKRD6*) and (iii) genes associated to integrin signalling pathway (P00034; *ITGB4*, *FRK*, *RAP2A*, *ITGB6*, *FLNA*, *COL4A6*, *ACTA1*, *FLNB* and *COL4A5*). In addition, a positive enrichment
1

Functional classification using DAVID identifies also a first cluster of nine genes encoding proteins including mainly G-protein coupled receptors (GO: 0005887; integral component of plasma membrane), which were under-expressed in ST from SNEB. Six genes encoding membrane proteins with immunoglobulin-like domains and related to cytokine are part of a second cluster and a last group includes 28 genes coding for component of membrane.

2

3

4

5

6

Over-expressed genes in ST (Table 3 and additional TableS6_david_ST-overexpressed.pdf)

7

8

9

10

11

12

13

14

15

16

17

18

The analysis from the GO molecular function annotation of PANTHER database indicates that 50% of the over-expressed genes from SNEB ST samples are distributed in three main categories: binding (GO:0005488) (n=186), catalytic activity (GO: 0003824) (n=130) and transporter activity (GO:0005215) (n=52). Binding categories includes cytoskeletal protein binding (GO: 0008092) (n=17), enzyme binding (GO: 0019899) (n=24) and signalling receptor binding (GO: 0005102)(n=21). Catalytic activity class includes genes involved in hydrolase activity (GO: 0016787) (n=57) and transferase activity (GO: 0016740) (n=47). In the transporter activity category 92% of genes are related to transmembrane transporter activity (GO: 0022857) and 8% to lipid transporter activity (GO: 0005319). Considering the PANTHER classification based on biological process annotation, the most frequently reported GO terms are cellular process (GO: 0009987; n=230), cell proliferation (GO: 0008283; n=105), metabolic process (GO: 0008152; n=101) and localization (GO: 0051179; n=72).

19

20

21

22

23

24

25

The analysis from PANTHER pathways revealed that genes from three significant pathways are over-represented in ST from SNEB vs MNEB cows including: (i) genes related to inflammation mediated by chemokine and cytokine signalling pathway (P00031; *CAMK2B*, *PLCB4*, *PRKCZ*, *PAK4*, *MYH14*, *JUNB*, *ACTA1*, *MYH11*, *CCL4*, *CCL3*, *ITPR2*, *PLCH1* and *CCL11*), (ii) genes involved in Wnt signalling pathway (P00057; *FZD5*, *PLCB4*, *CDH3*, *PRKCZ*, *CDH1*, *ACTA1*, *CTBP2*, *ITPR2*, *FRZB*, and *ANKRD6*) and (iii) genes associated to integrin signalling pathway (P00034; *ITGB4*, *FRK*, *RAP2A*, *ITGB6*, *FLNA*, *COL4A6*, *ACTA1*, *FLNB* and *COL4A5*). In addition, a positive enrichment
1

was detected for genes related to the sequestration of calcium ion (GO: 0015278) and for genes related to cytoskeleton, dynein complex and axoneme.

2

3

Using medium stringency for functional classification of genes, DAVID further identified 15 clusters.

4

According to ranking from enrichment score the top 11 main clusters group include (i) five genes involved in microtubule and axoneme assembly (GO: 0005874, microtubule; cilium; axoneme), (ii)

5

6

15 genes related to homeodomain (GO: 0043565), (iii) four genes of myosin complex (GO:0016459),

7

(iv) 6 genes for calcium ion binding, (v) nine genes related to ankirin repeat, (vi) five genes for

8

regulation of Rho protein signal transduction (GO: 0005089), (vii) 13 genes related to extracellular

9

region of the cell, (viii) seven genes for nucleotide and mRNA binding (GO: 0000166), (ix) four

10

genes for protein kinase activity (GO: 0004672), (x) 11 genes related to products being integral

11

components of plasma membrane (GO: 0005887) and (xi) 109 genes coding for membrane associated

12

proteins (GO: 0016021).

<table><thead><tr><th></th><th>annotation terms</th><th>genes (number)</th></tr></thead><tbody><tr><td rowspan="9">PANTHER pathways</td><td>Inflammation mediated by chemokine and cytokine signaling pathway (P00031)</td><td>15</td></tr><tr><td>Wnt signaling pathway (P00057)</td><td>12</td></tr><tr><td>Integrin signalling pathway (P00034)</td><td>11</td></tr><tr><td>protein binding</td><td>121</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;&gt;cytoskeletal protein binding (GO: 0008092)</td><td>18</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;&gt;enzyme binding (GO: 0019899)</td><td>25</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;&gt;signaling receptor binding (GO: 0005102)</td><td>22</td></tr><tr><td>catalytic activity</td><td>130</td></tr><tr><td rowspan="10">PANTHER molecular function</td><td>&nbsp;&nbsp;&nbsp;&nbsp;&gt;hydrolase activity (GO: 0016787)</td><td>57</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;&gt;transferase activity (GO: 0016740)</td><td>47</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;&gt;oxidoreductase activity (GO: 0016491)</td><td>17</td></tr><tr><td>transporter activity (GO: 0005215)</td><td>52</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;&gt;transmembrane transporter activity (GO: 0022857)</td><td>48</td></tr><tr><td>calcium-release channel activity (GO: 0015278)</td><td>5*</td></tr><tr><td>sequestering of calcium ion (GO: 0051208)</td><td>8*</td></tr><tr><td>plasma membrane bounded cell projection cytoplasm (GO: 0032838)</td><td>10*</td></tr><tr><td>PANTHER cellular component</td><td>cytoskeleton (GO: 0005856)</td><td>45*</td></tr><tr><td>DAVID (6.8)</td><td>cluster 1 microtubule; Cilium; axoneme (GO: 0005874)</td><td>5</td></tr></tbody></table>
1

was detected for genes related to the sequestration of calcium ion (GO: 0015278) and for genes related to cytoskeleton, dynein complex and axoneme.

2

3

Using medium stringency for functional classification of genes, DAVID further identified 15 clusters.

4

According to ranking from enrichment score the top 11 main clusters group include (i) five genes involved in microtubule and axoneme assembly (GO: 0005874, microtubule; cilium; axoneme), (ii)

5

6

15 genes related to homeodomain (GO: 0043565), (iii) four genes of myosin complex (GO:0016459),

7

(iv) 6 genes for calcium ion binding, (v) nine genes related to ankirin repeat, (vi) five genes for

8

regulation of Rho protein signal transduction (GO: 0005089), (vii) 13 genes related to extracellular

9

region of the cell, (viii) seven genes for nucleotide and mRNA binding (GO: 0000166), (ix) four

10

genes for protein kinase activity (GO: 0004672), (x) 11 genes related to products being integral

11

components of plasma membrane (GO: 0005887) and (xi) 109 genes coding for membrane associated

12

proteins (GO: 0016021).

<table><thead><tr><th></th><th>annotation terms</th><th>genes (number)</th></tr></thead><tbody><tr><td rowspan="8">PANTHER pathways</td><td>Inflammation mediated by chemokine and cytokine signaling pathway (P00031)</td><td>15</td></tr><tr><td>Wnt signaling pathway (P00057)</td><td>12</td></tr><tr><td>Integrin signalling pathway (P00034)</td><td>11</td></tr><tr><td>protein binding</td><td>121</td></tr><tr><td>&gt;cytoskeletal protein binding (GO: 0008092)</td><td>18</td></tr><tr><td>&gt;enzyme binding (GO: 0019899)</td><td>25</td></tr><tr><td>&gt;signaling receptor binding (GO: 0005102)</td><td>22</td></tr><tr><td>catalytic activity</td><td>130</td></tr><tr><td rowspan="10">PANTHER molecular function</td><td>&gt;hydrolase activity (GO: 0016787)</td><td>57</td></tr><tr><td>&gt;transferase activity (GO: 0016740)</td><td>47</td></tr><tr><td>&gt;oxidoreductase activity (GO: 0016491)</td><td>17</td></tr><tr><td>transporter activity (GO: 0005215)</td><td>52</td></tr><tr><td>&gt;transmembrane transporter activity (GO: 0022857)</td><td>48</td></tr><tr><td>calcium-release channel activity (GO: 0015278)</td><td>5*</td></tr><tr><td>sequestering of calcium ion (GO: 0051208)</td><td>8*</td></tr><tr><td>plasma membrane bounded cell projection cytoplasm (GO: 0032838)</td><td>10*</td></tr><tr><td>PANTHER cellular component</td><td>cytoskeleton (GO: 0005856)</td><td>45*</td></tr><tr><td>DAVID (6.8)</td><td>cluster 1 microtubule; Cilium; axoneme (GO: 0005874)</td><td>5</td></tr></tbody></table>
<table><caption>Table 3: Gene Functional Classification Result (PANTHER 14.1 and DAVID 6.8) of over-expressed genes in ST cells from SNEB animals. Main pathways and ontology annotation groups are shown. Asterix * indicates the significant (FDR P<0.05) over-representation statistical test (only positive enrichment is shown)</caption><thead><tr><th>clusters</th><th></th><th></th><th></th></tr></thead><tbody><tr><td></td><td>cluster 2</td><td>sequence-specific DNA binding; Homeodomain (GO: 0043565)</td><td>15</td></tr><tr><td></td><td>cluster 3</td><td>myosin complex (GO: 0016459)</td><td>4</td></tr><tr><td></td><td>cluster 4</td><td>calcium ion binding (GO: 0005509)</td><td>6</td></tr><tr><td></td><td>cluster 5</td><td>Ankyrin repeat</td><td>9</td></tr><tr><td></td><td>cluster 6</td><td>Rho guanyl-nucleotide exchange factor activity (GO: 0005089)</td><td>5</td></tr><tr><td></td><td>cluster 7</td><td>extracellular region (GO: 0005576)</td><td>13</td></tr><tr><td></td><td>cluster 8</td><td>nucleotide binding; RNA recognition motif domain (GO: 0000166)</td><td>7</td></tr><tr><td></td><td>cluster 9</td><td>protein kinase activity (GO: 0004672)</td><td>4</td></tr><tr><td></td><td>cluster 10</td><td>integral component of membrane (GO: 0016021)</td><td>130</td></tr><tr><td></td><td>cluster 11</td><td>GTP binding (GO: 000552)</td><td>4</td></tr><tr><td></td><td>cluster 12</td><td>Immunoglobulin-like domain</td><td>7</td></tr></tbody></table>

Differential expression in GE (Table 4, Table 5 and additional TableS7_david_GE-overexpressed.pdf)

Only seven known genes are under-expressed in GE cells from SNEB cows when compared to MNEB ones (CDH18, PPP1R1C, LY6G6C, MT1E, ASB16, PROM2 and SESN2). Four are related to binding functions (CDH18, PROM2, SESN and MT1E) and/or to cell surface component (CDH18, PROM2 and LY6G6C). Due to the very small number of under-expressed genes, no functional cluster is identified from DAVID. Among the 15 over-expressed genes, two pathways are over-represented. These genes are equivalently present in two of the three clusters defined by DAVID. Four genes (JUNB, CCL2, CCL4 and CCL3) relates to inflammation mediated by chemokine and cytokine signalling pathway (P00031). Three genes encoding immediate-early transcription factors (FOS, JUNB and ATF3) are over-expressed and associated with two annotation terms: RNA polymerase II proximal promoter sequence-specific DNA binding (GO: 0000978) and Gonadotropin-releasing hormone receptor pathway (P06664).
<table><caption>Table 3: Gene Functional Classification Result (PANTHER 14.1 and DAVID 6.8) of over-expressed genes in ST cells from SNEB animals. Main pathways and ontology annotation groups are shown. Asterix * indicates the significant (FDR P<0.05) over-representation statistical test (only positive enrichment is shown)</caption><thead><tr><th>clusters</th><th></th><th></th><th></th></tr></thead><tbody><tr><td></td><td>cluster 2</td><td>sequence-specific DNA binding; Homeodomain (GO: 0043565)</td><td>15</td></tr><tr><td></td><td>cluster 3</td><td>myosin complex (GO: 0016459)</td><td>4</td></tr><tr><td></td><td>cluster 4</td><td>calcium ion binding (GO: 0005509)</td><td>6</td></tr><tr><td></td><td>cluster 5</td><td>Ankyrin repeat</td><td>9</td></tr><tr><td></td><td>cluster 6</td><td>Rho guanyl-nucleotide exchange factor activity (GO: 0005089)</td><td>5</td></tr><tr><td></td><td>cluster 7</td><td>extracellular region (GO: 0005576)</td><td>13</td></tr><tr><td></td><td>cluster 8</td><td>nucleotide binding; RNA recognition motif domain (GO: 0000166)</td><td>7</td></tr><tr><td></td><td>cluster 9</td><td>protein kinase activity (GO: 0004672)</td><td>4</td></tr><tr><td></td><td>cluster 10</td><td>integral component of membrane (GO: 0016021)</td><td>130</td></tr><tr><td></td><td>cluster 11</td><td>GTP binding (GO: 000552)</td><td>4</td></tr><tr><td></td><td>cluster 12</td><td>Immunoglobulin-like domain</td><td>7</td></tr></tbody></table>

Differential expression in GE (Table 4, Table 5 and additional TableS7_david_GE-overexpressed.pdf)

Only seven known genes are under-expressed in GE cells from SNEB cows when compared to MNEB ones (CDH18, PPP1R1C, LY6G6C, MT1E, ASB16, PROM2 and SESN2). Four are related to binding functions (CDH18, PROM2, SESN and MT1E) and/or to cell surface component (CDH18, PROM2 and LY6G6C). Due to the very small number of under-expressed genes, no functional cluster is identified from DAVID. Among the 15 over-expressed genes, two pathways are over-represented. These genes are equivalently present in two of the three clusters defined by DAVID. Four genes (JUNB, CCL2, CCL4 and CCL3) relates to inflammation mediated by chemokine and cytokine signalling pathway (P00031). Three genes encoding immediate-early transcription factors (FOS, JUNB and ATF3) are over-expressed and associated with two annotation terms: RNA polymerase II proximal promoter sequence-specific DNA binding (GO: 0000978) and Gonadotropin-releasing hormone receptor pathway (P06664).
<table><thead><tr><th></th><th>annotation terms</th><th>genes (number)</th></tr></thead><tbody><tr><td rowspan="2">PANTHER<br>molecular<br>function</td><td>binding (GO: 0005488)</td><td>4</td></tr><tr><td>catalytic activity (GO: 0003824)</td><td>1</td></tr><tr><td>PANTHER<br>Cellular<br>component</td><td>cell surface (GO: 0009986)</td><td>3*</td></tr><tr><td>DAVID (6.8)</td><td>no cluster</td><td></td></tr></tbody></table>

1

2 Table 4: Gene Functional Classification Result (PANTHER 14.1 and DAVID 6.8) of under-expressed genes

3 in GE cells from SNEB animals. Main pathways and ontology annotation groups are shown. Asterix

4 * indicates the significant (FDR P<0.05) over-representation statistical test.

5
<table><thead><tr><th></th><th>annotation terms</th><th>genes (number)</th></tr></thead><tbody><tr><td rowspan="2">PANTHER<br>molecular<br>function</td><td>binding (GO: 0005488)</td><td>4</td></tr><tr><td>catalytic activity (GO: 0003824)</td><td>1</td></tr><tr><td>PANTHER<br>Cellular<br>component</td><td>cell surface (GO: 0009986)</td><td>3*</td></tr><tr><td>DAVID (6.8)</td><td>no cluster</td><td></td></tr></tbody></table>

1

2 Table 4: Gene Functional Classification Result (PANTHER 14.1 and DAVID 6.8) of under-expressed genes

3 in GE cells from SNEB animals. Main pathways and ontology annotation groups are shown. Asterix

4 * indicates the significant (FDR P<0.05) over-representation statistical test.

5
1

<table><thead><tr><th></th><th>annotation terms</th><th>genes (number)</th></tr></thead><tbody><tr><td rowspan="5">PANTHER Molecular function and pathway</td><td>RNA polymerase II proximal promoter sequence-specific DNA binding (GO:0000978)</td><td>3*</td></tr><tr><td>Inflammation mediated by chemokine and cytokine signaling pathway (P00031)</td><td rowspan="2">4*</td></tr><tr><td>cytokine activity (GO: 0005125); cytokine receptor binding (GO: 0005126)</td></tr><tr><td>Gonadotropin-releasing hormone receptor pathway (P06664)</td><td>3*</td></tr><tr><td>protein binding (GO: 0005515)</td><td>7</td></tr><tr><td rowspan="3">DAVID (6.8)</td><td>heterocyclic compound binding (GO: 1901363)</td><td>4</td></tr><tr><td>catalytic activity (GO: 0003824)</td><td>5</td></tr><tr><td>cluster 1 positive regulation of inflammatory response (GO: 0050729); chemokine-mediated signaling pathway (GO: 0070098)</td><td>3</td></tr><tr><td rowspan="2">cluster 2 RNA polymerase II core promoter proximal region sequence-specific DNA binding (GO: 0000978)</td><td rowspan="2">4</td></tr><tr><td>cluster 3 integral component of membrane (GO: 0016021)</td></tr></tbody></table>

2

<caption>Table 5: Gene Functional Classification Result (PANTHER 14.1 and DAVID 6.8) of over-expressed genes in GE cells from SNEB animals. Main pathways and ontology annotation groups are shown. Asterix * indicates the significant (FDR P<0.05) over-representation statistical test (only positive enrichment is shown)</caption>

7 *Differential expression in LE (Table 6 and additional TableS8_david_LE-underexpressed.pdf)*

8 In LE samples, only *B4GALT5* is over-expressed in SNEB. No significant enriched GO terms is related to the 55 under-expressed DEGs at FDR p value <0.05. By raising the FDR p value at 0.25, over-represented DEGs corresponds to biological processes associated with complement activation, B cell mediated immunity, defense response to bacterium, cell differentiation and cellular component link with plasma membrane and organelle.

<table><caption>Table 6: Gene Functional Classification Result (PANTHER 14.1 and DAVID 6.8) of under-expressed genes in LE cells from SNEB animals. Main pathways and ontology annotation groups are shown. Asterix * indicates the significant (FDR P<0.05) over-representation statistical test (only positive enrichment is shown)</caption><thead><tr><th></th><th>annotation terms</th><th>genes (number)</th></tr></thead><tbody><tr><td rowspan="4">PANTHER Molecular Function</td><td>binding (GO:0005488)</td><td>14</td></tr><tr><td>catalytic activity (GO:0003824)</td><td>13</td></tr><tr><td>molecular transducer activity (GO:0060089)</td><td>11</td></tr><tr><td>molecular function regulator (GO:0098772)</td><td>5</td></tr><tr><td rowspan="2">DAVID (6.8)</td><td rowspan="2">cluster 1 integral component of membrane (GO:0016021)</td><td>4</td></tr><tr><td>16</td></tr><tr><td></td><td></td><td>17</td></tr></tbody></table>
1

<table><thead><tr><th></th><th>annotation terms</th><th>genes (number)</th></tr></thead><tbody><tr><td rowspan="5">PANTHER Molecular function and pathway</td><td>RNA polymerase II proximal promoter sequence-specific DNA binding (GO:0000978)</td><td>3*</td></tr><tr><td>Inflammation mediated by chemokine and cytokine signaling pathway (P00031)</td><td rowspan="2">4*</td></tr><tr><td>cytokine activity (GO: 0005125); cytokine receptor binding (GO: 0005126)</td></tr><tr><td>Gonadotropin-releasing hormone receptor pathway (P06664)</td><td>3*</td></tr><tr><td>protein binding (GO: 0005515)</td><td>7</td></tr><tr><td rowspan="3">DAVID (6.8)</td><td>heterocyclic compound binding (GO: 1901363)</td><td>4</td></tr><tr><td>catalytic activity (GO: 0003824)</td><td>5</td></tr><tr><td>cluster 1 positive regulation of inflammatory response (GO: 0050729); chemokine-mediated signaling pathway (GO: 0070098)</td><td>3</td></tr><tr><td rowspan="2">cluster 2 RNA polymerase II core promoter proximal region sequence-specific DNA binding (GO: 0000978)</td><td rowspan="2">4</td></tr><tr><td>cluster 3 integral component of membrane (GO: 0016021)</td></tr></tbody></table>

2

<caption>Table 5: Gene Functional Classification Result (PANTHER 14.1 and DAVID 6.8) of over-expressed genes in GE cells from SNEB animals. Main pathways and ontology annotation groups are shown. Asterix * indicates the significant (FDR P<0.05) over-representation statistical test (only positive enrichment is shown)</caption>

7 *Differential expression in LE (Table 6 and additional TableS8_david_LE-underexpressed.pdf)*

8 In LE samples, only *B4GALT5* is over-expressed in SNEB. No significant enriched GO terms is related to the 55 under-expressed DEGs at FDR p value <0.05. By raising the FDR p value at 0.25, over-represented DEGs corresponds to biological processes associated with complement activation, B cell mediated immunity, defense response to bacterium, cell differentiation and cellular component link with plasma membrane and organelle.

<table><caption>Table 6: Gene Functional Classification Result (PANTHER 14.1 and DAVID 6.8) of under-expressed genes in LE cells from SNEB animals. Main pathways and ontology annotation groups are shown. Asterix * indicates the significant (FDR P<0.05) over-representation statistical test (only positive enrichment is shown)</caption><thead><tr><th></th><th>annotation terms</th><th>genes (number)</th></tr></thead><tbody><tr><td rowspan="5">PANTHER Molecular Function</td><td>binding (GO:0005488)</td><td>14</td></tr><tr><td>catalytic activity (GO:0003824)</td><td>13</td></tr><tr><td>molecular transducer activity (GO:0060089)</td><td>11</td></tr><tr><td>molecular function regulator (GO:0098772)</td><td>5</td></tr><tr><td>cluster 1 integral component of membrane (GO:0016021)</td><td>4</td></tr><tr><td>DAVID (6.8)</td><td></td><td>16</td></tr></tbody></table>
<table><caption>Table 7: The significant KEGG pathways with over- or under-expressed DEGs for three endometrial cell types (ST, GE and LE) were identified using DAVID database (adjusted p-value < 0.05).</caption><thead><tr><th>cell type</th><th>under/over</th><th>KEGG Pathway Id</th><th>pathway name</th><th>genes</th></tr></thead><tbody><tr><td rowspan="3">Stromal cells</td><td rowspan="2">over-expressed</td><td>map04020</td><td>calcium signaling</td><td>P2RX3, ITPKA, ITPR2, CHRM3, ERBB3, CAMK2B, PLN, PLCB4, SLC25A4, HTR2A, MYLK, ADCY8, TACR1, PTGFR, PTGER3, RYR3, GRIN1</td></tr><tr><td>map04530</td><td>tight junction</td><td>OCLN, IGSF5, MYH14, CLDN23, PRKCZ, MYH11, CGN, TJP3, LLGL2, CLDN8, CLDN3</td></tr><tr><td>under-expressed</td><td>map05162<br>map05164</td><td>measles and influenza A</td><td>JAK1, DDX58, ADAR, STAT1, IFIH1, EIF2AK2, IFNGR2, MX1, OAS1Z, OAS1Y, IRF7</td></tr><tr><td>Glandular cells</td><td>over-expressed</td><td>map04010</td><td>TNF signaling</td><td>FOS, SOCS3, JUNB, CCL2</td></tr></tbody></table>

KEGG pathway analysis of the DEGs.

Significantly enriched KEGG pathways from DAVID database were found in GE and ST, whereas no significant KEGG pathway was detected in LE. In ST cells, DEGs between SNEB and MNEB cows were significantly enriched in four different KEGG pathways. 25 KEGG pathways were recognized by David with the overexpressed genes. Two were found significantly enriched. They are related to calcium signalling pathway (KEGG map04020, fold enrichment = 3.4; 17 DEGs) and tight junctions (KEGG map04530; fold enrichment = 4.8; 11 DEGs). With under-expressed DEGs, two KEGG pathways associated with viral infectious diseases (KEGG "measles" map05162 and KEGG "Influenza A" map05164; fold enrichment respectively = 5.0 and 4.1; 11 DEGs) are overrepresented (Table 7). The names of these two KEGG pathways do not make sense with endometrial physiology. The genes of these pathways are known to be important partners of interferon signalling that is a critical mechanism for establishment of pregnancy (reactome pathways: BTA-913531, BTA-877312). For glandular epithelium, over-expressed DEGs matched to 10 overrepresented KEGG pathways. The KEGG TNF signalling pathway (KEGG map04010) was the only one found to be significantly enriched (Fold enrichment = 21.5). In contrast, no enriched KEGG pathways were found from the set of under-expressed DEGs.
<table><caption>Table 7: The significant KEGG pathways with over- or under-expressed DEGs for three endometrial cell types (ST, GE and LE) were identified using DAVID database (adjusted p-value < 0.05).</caption><thead><tr><th>cell type</th><th>under/over</th><th>KEGG Pathway Id</th><th>pathway name</th><th>genes</th></tr></thead><tbody><tr><td rowspan="3">Stromal cells</td><td rowspan="2">over-expressed</td><td>map04020</td><td>calcium signaling</td><td>P2RX3, ITPKA, ITPR2, CHRM3, ERBB3, CAMK2B, PLN, PLCB4, SLC25A4, HTR2A, MYLK, ADCY8, TACR1, PTGFR, PTGER3, RYR3, GRIN1</td></tr><tr><td>map04530</td><td>tight junction</td><td>OCLN, IGSF5, MYH14, CLDN23, PRKCZ, MYH11, CGN, TJP3, LLGL2, CLDN8, CLDN3</td></tr><tr><td>under-expressed</td><td>map05162<br>map05164</td><td>measles and influenza A</td><td>JAK1, DDX58, ADAR, STAT1, IFIH1, EIF2AK2, IFNGR2, MX1, OAS1Z, OAS1Y, IRF7</td></tr><tr><td>Glandular cells</td><td>over-expressed</td><td>map04010</td><td>TNF signaling</td><td>FOS, SOCS3, JUNB, CCL2</td></tr></tbody></table>

KEGG pathway analysis of the DEGs.

Significantly enriched KEGG pathways from DAVID database were found in GE and ST, whereas no significant KEGG pathway was detected in LE. In ST cells, DEGs between SNEB and MNEB cows were significantly enriched in four different KEGG pathways. 25 KEGG pathways were recognized by David with the overexpressed genes. Two were found significantly enriched. They are related to calcium signalling pathway (KEGG map04020, fold enrichment = 3.4; 17 DEGs) and tight junctions (KEGG map04530; fold enrichment = 4.8; 11 DEGs). With under-expressed DEGs, two KEGG pathways associated with viral infectious diseases (KEGG “measles” map05162 and KEGG “Influenza A” map05164; fold enrichment respectively = 5.0 and 4.1; 11 DEGs) are overrepresented (Table 7). The names of these two KEGG pathways do not make sense with endometrial physiology. The genes of these pathways are known to be important partners of interferon signalling that is a critical mechanism for establishment of pregnancy (reactome pathways: BTA-913531, BTA-877312). For glandular epithelium, over-expressed DEGs matched to 10 overrepresented KEGG pathways. The KEGG TNF signalling pathway (KEGG map04010) was the only one found to be significantly enriched (Fold enrichment = 21.5). In contrast, no enriched KEGG pathways were found from the set of under-expressed DEGs.
1

The corresponding STRING-generated interaction network obtained from DEGs belonging to the 5

2

KEGG pathways associated to ST and GE cells revealed strong interactions (PPI enrichment value <

3

1.0E-16) between these sets of DEGs that are related to the JAK/STAT signalling (Figure 6).

4

DISCUSSION

5

During negative energy balance (NEB), lipolysis in adipose tissue is increased resulting in decreased

6

BCS and increased NEFAs in blood [31]. Changes in BCS and NEFA concentrations were correlated

7

with NEB nadir and plasma NEFA concentrations in SNEB cows were greater than in MNEB cows in

8

the prepartum and early post-partum. Both observations are consistent with earlier findings [32] and

9

shows that the two groups were in a different metabolic status before and during the two first weeks

10

post-partum. The impacts of NEB on bovine reproductive performances are well documented [33]. A

11

wealth of information illustrates the negative effects of NEB and NEFA on ovarian cells [34],

12

embryos [35] and oviduct [36]. On the contrary, relatively few publications have reported effects of

13

NEB on the endometrial tissue or cells. *In vivo* studies showed that NEB had negative impacts on

14

endometrial function through the alteration of immune response and activation of pro-inflammatory

15

and IGF-insulin signalling pathways [37, 38]. However, in those studies information was obtained

16

from full tissue and to our knowledge, the present study is the first time that the specific effects of

17

NEB on the three main cell types of the endometrium are reported.

18

Transcriptome of the three endometrial cell types

19

Our results fully confirm that stromal cells, glandular and luminal epithelial cells reveal specific

20

molecular signatures as documented before in studies using LCM in human [39], sheep [26] and horse

21

[40]. Our results based on biopsies collected in the luteal phase, have shown that a higher number of

22

genes with a strong constitutive expression in stromal cells compared with epithelial cells (either

23

glandular or luminal) are different from the expression pattern observed at the beginning of pregnancy

24

[40]. This may result from differences between species but could also reveal the changes induced by

25

the conceptus on the endometrial transcriptome previously reported from full tissue [37, 41] and

26

epithelial cells [26].
1

The corresponding STRING-generated interaction network obtained from DEGs belonging to the 5

2

KEGG pathways associated to ST and GE cells revealed strong interactions (PPI enrichment value <

3

1.0E-16) between these sets of DEGs that are related to the JAK/STAT signalling (Figure 6).

4

DISCUSSION

5

During negative energy balance (NEB), lipolysis in adipose tissue is increased resulting in decreased

6

BCS and increased NEFAs in blood [31]. Changes in BCS and NEFA concentrations were correlated

7

with NEB nadir and plasma NEFA concentrations in SNEB cows were greater than in MNEB cows in

8

the prepartum and early post-partum. Both observations are consistent with earlier findings [32] and

9

shows that the two groups were in a different metabolic status before and during the two first weeks

10

post-partum. The impacts of NEB on bovine reproductive performances are well documented [33]. A

11

wealth of information illustrates the negative effects of NEB and NEFA on ovarian cells [34],

12

embryos [35] and oviduct [36]. On the contrary, relatively few publications have reported effects of

13

NEB on the endometrial tissue or cells. *In vivo* studies showed that NEB had negative impacts on

14

endometrial function through the alteration of immune response and activation of pro-inflammatory

15

and IGF-insulin signalling pathways [37, 38]. However, in those studies information was obtained

16

from full tissue and to our knowledge, the present study is the first time that the specific effects of

17

NEB on the three main cell types of the endometrium are reported.

18

Transcriptome of the three endometrial cell types

19

Our results fully confirm that stromal cells, glandular and luminal epithelial cells reveal specific

20

molecular signatures as documented before in studies using LCM in human [39], sheep [26] and horse

21

[40]. Our results based on biopsies collected in the luteal phase, have shown that a higher number of

22

genes with a strong constitutive expression in stromal cells compared with epithelial cells (either

23

glandular or luminal) are different from the expression pattern observed at the beginning of pregnancy

24

[40]. This may result from differences between species but could also reveal the changes induced by

25

the conceptus on the endometrial transcriptome previously reported from full tissue [37, 41] and

26

epithelial cells [26].
1 Using a cut-off of 10 TPM, different numbers of genes were expressed in the three endometrial cell
types. ST expressed 5% and 25% more genes than GE and LE, respectively. However, as reported
2 before from a large variety of tissues [42], and the three laser-dissected cell types of porcine
3 endometrium [43], our results confirm that a high number of genes are expressed in common in
4 different endometrial cell types. In the present study, 70 to 85% of genes were expressed in all cells
5 suggesting either "house-keeping" functions or genes encoding proteins with functions common to the
6 endometrium while lower proportions (5%, 7% and 15% for LE, GE and ST, respectively) were
7 restricted to each cell type indicating that they code for proteins supporting the functional specialized
8 signature of each cell type. When compared to porcine endometrium [43], the number of genes
9 showing cell-specific expression is in the same order of magnitude for GE and LE, but appears
10 different for ST cells where this number is ten times higher. These differences in specific expression
11 between cell types, especially the large number of functions enriched in ST are well reflected by the
12 REVIGO analysis (Figure 4).
13

14 Regardless of the cut off chosen and related limitations, these studies illustrate huge differences in the
15 gene expression patterns between cell types corresponding to specialized functions. This confirms that
16 separating cell types is more appropriate and possibly less biased to decipher the impacts of any factor
17 on a given tissue than former approaches based on full tissue. The clear clustering obtained when
18 analysing the full transcriptome, indicates that luminal and glandular epithelial cells are closely
19 related. These similarities may reflect common functional properties and/or may be related to the
20 common epithelial nature of these cells. The genes associated with GE and LE, which distinguish
21 these two epithelial cells from stromal cells, are all related to GO terms typical of epithelia (GO:
22 0030855, epithelial cell differentiation; GO: 0060429, epithelium development; GO: 0045216, cell-
23 cell junction organization). Examples are given below for critical genes previously cited as key
24 regulators of endometrial epithelial cells. CDH1 is involved in organization of epithelium in mouse
25 and its ablation causes the absence of endometrial glands. Occludin is an important protein for tight
26 junction assembly which preserves the epithelial barrier function. The REVIGO analysis showed that
27 in both epithelial cell types, genes encoding proteins related to metabolism were under-represented.
28
1 Using a cut-off of 10 TPM, different numbers of genes were expressed in the three endometrial cell
types. ST expressed 5% and 25% more genes than GE and LE, respectively. However, as reported
2 before from a large variety of tissues [42], and the three laser-dissected cell types of porcine
3 endometrium [43], our results confirm that a high number of genes are expressed in common in
4 different endometrial cell types. In the present study, 70 to 85% of genes were expressed in all cells
5 suggesting either "house-keeping" functions or genes encoding proteins with functions common to the
6 endometrium while lower proportions (5%, 7% and 15% for LE, GE and ST, respectively) were
7 restricted to each cell type indicating that they code for proteins supporting the functional specialized
8 signature of each cell type. When compared to porcine endometrium [43], the number of genes
9 showing cell-specific expression is in the same order of magnitude for GE and LE, but appears
10 different for ST cells where this number is ten times higher. These differences in specific expression
11 between cell types, especially the large number of functions enriched in ST are well reflected by the
12 REVIGO analysis (Figure 4).
13

14 Regardless of the cut off chosen and related limitations, these studies illustrate huge differences in the
gene expression patterns between cell types corresponding to specialized functions. This confirms that
15 separating cell types is more appropriate and possibly less biased to decipher the impacts of any factor
16 on a given tissue than former approaches based on full tissue. The clear clustering obtained when
17 analysing the full transcriptome, indicates that luminal and glandular epithelial cells are closely
18 related. These similarities may reflect common functional properties and/or may be related to the
19 common epithelial nature of these cells. The genes associated with GE and LE, which distinguish
20 these two epithelial cells from stromal cells, are all related to GO terms typical of epithelia (GO:
21 0030855, epithelial cell differentiation; GO: 0060429, epithelium development; GO: 0045216, cell-
22 cell junction organization). Examples are given below for critical genes previously cited as key
23 regulators of endometrial epithelial cells. CDH1 is involved in organization of epithelium in mouse
24 and its ablation causes the absence of endometrial glands. Occludin is an important protein for tight
25 junction assembly which preserves the epithelial barrier function. The REVIGO analysis showed that
26 in both epithelial cell types, genes encoding proteins related to metabolism were under-represented.
27
1

On the contrary, genes related to cilium function are enriched in GE, whereas those involved in
binding/receptor function and adhesion are over-represented in LE. In addition, LE cells differentiate
from GE by the expression of genes like *CYP26A1*, that encodes a key enzyme of trans retinoic acid
inactivation, already shown as strongly expressed in luminal epithelial cells of rat endometrium and
playing a role in embryo implantation [44]. Endometrial expression of *HCTR1* has been reported to
be, with its main ligand orexin-A, an important local regulator of endometrial functions in porcine
uterus [45, 46].

2

3

4

5

6

7

8

As mentioned above for GE and LE, the REVIGO analysis showed that genes involved in metabolism
were also under-represented in ST. In contrast, a very large number of functions including but not
limited to, cell structure, angiogenesis, extra cellular matrix and immunity are enriched in ST whereas
a lack of strong expression of these genes is observed in GE and LE. As awaited, among the genes
most discriminating stromal cells, those involved in the production of extracellular matrix and
collagen are highly represented in ST. *COL1A2*, *COL3A1*, *COL7A1* and *COL3A3* encode proteins that
are involved in dynamic remodeling of endometrial extracellular matrix in cattle and regulate embryo
receptivity [47]. Our data identified genes associated with extracellular matrix organization that had
not been previously described in bovine endometrium including *LOXL2*, responsible for the cross-
linking of collagen and elastin [48], *ECM2* involved in the regulation of cell proliferation and
differentiation [49] and *CRISPDL* Lknown to regulate extracellular matrix and branching
morphogenesis [50]. These genes encode proteins that may have an important role in the formation of
glands and vasculature in bovine endometrium as well as *WT1*, already known to be preferentially
expressed in stromal endometrial cells [51, 52].

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

We identify here also original genes related to stromal cell differentiation and cell migration such as
*CDH11*, *PRELP*, *THY1* (the latter encoding a stem cell marker) [53], *GJA1* [54], *OSR2* [55], *P4HA3*.
*PRLEP* gene expression has been reported to be regulated by the embryo in the bovine oviduct [56].
Contrary to the porcine endometrium where its expression was located in epithelial cells, *NTRK2* was
mainly expressed here in stromal cells [57]. The expression of the NTRK2 gene, which encodes the
receptor of brain derived neurotrophic factor, is conserved in mammalian uterus but its signalling

24

25

26

27
1

On the contrary, genes related to cilium function are enriched in GE, whereas those involved in
binding/receptor function and adhesion are over-represented in LE. In addition, LE cells differentiate
from GE by the expression of genes like *CYP26A1*, that encodes a key enzyme of trans retinoic acid
inactivation, already shown as strongly expressed in luminal epithelial cells of rat endometrium and
playing a role in embryo implantation [44]. Endometrial expression of *HCTR1* has been reported to
be, with its main ligand orexin-A, an important local regulator of endometrial functions in porcine
uterus [45, 46].

2

3

4

5

6

7

8

As mentioned above for GE and LE, the REVIGO analysis showed that genes involved in metabolism
were also under-represented in ST. In contrast, a very large number of functions including but not
limited to, cell structure, angiogenesis, extra cellular matrix and immunity are enriched in ST whereas
a lack of strong expression of these genes is observed in GE and LE. As awaited, among the genes
most discriminating stromal cells, those involved in the production of extracellular matrix and
collagen are highly represented in ST. *COL1A2*, *COL3A1*, *COL7A1* and *COL3A3* encode proteins that
are involved in dynamic remodeling of endometrial extracellular matrix in cattle and regulate embryo
receptivity [47]. Our data identified genes associated with extracellular matrix organization that had
not been previously described in bovine endometrium including *LOXL2*, responsible for the cross-
linking of collagen and elastin [48], *ECM2* involved in the regulation of cell proliferation and
differentiation [49] and *CRISPDL* Lknown to regulate extracellular matrix and branching
morphogenesis [50]. These genes encode proteins that may have an important role in the formation of
glands and vasculature in bovine endometrium as well as *WT1*, already known to be preferentially
expressed in stromal endometrial cells [51, 52].

10

11

12

13

14

15

16

17

18

19

20

21

22

We identify here also original genes related to stromal cell differentiation and cell migration such as
*CDH11*, *PRELP*, *THY1* (the latter encoding a stem cell marker) [53], *GJA1* [54], *OSR2* [55], *P4HA3*.
*PRLEP* gene expression has been reported to be regulated by the embryo in the bovine oviduct [56].
Contrary to the porcine endometrium where its expression was located in epithelial cells, *NTRK2* was
mainly expressed here in stromal cells [57]. The expression of the NTRK2 gene, which encodes the
receptor of brain derived neurotrophic factor, is conserved in mammalian uterus but its signalling

23

24

25

26

27
1

function is not yet understood in the female reproductive system [58]. Genes known to be key
regulators of uterine receptivity in different species such as, *HOXA10* and *HOXA11* belong also to the
top list of 50 genes which characterize ST (human [59], mice [60] and goat [61]). This list includes
*CALPAIN7* [62] and *SNAI2* [63] which are involved in embryo attachment and implantation and the
disintegrins and metalloproteases *ADAMTS1* and *ADAM23* which are genes encoding key molecules
for bovine endometrial remodelling [64]. In addition, a group of stromal genes including *SERPING1*
[65], *C1R*, *C1S* [66], *SFRP1* and *IGF1* are involved in embryo maternal immune modulation and IFN
response.

2

3

4

5

6

7

8

9

Finally, among these first 50 genes that best separate ST from epithelial cells, numerous ones have not been described so far in the mammalian endometrium. For instance, we could not find any information on the expression and function in the endometrium of the following genes and their encoded proteins: *MUSTN1*, *OSR2*, *TGM2*, *PCDH9*, *PGM5*, *MXRA5*, *MAMDC2*, *MRGPRF*, *RASD2*, *SULF1*, *RASL11A*, *ECM2*, *OLFML3* and *P4HA3*. These results may help to formulate new hypotheses for exploring new biological roles for stromal genes.

10

11

12

13

14

15

## Impact of NEB on the three endometrial cell types

16

Overall, our results show that NEB impacts mainly ST whereas GE and LE cells are less affected. More than 10% (13%) of the total number of genes expressed in ST were impaired by NEB status while less than 1% were affected in GE and LE (0.3% and 0.7% respectively). When considering the sub groups of genes showing a specific expression related to cell type, NEB did not affect any of those in GE and modified only the expression of *TCN1* and *B4GALT5* in LE cells. This number is probably under-estimated in LE due to the comparison restricted to a single sample in the SNEB group. By contrast, a relatively high number of genes (about 8%; n=91) specifically expressed by ST are affected by NEB.

17

18

19

20

21

22

23

24

## Impact of NEB on genes related to cytoskeleton and cell adhesion.
Genes encoding tropomyosins (*TPM1*, *TPM2*) and myosins (*MYO5C*, *MYO5B*) proteins which are structural constituents of cytoskeleton (GO: 0016459) were over-expressed in ST of SNEB cows. Similar over-expression of

25

26
1

function is not yet understood in the female reproductive system [58]. Genes known to be key
regulators of uterine receptivity in different species such as, *HOXA10* and *HOXA11* belong also to the
top list of 50 genes which characterize ST (human [59], mice [60] and goat [61]). This list includes
*CALPAIN7* [62] and *SNAI2* [63] which are involved in embryo attachment and implantation and the
disintegrins and metalloproteases *ADAMTS1* and *ADAM23* which are genes encoding key molecules
for bovine endometrial remodelling [64]. In addition, a group of stromal genes including *SERPING1*
[65], *C1R*, *C1S* [66], *SFRP1* and *IGF1* are involved in embryo maternal immune modulation and IFN
response.

2

3

4

5

6

7

8

9

Finally, among these first 50 genes that best separate ST from epithelial cells, numerous ones have not been described so far in the mammalian endometrium. For instance, we could not find any information on the expression and function in the endometrium of the following genes and their encoded proteins: *MUSTN1*, *OSR2*, *TGM2*, *PCDH9*, *PGM5*, *MXRA5*, *MAMDC2*, *MRGPRF*, *RASD2*, *SULF1*, *RASL11A*, *ECM2*, *OLFML3* and *P4HA3*. These results may help to formulate new hypotheses for exploring new biological roles for stromal genes.

10

11

12

13

14

15

## Impact of NEB on the three endometrial cell types

16

Overall, our results show that NEB impacts mainly ST whereas GE and LE cells are less affected. More than 10% (13%) of the total number of genes expressed in ST were impaired by NEB status while less than 1% were affected in GE and LE (0.3% and 0.7% respectively). When considering the sub groups of genes showing a specific expression related to cell type, NEB did not affect any of those in GE and modified only the expression of *TCN1* and *B4GALT5* in LE cells. This number is probably under-estimated in LE due to the comparison restricted to a single sample in the SNEB group. By contrast, a relatively high number of genes (about 8%; n=91) specifically expressed by ST are affected by NEB.

17

18

19

20

21

22

23

24

## Impact of NEB on genes related to cytoskeleton and cell adhesion.
Genes encoding tropomyosins (*TPM1*, *TPM2*) and myosins (*MYO5C*, *MYO5B*) proteins which are structural constituents of cytoskeleton (GO: 0016459) were over-expressed in ST of SNEB cows. Similar over-expression of

25

26
tropomyosins and myosins has been reported in the endometrium of fertile cows [67]. The increased expression of myosins was associated to over-expression of genes of the dynein family (DNAH5, DNAH7, DNAH11, DYNC1I1 and DYNLRB2) which encode proteins that are involved in cell mobility (GO: 0005874). The signification of these changes in the context of fertility deserves further investigations. In contrast, a large set of genes related to cell adhesion and cell-cell and cell-extracellular matrix adhesion [68], such as integrins (ITGA5, ITGA10), cadherins (CDH2, CDH11, CDH12), AGRN, EGFLAM, TGFBI, type IV collagen (COL4A4), type VIII collagen (COL8A1), ODZ3, SCARB2 and WISP3 were under-expressed in ST of SNEB cows. The lower expression of integrins could be seen as unfavourable to establishment of pregnancy. In humans, ITGB3 mRNA has been cited as a positive marker associated with pregnancy [69, 70]. In sheep, elevated expression of ITGAV, ITGA4, and ITGA5 in GE have been found during pregnancy [71]. E-cadherin (CDH1) has been documented as a critical gene for embryo implantation as its under-expression in epithelial cells allows endometrial cells dissociation following blastocyst invasion [72]. Moreover, an increased expression of type IV collagens has been identified in endometrium of low fertility heifers [73], however, the opposite trend was found here in SNEB cows. In ST from the SNEB group, genes belonging to the Wnt pathway (P00057) were either over expressed (ACTG2, FZD5, PLCB4, CDH3, PRKCZ, CDH1, ACTA1, CTBP2, ITPR2, FRZB, ANKRD6 and ACTA2) or under expressed (CDH11, TLE4, LEF1, NFATC1, PRKCH, SMARCD2 and FBXW7). These genes encode proteins that are associated with GO: 0001763 (morphogenesis of a branching structure) GO: 0001944 (vasculature development) including involvement in the morphogenesis and function of the endometrial glands [74, 75] as well as in the development of uterine vasculature [76]. The altered expression of these genes by the NEB can have a critical role in the regeneration of the endometrium during the postpartum period.

**Impact of NEB on genes related to energy metabolism.** In SNEB cows, among the 700 genes that are over-expressed in ST, a large proportion were genes classified to encode proteins related to metabolic process (GO: 0008152), macromolecule metabolic process (GO: 0043170) and organic substance metabolic process (GO: 0071704). DEGs were most particularly related to catalytic activity (GO:
tropomyosins and myosins has been reported in the endometrium of fertile cows [67]. The increased expression of myosins was associated to over-expression of genes of the dynein family (DNAH5, DNAH7, DNAH11, DYNC1I1 and DYNLRB2) which encode proteins that are involved in cell mobility (GO: 0005874). The signification of these changes in the context of fertility deserves further investigations. In contrast, a large set of genes related to cell adhesion and cell-cell and cell-extracellular matrix adhesion [68], such as integrins (ITGA5, ITGA10), cadherins (CDH2, CDH11, CDH12), AGRN, EGFLAM, TGFBI, type IV collagen (COL4A4), type VIII collagen (COL8A1), ODZ3, SCARB2 and WISP3 were under-expressed in ST of SNEB cows. The lower expression of integrins could be seen as unfavourable to establishment of pregnancy. In humans, ITGB3 mRNA has been cited as a positive marker associated with pregnancy [69, 70]. In sheep, elevated expression of ITGAV, ITGA4, and ITGA5 in GE have been found during pregnancy [71]. E-cadherin (CDH1) has been documented as a critical gene for embryo implantation as its under-expression in epithelial cells allows endometrial cells dissociation following blastocyst invasion [72]. Moreover, an increased expression of type IV collagens has been identified in endometrium of low fertility heifers [73], however, the opposite trend was found here in SNEB cows. In ST from the SNEB group, genes belonging to the Wnt pathway (P00057) were either over expressed (ACTG2, FZD5, PLCB4, CDH3, PRKCZ, CDH1, ACTA1, CTBP2, ITPR2, FRZB, ANKRD6 and ACTA2) or under expressed (CDH11, TLE4, LEF1, NFATC1, PRKCH, SMARCD2 and FBXW7). These genes encode proteins that are associated with GO: 0001763 (morphogenesis of a branching structure) GO: 0001944 (vasculature development) including involvement in the morphogenesis and function of the endometrial glands [74, 75] as well as in the development of uterine vasculature [76]. The altered expression of these genes by the NEB can have a critical role in the regeneration of the endometrium during the postpartum period.

**Impact of NEB on genes related to energy metabolism.** In SNEB cows, among the 700 genes that are over-expressed in ST, a large proportion were genes classified to encode proteins related to metabolic process (GO: 0008152), macromolecule metabolic process (GO: 0043170) and organic substance metabolic process (GO: 0071704). DEGs were most particularly related to catalytic activity (GO:
1 0003824) revealing the breakdown of nutrient molecules to supply energy to cells. This suggest that
2 SNEB cows still presented an energy deficit in endometrial cells at time planned for breeding, despite
3 that energy balance is progressively restored. SNEB cows presented also an increased expression of
4 many genes encoding proteins with functions related to lipid metabolism (fatty acids, triglyceride and
5 cholesterol metabolic processes) such as ACSM3, CPT1B, LPL, PPARGC1A, PRKAA2, GGT1,
6 PLA2G10, CYP2B6, CYP2C18, HACD1, SLC27A6 and PLIN4 in ST. Four of them CYP2B6,
7 CYP2C18, PLA2G10, and GGT1 are involved in arachidonic acid (AA) metabolism. While the release
8 of arachidonic acid following phospholipase activation is usually engaged in the production of
9 endometrial prostaglandins via cyclooxygenases enzymes, the conversion of AA by CYP enzymes
10 contribute to oxidative stress and inflammation and may not be favourable to endometrial function
11 [77]. The receptivity of fibroblasts to prostaglandins could also be modified through their receptors
12 with the observed extreme over-expression of PTGFR mRNA (the second top of over-expressed
13 DEGs in ST) and PTGER3. The over-expression of SLC27A6, a fatty acid binding protein (FABP)
14 [78] and PLIN4, which controls intra-cellular lipid droplet-associated proteins, are consistent with
15 earlier findings in obese mice and human [79, 80]. Our data showing associations between over-
16 expression of these genes with increased plasma NEFA concentrations are consistent with the over-
17 expression of genes of the PLIN family found in the endometrium of low fertility heifers [73]. Taken
18 together, this information suggests that up-regulation of genes involved in lipid uptake in ST of SNEB
19 cows, associated with elevated NEFA concentration during the peri-parturient period may not be
20 favourable to fertility in postpartum cows. Increased gene expression from the solute carrier family in
21 ST from SNEB cows (such as SLC2A12, SLC45A2 and SLC35A3), which encode proteins involved in
22 carbohydrate transportation, could be seen as a compensatory mechanism as the under-expression of
23 the glucose transporter (SLC2A1) mRNA was detected in endometrial tissue of subfertile dairy cows
24 [81].

25 **Impact of NEB on genes related to growth factors.** Interestingly, expression of genes associated with
26 IGF-insulin signalling, such as *IGF1R* and *IGF2BP2*, was higher in SNEB cows. On the contrary,
27 *IGFBP2*, *GDF6*, *EDIL3* and *TGFBI* were under-expressed in ST of SNEB cows. The expression of
1 0003824) revealing the breakdown of nutrient molecules to supply energy to cells. This suggest that
2 SNEB cows still presented an energy deficit in endometrial cells at time planned for breeding, despite
3 that energy balance is progressively restored. SNEB cows presented also an increased expression of
4 many genes encoding proteins with functions related to lipid metabolism (fatty acids, triglyceride and
5 cholesterol metabolic processes) such as ACSM3, CPT1B, LPL, PPARGC1A, PRKAA2, GGT1,
6 PLA2G10, CYP2B6, CYP2C18, HACD1, SLC27A6 and PLIN4 in ST. Four of them CYP2B6,
7 CYP2C18, PLA2G10, and GGT1 are involved in arachidonic acid (AA) metabolism. While the release
8 of arachidonic acid following phospholipase activation is usually engaged in the production of
9 endometrial prostaglandins via cyclooxygenases enzymes, the conversion of AA by CYP enzymes
10 contribute to oxidative stress and inflammation and may not be favourable to endometrial function
11 [77]. The receptivity of fibroblasts to prostaglandins could also be modified through their receptors
12 with the observed extreme over-expression of PTGFR mRNA (the second top of over-expressed
13 DEGs in ST) and PTGER3. The over-expression of SLC27A6, a fatty acid binding protein (FABP)
14 [78] and PLIN4, which controls intra-cellular lipid droplet-associated proteins, are consistent with
15 earlier findings in obese mice and human [79, 80]. Our data showing associations between over-
16 expression of these genes with increased plasma NEFA concentrations are consistent with the over-
17 expression of genes of the PLIN family found in the endometrium of low fertility heifers [73]. Taken
18 together, this information suggests that up-regulation of genes involved in lipid uptake in ST of SNEB
19 cows, associated with elevated NEFA concentration during the peri-parturient period may not be
20 favourable to fertility in postpartum cows. Increased gene expression from the solute carrier family in
21 ST from SNEB cows (such as SLC2A12, SLC45A2 and SLC35A3), which encode proteins involved in
22 carbohydrate transportation, could be seen as a compensatory mechanism as the under-expression of
23 the glucose transporter (SLC2A1) mRNA was detected in endometrial tissue of subfertile dairy cows
24 [81].

25 **Impact of NEB on genes related to growth factors.** Interestingly, expression of genes associated with
26 IGF-insulin signalling, such as *IGF1R* and *IGF2BP2*, was higher in SNEB cows. On the contrary,
27 *IGFBP2*, *GDF6*, *EDIL3* and *TGFBI* were under-expressed in ST of SNEB cows. The expression of
IGFs were detected in the uterine stroma especially the caruncular areas of cyclic cows [82]. As suggested in the above-referred study and by others [38], the dysregulation of genes related to insulin-like growth factors function may delay tissue remodelling during the postpartum period. In our study, the importance of those changes on matrix metalloproteinase (MMP) appeared limited as only one gene of the MMPs family (MMP19) was under-expressed in ST of SNEB cows. However, 9 closely related genes involved in the degradation of the cellular matrix and tissue remodelling were also under-expressed in the SNEB cows. On the contrary, growth factor receptors such as GRB7, GRB14 and FGFR2, which are known as stromal-derived paracrine stimulators of epithelial proliferation, were over-expressed in ST of SNEB. This increase may be a mechanism for compensating endometrial epithelial defects in order to achieve uterine receptivity [83]. In bovine species, gene expression of FGFs and their receptors is upregulated during pregnancy and these factors stimulate interferon-tau (IFN-T) production during the pre-attachment phase of conceptus development [84]. The increase of transcripts encoding proteins of the cyclin family (CCND3 and CCNB1) in ST of SNEB cows may also be associated with the modifications of proliferative properties and tissue differentiation in the endometrium for preparing embryo implantation [85]. Our results show that NEB status influences both the over-expression and under-expression of different and numerous growth factors. However, further studies are needed to decipher the consequences of these changes and how they may affect fertility.

**Impact of NEB on genes related to inflammatory responses.** Nearly 20 genes belonging to two pathways [cytokine signalling in immune system pathway (R-BTA-1280215) and inflammation mediated by chemokine and cytokine signalling pathway (P00031)] displayed reduced transcripts in ST of SNEB. Among these genes JAK1 and STAT1 have been associated with both IFN-γ and IFNα/β endometrial receptors [86]. It may be hypothesized that the reduced-expression of JAK1 and STAT1 may alter JAK/STAT signalling and immune response in stromal cells. Indeed, a large number of IFN-inducible genes (R-BTA-877312), such as MX1, MX2, IFI44, IFI6, IFIH1, IFIT1, IFITM2 and IFNGR2 were under- expressed in ST of SNEB cows. These findings are different from previous observations showing over-expression of MX1 and MX2 genes in the full endometrium of SNEB cows
IGFs were detected in the uterine stroma especially the caruncular areas of cyclic cows [82]. As suggested in the above-referred study and by others [38], the dysregulation of genes related to insulin-like growth factors function may delay tissue remodelling during the postpartum period. In our study, the importance of those changes on matrix metalloproteinase (MMP) appeared limited as only one gene of the MMPs family (MMP19) was under-expressed in ST of SNEB cows. However, 9 closely related genes involved in the degradation of the cellular matrix and tissue remodelling were also under-expressed in the SNEB cows. On the contrary, growth factor receptors such as GRB7, GRB14 and FGFR2, which are known as stromal-derived paracrine stimulators of epithelial proliferation, were over-expressed in ST of SNEB. This increase may be a mechanism for compensating endometrial epithelial defects in order to achieve uterine receptivity [83]. In bovine species, gene expression of FGFs and their receptors is upregulated during pregnancy and these factors stimulate interferon-tau (IFN-T) production during the pre-attachment phase of conceptus development [84]. The increase of transcripts encoding proteins of the cyclin family (CCND3 and CCNB1) in ST of SNEB cows may also be associated with the modifications of proliferative properties and tissue differentiation in the endometrium for preparing embryo implantation [85]. Our results show that NEB status influences both the over-expression and under-expression of different and numerous growth factors. However, further studies are needed to decipher the consequences of these changes and how they may affect fertility.

**Impact of NEB on genes related to inflammatory responses.** Nearly 20 genes belonging to two pathways [cytokine signalling in immune system pathway (R-BTA-1280215) and inflammation mediated by chemokine and cytokine signalling pathway (P00031)] displayed reduced transcripts in ST of SNEB. Among these genes *JAK1* and *STAT1* have been associated with both IFN-γ and IFNα/β endometrial receptors [86]. It may be hypothesized that the reduced-expression of *JAK1* and *STAT1* may alter JAK/STAT signalling and immune response in stromal cells. Indeed, a large number of IFN-inducible genes (R-BTA-877312), such as *MX1*, *MX2*, *IFI44*, *IFI6*, *IFIH1*, *IFIT1*, *IFITM2* and *IFNGR2* were under- expressed in ST of SNEB cows. These findings are different from previous observations showing over-expression of *MX1* and *MX2* genes in the full endometrium of SNEB cows
during early postpartum [37]. The specificity of stromal cell response to SNEB, may explain
differences between studies, however due to the lack of effect on GE, these discrepancies may result
also from differences in time postpartum and severity of NEB. The glandular epithelium plays a major
role in the activation of the innate immune system as reviewed by [87]. In our study, most of the
DEGs in GE related to chemokines, immune response processes, TLRs and TNF signalling pathways,
such as CCL2, CCL3, CCL4, CCL11, FOS, JUNB, and SOCS3 were strongly over-expressed in SNEB
cows. Some of those genes belonged to the C-C motif chemokine ligands (CCLs) family and play an
important role in monocyte recruitment in the endometrium [88]. Increased expression of CCL2
mRNA was found associated with lipid accumulation induced uterine inflammation in obese rats [89].
The present results are similar with previous studies performed with full endometrial tissue, showing
the up-regulation of inflammatory response genes in SNEB cows [38]. This is also consistent with
several studies in mammals showing that metabolic imbalance, increased lipolysis and most
particularly NEFAs, play essential functions in the activation of TNF and TLRs signalling to promote
the release of pro-inflammatory molecules [90, 91]. Taken together, these studies and our present
findings suggest that SNEB and NEFAs activate pro-inflammatory pathways in the glandular
epithelium and stromal cells. On the contrary, in luminal epithelium, the adaptive immune response
(B cell-mediated immunity) and innate immunity, was represented by under-expressed genes such as
tracheal antimicrobial peptide (TAP), a beta-defensin gene, which was associated to the NF-κB
pathway [92], and by genes coding for immunoglobulin heavy variable chains that participates in the
antigen recognition. These observations need further confirmation. Our results indicate that SNEB
induces changes in immune responses, which are different in the three endometrial cell types. They
show also that these changes are still present, long after NEB has disappeared suggesting long term
effects of metabolic imbalance and NEFAs on the pro-inflammatory status of the glandular epithelium
and the stroma.

Effect of NEB on genes related to maternal-conceptus recognition. A large set of IFN-inducible genes such as MX1, MX2, STAT1, JAK1, IFIH1, IFNGR2, ISG15, LY6G6C, OAS1Y, OAS1Z and IRF7 were under-expressed in ST of SNEB cows. A weaker expression of those genes that encode proteins
during early postpartum [37]. The specificity of stromal cell response to SNEB, may explain
differences between studies, however due to the lack of effect on GE, these discrepancies may result
also from differences in time postpartum and severity of NEB. The glandular epithelium plays a major
role in the activation of the innate immune system as reviewed by [87]. In our study, most of the
DEGs in GE related to chemokines, immune response processes, TLRs and TNF signalling pathways,
such as *CCL2*, *CCL3*, *CCL4*, *CCL11*, *FOS*, *JUNB*, and *SOCS3* were strongly over-expressed in SNEB
cows. Some of those genes belonged to the C-C motif chemokine ligands (CCLs) family and play an
important role in monocyte recruitment in the endometrium [88]. Increased expression of *CCL2*
mRNA was found associated with lipid accumulation induced uterine inflammation in obese rats [89].
The present results are similar with previous studies performed with full endometrial tissue, showing
the up-regulation of inflammatory response genes in SNEB cows [38]. This is also consistent with
several studies in mammals showing that metabolic imbalance, increased lipolysis and most
particularly NEFAs, play essential functions in the activation of TNF and TLRs signalling to promote
the release of pro-inflammatory molecules [90, 91]. Taken together, these studies and our present
findings suggest that SNEB and NEFAs activate pro-inflammatory pathways in the glandular
epithelium and stromal cells. On the contrary, in luminal epithelium, the adaptive immune response
(B cell-mediated immunity) and innate immunity, was represented by under-expressed genes such as
tracheal antimicrobial peptide (TAP), a beta-defensin gene, which was associated to the NF-κB
pathway [92], and by genes coding for immunoglobulin heavy variable chains that participates in the
antigen recognition. These observations need further confirmation. Our results indicate that SNEB
induces changes in immune responses, which are different in the three endometrial cell types. They
show also that these changes are still present, long after NEB has disappeared suggesting long term
effects of metabolic imbalance and NEFAs on the pro-inflammatory status of the glandular epithelium
and the stroma.

**Effect of NEB on genes related to maternal-conceptus recognition.** A large set of IFN-inducible genes such as *MX1*, *MX2*, *STAT1*, *JAK1*, *IFIH1*, *IFNGR2*, *ISG15*, *LY6G6C*, *OAS1Y*, *OAS1Z* and *IRF7* were under-expressed in ST of SNEB cows. A weaker expression of those genes that encode proteins
involved in IFN-T signalling could account for the decreased endometrium-related fertility in SNEB cows. In pregnant ruminants, IFN-T is the main pregnancy recognition signal [93], that allows the persistence of the corpus luteum and maintaining elevated progesterone concentrations by blocking oxytocin signalling and PGF2α secretion [94]. Oxytocin signalling has been associated with the maintenance of gap-junctions in luteal tissue [95] and intracellular calcium release in endometrial cells [96]. Differentially expressed genes and our STRING protein-protein network revealed in ST of SNEB cows showed an increase in expression of six genes encoding proteins belonging to the oxytocin signalling pathway namely PLCB4, ADCY8, CAMK2B, ITPR2, and MYLK (Figure 6). These changes are consistent with the over-expression of 10 genes related to tight junction such as MYH14, MYH11, PRKCZ, OCLN, CGN, IGSF5, TJP3, CLDN3, CLDN8 and CLDN23. Our data suggest that in ST of SNEB cows, the over- representation of oxytocin signalling and tight junction pathways results from the decreased expression of IFN-T inducible genes. The changes in ST are consistent with downstream changes related to PGF2α produced by both endometrial epithelial and stromal cells [97]. Furthermore the deregulation of this signalling pathway in SNEB cows is supported by changes in PTGFR which was over-expressed in ST but under-expressed in GE. In addition, other important genes encoding proteins with established functions critical for implantation such as IL1RAP, SOSC3 and AREG were found differentially expressed in SNEB cows. We observed a lower expression of the IL1RAP gene in ST of SNEB cows. The IL1RAP protein is a necessary part of the interleukin 1 receptor complex and is regulated by interleukin 1 beta (IL-1β). The over- expression of IL1R and IL1RAP under IL-1β regulation has been reported in the pig endometrium at day 12 of pregnancy to stimulate the expression of PTGS1 and PTGS2 genes which encode key enzymes for PGE2 and PGF2α synthesis [98]. Blocking IL1R signalling with an IL-1 receptor antagonist led to implantation failure in mice [99]. The reduced expression of IL1RAP in ST of SNEB cows may compromise the establishment of pregnancy, but this deserves further investigation in the cow. SOCS family genes (SOCS1-7) inhibit cytokine signalling through the JAK-STAT pathway and regulate IFNs, growth factors and hormones which are critical for implantation [100]). SOCS1-3 mRNAs are over-expressed at time of implantation in the endometrium of pregnant cows and their expression was induced by IFN-tau in endometrial cells in vitro [101]. The over-expression of SOCS3 mRNA in GE may
involved in IFN-T signalling could account for the decreased endometrium-related fertility in SNEB cows. In pregnant ruminants, IFN-T is the main pregnancy recognition signal [93], that allows the persistence of the corpus luteum and maintaining elevated progesterone concentrations by blocking oxytocin signalling and PGF2α secretion [94]. Oxytocin signalling has been associated with the maintenance of gap-junctions in luteal tissue [95] and intracellular calcium release in endometrial cells [96]. Differentially expressed genes and our STRING protein-protein network revealed in ST of SNEB cows showed an increase in expression of six genes encoding proteins belonging to the oxytocin signalling pathway namely PLCB4, ADCY8, CAMK2B, ITPR2, and MYLK (Figure 6). These changes are consistent with the over-expression of 10 genes related to tight junction such as MYH14, MYH11, PRKCZ, OCLN, CGN, IGSF5, TJP3, CLDN3, CLDN8 and CLDN23. Our data suggest that in ST of SNEB cows, the over- representation of oxytocin signalling and tight junction pathways results from the decreased expression of IFN-T inducible genes. The changes in ST are consistent with downstream changes related to PGF2α produced by both endometrial epithelial and stromal cells [97]. Furthermore the deregulation of this signalling pathway in SNEB cows is supported by changes in PTGFR which was over-expressed in ST but under-expressed in GE. In addition, other important genes encoding proteins with established functions critical for implantation such as IL1RAP, SOSC3 and AREG were found differentially expressed in SNEB cows. We observed a lower expression of the IL1RAP gene in ST of SNEB cows. The IL1RAP protein is a necessary part of the interleukin 1 receptor complex and is regulated by interleukin 1 beta (IL-1β). The over- expression of IL1R and IL1RAP under IL-1β regulation has been reported in the pig endometrium at day 12 of pregnancy to stimulate the expression of PTGS1 and PTGS2 genes which encode key enzymes for PGE2 and PGF2α synthesis [98]. Blocking IL1R signalling with an IL-1 receptor antagonist led to implantation failure in mice [99]. The reduced expression of IL1RAP in ST of SNEB cows may compromise the establishment of pregnancy, but this deserves further investigation in the cow. SOCS family genes (SOCS1-7) inhibit cytokine signalling through the JAK-STAT pathway and regulate IFNs, growth factors and hormones which are critical for implantation [100]). SOCS1-3 mRNAs are over-expressed at time of implantation in the endometrium of pregnant cows and their expression was induced by IFN-tau in endometrial cells in vitro [101]. The over-expression of SOCS3 mRNA in GE may
1

contribute to down regulate the JAK/STAT pathway in the neighbouring ST cells, as reported above.

2

AREG was over-expressed in GE of SNEB cows. AREG gene is known as an epidermal growth factor

3

receptor and is involved in cell growth, proliferation, differentiation and migration. It is highly

4

expressed in luminal and glandular epithelium during the secretory phase of menstrual cycle and early

5

pregnancy in human and primate [102]. As for SOCS3, it could be speculated that the over-expression

6

of AREG mRNAs in GE may be part of a compensatory mechanisms in response to the increased

7

expression of cytokines in these cells. It would be interesting to compare the amplitude of over-

8

expression of SOCS3 and AREG in the present situation (luteal phase under cyclic conditions) and in

9

pregnancy to evaluate possible impacts of NEB on implantation.

10

Conclusion

11

The present study provides novel and specific information about gene expression in three endometrial

12

cell types from postpartum dairy cows and illustrates specific signatures in ST, LE and GE cells. We

13

also show that the impacts of negative energy balance on the gene expression of endometrial cells are

14

cell type specific. Major and specific changes in gene expression were observed in stromal cells

15

illustrating dysregulation of metabolic processes especially lipid and carbohydrate metabolism,

16

cytoskeleton and cell adhesion properties. Altered gene expression of endometrial epithelial cells

17

under SNEB condition was related to activation of pro-inflammatory responses via chemokine

18

pathway in GE, whereas down-regulation on adaptive immunity and defence mechanism were found

19

in LE. Strong changes in the expression of genes involved in prostaglandin production and maternal-

20

conceptus recognition was found in ST and in GE. Considering the above and the crucial role of IFN-

21

tau for embryo implantation and maintenance pregnancy, our hypothesis is that the under-expression

22

of IFN-tau responsive genes associated with the increased expression to oxytocin and PGF2α related

23

genes may be detrimental for the establishment of pregnancy in SNEB cows. The changes in gene

24

expression induced by NEB in LE should be considered as preliminary and needs further confirmation

25

whereas the specific response of ST and GE to NEB paves the way for functional studies relating the

26

importance of these changes for the establishment of pregnancy
1

contribute to down regulate the JAK/STAT pathway in the neighbouring ST cells, as reported above.

2

AREG was over-expressed in GE of SNEB cows. AREG gene is known as an epidermal growth factor

3

receptor and is involved in cell growth, proliferation, differentiation and migration. It is highly

4

expressed in luminal and glandular epithelium during the secretory phase of menstrual cycle and early

5

pregnancy in human and primate [102]. As for SOCS3, it could be speculated that the over-expression

6

of AREG mRNAs in GE may be part of a compensatory mechanisms in response to the increased

7

expression of cytokines in these cells. It would be interesting to compare the amplitude of over-

8

expression of SOCS3 and AREG in the present situation (luteal phase under cyclic conditions) and in

9

pregnancy to evaluate possible impacts of NEB on implantation.

10

Conclusion

11

The present study provides novel and specific information about gene expression in three endometrial

12

cell types from postpartum dairy cows and illustrates specific signatures in ST, LE and GE cells. We

13

also show that the impacts of negative energy balance on the gene expression of endometrial cells are

14

cell type specific. Major and specific changes in gene expression were observed in stromal cells

15

illustrating dysregulation of metabolic processes especially lipid and carbohydrate metabolism,

16

cytoskeleton and cell adhesion properties. Altered gene expression of endometrial epithelial cells

17

under SNEB condition was related to activation of pro-inflammatory responses via chemokine

18

pathway in GE, whereas down-regulation on adaptive immunity and defence mechanism were found

19

in LE. Strong changes in the expression of genes involved in prostaglandin production and maternal-

20

conceptus recognition was found in ST and in GE. Considering the above and the crucial role of IFN-

21

tau for embryo implantation and maintenance pregnancy, our hypothesis is that the under-expression

22

of IFN-tau responsive genes associated with the increased expression to oxytocin and PGF2α related

23

genes may be detrimental for the establishment of pregnancy in SNEB cows. The changes in gene

24

expression induced by NEB in LE should be considered as preliminary and needs further confirmation

25

whereas the specific response of ST and GE to NEB paves the way for functional studies relating the

26

importance of these changes for the establishment of pregnancy
1 **Abbreviations**

2 BCS: Body condition score

3 CIDR: Controlled Internal Drug Release)

4 DAVID: Database for annotation, visualization and integrated discovery

5 DEG: Differentially expressed gene

6 EB: Energy balance

7 ECM: Energy-corrected milk

8 Elisa: enzyme-linked immunosorbent assay

9 FDR: False discovery rate

10 GE: Glandular epithelial cell

11 GO: Gene ontology

12 KEGG: Kyoto encyclopedia of genes and genomes

13 LCM: Laser capture microdissection

14 LE: Luminal epithelial cell

15 MNEB: Mild energy balance

16 NEB: Negative energy balance

17 NEFAs: Non-esterified fatty acids

18 NorFor: Nordic Feed Evaluation System

19 OCT: Optimal cutting temperature compound

20 PANTHER: Protein analysis through evolutionary relationships

21 PCA: Principal Component Analysis

22 PGE2: Prostaglandin-E2

23 PGF2α: Prostaglandin-F2α

24 REVIGO: Reduce, visualize gene ontology
1 **Abbreviations**

2 BCS: Body condition score

3 CIDR: Controlled Internal Drug Release)

4 DAVID: Database for annotation, visualization and integrated discovery

5 DEG: Differentially expressed gene

6 EB: Energy balance

7 ECM: Energy-corrected milk

8 Elisa: enzyme-linked immunosorbent assay

9 FDR: False discovery rate

10 GE: Glandular epithelial cell

11 GO: Gene ontology

12 KEGG: Kyoto encyclopedia of genes and genomes

13 LCM: Laser capture microdissection

14 LE: Luminal epithelial cell

15 MNEB: Mild energy balance

16 NEB: Negative energy balance

17 NEFAs: Non-esterified fatty acids

18 NorFor: Nordic Feed Evaluation System

19 OCT: Optimal cutting temperature compound

20 PANTHER: Protein analysis through evolutionary relationships

21 PCA: Principal Component Analysis

22 PGE2: Prostaglandin-E2

23 PGF2α: Prostaglandin-F2α

24 REVIGO: Reduce, visualize gene ontology
1 RFI: Residual feed intake

2 RNA: Ribonucleic acid

3 RNA-Seq: RNA sequencing

4 SRB: Swedish Red breed

5 ST: Stromal cell

6 STRING: Search Tool for the Retrieval of Interacting Genes/Proteins

7 TPM: Transcripts per million

8
1 RFI: Residual feed intake

2 RNA: Ribonucleic acid

3 RNA-Seq: RNA sequencing

4 SRB: Swedish Red breed

5 ST: Stromal cell

6 STRING: Search Tool for the Retrieval of Interacting Genes/Proteins

7 TPM: Transcripts per million

8
1 **Declarations**

2

3 **Ethics approval and consent to participate**

4 All experimental protocols were approved by the Uppsala Animal Experiment Ethics Board

5 (application C329/12, PROLIFIC)(Uppsala University, Sweden) and were carried out in accordance

6 with the terms of the Swedish Animal Welfare Act. After the study was completed, all cows were kept

7 alive under normal husbandry conditions.

8 **Consent for publication**

9 « Not applicable »

10 **Availability of data and materials**

11 The data will be deposited pending acceptance of publication. The datasets generated and/or analyzed

12 during the current study will be available in the [NCBI/Gene expression omnibus)] repository,

13 [https://www.ncbi.nlm.nih.gov/geo/info/seq.html]

14 **Competing interests**

15 The authors declare that they have no competing interests.

16 **Funding**

17 This work was funded by from European Union [FP7-KBBE-2012-6, Prolific (Pluridisciplinary study

18 for a RObust and sustainanLe Improvement of the Fertility In Cows,) Grant agreement number

19 311776) for animals, reagents, sequencing and travel. WC was supported by the Rajamangala

20 University of Technology Srivijaya (RMUTSV), Thailand. The funders had no role in study design,

21 data collection, and interpretation, or the decision to submit the work for publication.

22 **Authors' contributions**

23 W.C., P.H. and G.C. contributed to the conception and design of the study. S.L., M.R., C.R., C.B. and

24 T.N. contributed to sample collection and preparation. G.C., D.M., Y.G., W.C. and P.H. performed

25 bioinformatics analysis and integration of data. W.C. performed the experiment, sample collection

26 and preparation, data analyses and W.C., G.C., and P.H. drafted the manuscript. All authors provided

27 critical feedback and helped shape research, analyses and manuscript. GC and PH are both senior co-

28 authorship.

29 **Acknowledgements**

30 We acknowledge Pierrette Reinaud (INRAE, Jouy en Josas, France) and Olivier Dubois for consulting

31 during the LCM process and RNA analysis. The authors would like to thank the staff of Swedish

32 Research Center, Lövsta, Uppsala, Sweden and Biology of Reproduction, Epigenetic, Environment

33 and Development (BREED), INRA, Jouy en Josas, France for their help and support.

34 **Authors' information (optional)**

35
1 **Declarations**

2

3 **Ethics approval and consent to participate**

4 All experimental protocols were approved by the Uppsala Animal Experiment Ethics Board

5 (application C329/12, PROLIFIC)(Uppsala University, Sweden) and were carried out in accordance

6 with the terms of the Swedish Animal Welfare Act. After the study was completed, all cows were kept

7 alive under normal husbandry conditions.

8 **Consent for publication**

9 « Not applicable »

10 **Availability of data and materials**

11 The data will be deposited pending acceptance of publication. The datasets generated and/or analyzed

12 during the current study will be available in the [NCBI/Gene expression omnibus)] repository,

13 [https://www.ncbi.nlm.nih.gov/geo/info/seq.html]

14 **Competing interests**

15 The authors declare that they have no competing interests.

16 **Funding**

17 This work was funded by from European Union [FP7-KBBE-2012-6, Prolific (Pluridisciplinary study

18 for a RObust and sustainanLe Improvement of the Fertility In Cows,) Grant agreement number

19 311776) for animals, reagents, sequencing and travel. WC was supported by the Rajamangala

20 University of Technology Srivijaya (RMUTSV), Thailand. The funders had no role in study design,

21 data collection, and interpretation, or the decision to submit the work for publication.

22 **Authors' contributions**

23 W.C., P.H. and G.C. contributed to the conception and design of the study. S.L., M.R., C.R., C.B. and

24 T.N. contributed to sample collection and preparation. G.C., D.M., Y.G., W.C. and P.H. performed

25 bioinformatics analysis and integration of data. W.C. performed the experiment, sample collection

26 and preparation, data analyses and W.C., G.C., and P.H. drafted the manuscript. All authors provided

27 critical feedback and helped shape research, analyses and manuscript. GC and PH are both senior co-

28 authorship.

29 **Acknowledgements**

30 We acknowledge Pierrette Reinaud (INRAE, Jouy en Josas, France) and Olivier Dubois for consulting

31 during the LCM process and RNA analysis. The authors would like to thank the staff of Swedish

32 Research Center, Lövsta, Uppsala, Sweden and Biology of Reproduction, Epigenetic, Environment

33 and Development (BREED), INRA, Jouy en Josas, France for their help and support.

34 **Authors' information (optional)**

35
# 1 Methods

2

*Animals and experimental design.* This study was approved by the Uppsala Animal Experiment Ethics

3

Board (application C329/12, PROLIFIC). After the study was conducted all cows have been kept in

4

usual farm living conditions. The animals used in this study were second lactation cows of the

5

Swedish Red breed (SRB; n = 12) fed two different diets i.e. i) high-energy diet (control, n=6)

6

targeting 35 kg energy-corrected milk (ECM) and ii) low-energy diet targeting (n=6) 25 kg energy-

7

corrected milk (ECM) which was achieved by giving to these cows 50% concentrate. All cows were

8

conducted at the Swedish Livestock Research Centre in Lövsta, Uppsala, Sweden. For each cow, the

9

differential diets were given between 30 days prepartum and 120 days postpartum. The animals were

10

kept in a loose housing barn with a voluntary milking system (VMS, DeLaval, Tumba, Sweden), and

11

had free access to drinking water. The dietary details and management conditions were previously

12

described [32]. During the experiment, consumption of concentrate was individually adjusted with an

13

automatic feeding machine while forage was fed *ad libitum*. At day 60 after calving, estrous was

14

synchronized using an intra-vaginal progesterone device (CIDR, Zoetis, Parsippany, NJ, USA) for a

15

week followed by i.m. injection of 500 µg of prostaglandin analog (Estrumate®, MSD animal health,

16

Madison, NJ, USA) intramuscular as described [103]. Fifteen days after visual oestrus detection,

17

endometrial tissue biopsies were collected under epidural anesthesia with 0.5 mg/kg of 1% lidocaine

18

hydrochloride (1% Xylocaine®, Astra Zeneca, Cambridge, UK). Timeline for samplings and analysis

19

of phenotypic responses are presented in supplemental Figure.S1.

20

*Energy balance (EB) calculation and classification.* The energy balance (EB) (residual feed intake

21

(RFI) expressed in MJ/day) was calculated as the difference between energy consumed and energy

22

used for milk production, body maintenance, growth and pregnancy for each individual cow.

23

Calculations were performed once per week from first week after calving to day 120 as described in

24

[104]. All data used were routinely recorded in the university herd and energy balance calculation was

25

performed with NorFor used as the reference system in the Nordic countries. Based on most

26

differentiated EB profiles, nine out of twelve cows were classified into two NEB groups with either a

27

mild negative energy balance (MNEB) group (n = 5) or a severe negative energy balance (SNEB)
# 1 Methods

2

*Animals and experimental design.* This study was approved by the Uppsala Animal Experiment Ethics

3

Board (application C329/12, PROLIFIC). After the study was conducted all cows have been kept in

4

usual farm living conditions. The animals used in this study were second lactation cows of the

5

Swedish Red breed (SRB; n = 12) fed two different diets i.e. i) high-energy diet (control, n=6)

6

targeting 35 kg energy-corrected milk (ECM) and ii) low-energy diet targeting (n=6) 25 kg energy-

7

corrected milk (ECM) which was achieved by giving to these cows 50% concentrate. All cows were

8

conducted at the Swedish Livestock Research Centre in Lövsta, Uppsala, Sweden. For each cow, the

9

differential diets were given between 30 days prepartum and 120 days postpartum. The animals were

10

kept in a loose housing barn with a voluntary milking system (VMS, DeLaval, Tumba, Sweden), and

11

had free access to drinking water. The dietary details and management conditions were previously

12

described [32]. During the experiment, consumption of concentrate was individually adjusted with an

13

automatic feeding machine while forage was fed *ad libitum*. At day 60 after calving, estrous was

14

synchronized using an intra-vaginal progesterone device (CIDR, Zoetis, Parsippany, NJ, USA) for a

15

week followed by i.m. injection of 500 µg of prostaglandin analog (Estrumate®, MSD animal health,

16

Madison, NJ, USA) intramuscular as described [103]. Fifteen days after visual oestrus detection,

17

endometrial tissue biopsies were collected under epidural anesthesia with 0.5 mg/kg of 1% lidocaine

18

hydrochloride (1% Xylocaine®, Astra Zeneca, Cambridge, UK). Timeline for samplings and analysis

19

of phenotypic responses are presented in supplemental Figure.S1.

20

*Energy balance (EB) calculation and classification.* The energy balance (EB) (residual feed intake

21

(RFI) expressed in MJ/day) was calculated as the difference between energy consumed and energy

22

used for milk production, body maintenance, growth and pregnancy for each individual cow.

23

Calculations were performed once per week from first week after calving to day 120 as described in

24

[104]. All data used were routinely recorded in the university herd and energy balance calculation was

25

performed with NorFor used as the reference system in the Nordic countries. Based on most

26

differentiated EB profiles, nine out of twelve cows were classified into two NEB groups with either a

27

mild negative energy balance (MNEB) group (n = 5) or a severe negative energy balance (SNEB)
1

2

3

group (n = 4). Residual feed intake values in the first week postpartum of these nine cows ranged from -52.77 to 21.26 MJ/day and means (± s.e.m.) of 1.30 ± 6.35 and -29.48 ± 7.10 MJ/day were observed in the MNEB and SNEB groups, respectively.

4

5

6

*Body condition score (BCS) and plasma NEFA measurements.* Body condition score (BCS) was evaluated and recorded by the same person every two weeks, from 30 days prepartum until 120 days postpartum. BCS was used on a 5 point scale with 0.5 point increments, 1 = very lean to 5 = fat [105].

7

8

9

10

11

12

13

Blood samples were taken every two weeks from the coccygeal vein in EDTA containing tubes (BD Vacutainer, Kremsmünster, Austria) from 30 days prepartum to 56 days postpartum and then centrifuged at 4000 g for 10 min at 4°C. Following centrifugation, plasma samples were distributed into 0.5 mL aliquots and stored in -20° C until NEFA analyses were performed. NEFA concentrations were measured in duplicate by using a non-esterified fatty assay kit (Bio Scientific Corporation, Austin, TX, USA) with detection range 0 – 4 mM. The intra- and inter-assay variability was 4.19 ± 3.99% and 2.63 ± 1.08%, respectively.

14

15

16

17

18

19

20

21

*Milk progesterone measurements and estrous cycle stage at time of biopsies.* Whole milk samples were collected by the automatic milking machine, VMS (DeLaval) three times per week from Day 7 to Day 120 after calving. Milk progesterone concentrations were measured with a commercial enzyme-linked immunosorbent assay (ELISA) (Ridge way 'M' kit, Ridgeway Science, Gloucester, UK) as previously published [32]. The progesterone concentration profile was used to determine the estrous cycle stage at the time of biopsy sampling. All cows selected were in the luteal phase at time of endometrial biopsy as shown by their mean (± s.e.m.) progesterone concentration (8.78 ± 2.12 ng/mL; range from 6.66 to 10.90 ng/ml).

22

23

24

25

26

*Collection of endometrial biopsies.* Endometrial biopsies were collected from the uterine horn ipsilateral to the corpus luteum by using Kevorkian-Younge uterine biopsy forceps (Alcyon, Paris, France). Biopsies were cut into 3 pieces (sizes ≈ 4×4 mm²). One of them was snap frozen in cold isopentane (2-Methylbutane, Sigma Aldrich, Saint Louis, MO, USA) previously placed in liquid nitrogen for 5 min, and immediately embedded in ≈1 cm³ optimal cutting temperature (OCT)
1

2

3

group (n = 4). Residual feed intake values in the first week postpartum of these nine cows ranged from -52.77 to 21.26 MJ/day and means (± s.e.m.) of 1.30 ± 6.35 and -29.48 ± 7.10 MJ/day were observed in the MNEB and SNEB groups, respectively.

4

5

6

*Body condition score (BCS) and plasma NEFA measurements.* Body condition score (BCS) was evaluated and recorded by the same person every two weeks, from 30 days prepartum until 120 days postpartum. BCS was used on a 5 point scale with 0.5 point increments, 1 = very lean to 5 = fat [105].

7

8

9

10

11

12

13

Blood samples were taken every two weeks from the coccygeal vein in EDTA containing tubes (BD Vacutainer, Kremsmünster, Austria) from 30 days prepartum to 56 days postpartum and then centrifuged at 4000 g for 10 min at 4°C. Following centrifugation, plasma samples were distributed into 0.5 mL aliquots and stored in -20° C until NEFA analyses were performed. NEFA concentrations were measured in duplicate by using a non-esterified fatty assay kit (Bio Scientific Corporation, Austin, TX, USA) with detection range 0 – 4 mM. The intra- and inter-assay variability was 4.19 ± 3.99% and 2.63 ± 1.08%, respectively.

14

15

16

17

18

19

20

21

*Milk progesterone measurements and estrous cycle stage at time of biopsies.* Whole milk samples were collected by the automatic milking machine, VMS (DeLaval) three times per week from Day 7 to Day 120 after calving. Milk progesterone concentrations were measured with a commercial enzyme-linked immunosorbent assay (ELISA) (Ridge way ‘M’ kit, Ridgeway Science, Gloucester, UK) as previously published [32]. The progesterone concentration profile was used to determine the estrous cycle stage at the time of biopsy sampling. All cows selected were in the luteal phase at time of endometrial biopsy as shown by their mean (± s.e.m.) progesterone concentration (8.78 ± 2.12 ng/mL; range from 6.66 to 10.90 ng/ml).

22

23

24

25

26

*Collection of endometrial biopsies.* Endometrial biopsies were collected from the uterine horn ipsilateral to the corpus luteum by using Kevorkian-Younge uterine biopsy forceps (Alcyon, Paris, France). Biopsies were cut into 3 pieces (sizes ≈ 4×4 mm²). One of them was snap frozen in cold isopentane (2-Methylbutane, Sigma Aldrich, Saint Louis, MO, USA) previously placed in liquid nitrogen for 5 min, and immediately embedded in ≈1 cm³ optimal cutting temperature (OCT)
1

compound (VWR, Radnor, PA, USA). OCT conditioned biopsies were then put into dry ice and kept

2

at -80°C until sectioning. Tissue blocks were 8 µm sectioned with a cryostat (Leica CM1860 Cryostat,

3

Wetzlar, Germany) at -20°C under RNA-free conditions. Tissue section slices were mounted on Super

4

Frost slides RNA-free which were chilled on ice, following immersion in ice-cold 75% RNA-free

5

ethanol and stored at -80°C until staining [106].

6

*Laser capture microdissection (LCM) and RNA isolation.* All procedures used were those previously

7

published [106]. Tissue sections were mounted on RNAse-free glass slides which were chilled on ice,

8

following immersion in cold 75% RNA-free ethanol at -20°C in the cryostat and then transferred into

9

75% ethanol at RT (30 sec), stained with 1% cresyl violet in ethanol (15 sec), rinsed successively with

10

75% ethanol (30 sec), 95% ethanol (2 x 1 min), and 100% ethanol (2 x 1 min) (anhydrous Ethanol

11

absolute). Finally, the slides were completely dehydrated by immersion in pure xylene (M-xylene,

12

Sigma-Aldrich, Saint-Quentin-Fallavier, France) for 2 × 5 min. Stained tissue sections were then

13

immediately air dried. The LCM process was performed by using an ArcturusXT™ Laser Capture

14

Microdissection System and software (Applied Biosystems®, Arcturus, ThermoFisher Scientific,

15

Waltham, MA, USA), within 1 h to avoid RNA degradation. Luminal epithelial cells (LE), glandular

16

epithelial cells (GE) and stromal cells (ST), were harvested in sufficient numbers to obtain at least 5

17

ng of total RNA for each endometrial cell type. Briefly, cells were captured from the slide onto LCM

18

plastic caps (CapSure® Macro LCM Caps, Arcturus) by using infrared laser with the following

19

settings: power range 75 to 90mW, time 1300 to 3500µsec and 200mV intensity. Collected cells were

20

then placed in a RNAse-free 0.5 mL microcentrifuge tube with 25 µL extraction buffer (provided

21

together with the PicoPure™RNA isolation kit; KIT0202, Arcturus) and incubated for 30 min at

22

42°C. The histology of each endometrial cell type before and after capture with LCM is presented in

23

Figure 7. Captured cells in PicoPure extraction buffer were frozen at -80°C before processing samples

24

for RNA isolation. Total RNA from LCM samples was isolated and mRNA purified using the

25

PicoPure™RNA isolation kit (KIT0202, Arcturus) following the manufacturer's protocol. RNA

26

integrity value (RIN values) and quantity were evaluated using the Pico RNA chip on the Agilent

27

2100 Bioanalyzer (Agilent technologies, Santa Clara, CA, USA). Mean RNA integrity (RIN) values
1

compound (VWR, Radnor, PA, USA). OCT conditioned biopsies were then put into dry ice and kept

2

at -80°C until sectioning. Tissue blocks were 8 µm sectioned with a cryostat (Leica CM1860 Cryostat,

3

Wetzlar, Germany) at -20°C under RNA-free conditions. Tissue section slices were mounted on Super

4

Frost slides RNA-free which were chilled on ice, following immersion in ice-cold 75% RNA-free

5

ethanol and stored at -80°C until staining [106].

6

*Laser capture microdissection (LCM) and RNA isolation.* All procedures used were those previously

7

published [106]. Tissue sections were mounted on RNAse-free glass slides which were chilled on ice,

8

following immersion in cold 75% RNA-free ethanol at -20°C in the cryostat and then transferred into

9

75% ethanol at RT (30 sec), stained with 1% cresyl violet in ethanol (15 sec), rinsed successively with

10

75% ethanol (30 sec), 95% ethanol (2 x 1 min), and 100% ethanol (2 x 1 min) (anhydrous Ethanol

11

absolute). Finally, the slides were completely dehydrated by immersion in pure xylene (M-xylene,

12

Sigma-Aldrich, Saint-Quentin-Fallavier, France) for 2 × 5 min. Stained tissue sections were then

13

immediately air dried. The LCM process was performed by using an ArcturusXT™ Laser Capture

14

Microdissection System and software (Applied Biosystems®, Arcturus, ThermoFisher Scientific,

15

Waltham, MA, USA), within 1 h to avoid RNA degradation. Luminal epithelial cells (LE), glandular

16

epithelial cells (GE) and stromal cells (ST), were harvested in sufficient numbers to obtain at least 5

17

ng of total RNA for each endometrial cell type. Briefly, cells were captured from the slide onto LCM

18

plastic caps (CapSure® Macro LCM Caps, Arcturus) by using infrared laser with the following

19

settings: power range 75 to 90mW, time 1300 to 3500µsec and 200mV intensity. Collected cells were

20

then placed in a RNAse-free 0.5 mL microcentrifuge tube with 25 µL extraction buffer (provided

21

together with the PicoPure™RNA isolation kit; KIT0202, Arcturus) and incubated for 30 min at

22

42°C. The histology of each endometrial cell type before and after capture with LCM is presented in

23

Figure 7. Captured cells in PicoPure extraction buffer were frozen at -80°C before processing samples

24

for RNA isolation. Total RNA from LCM samples was isolated and mRNA purified using the

25

PicoPure™RNA isolation kit (KIT0202, Arcturus) following the manufacturer's protocol. RNA

26

integrity value (RIN values) and quantity were evaluated using the Pico RNA chip on the Agilent

27

2100 Bioanalyzer (Agilent technologies, Santa Clara, CA, USA). Mean RNA integrity (RIN) values
1
2

obtained from LCM samples and from the full tissue samples issued from the same biopsy were
similar (paired T-test; Table S9).

3
4
5
6
7
8
9

*RNA sequencing and data analysis.* RNA sequencing libraries prepared from 24 samples (number of samples in each NEB group and endometrial cell types presented in Table S9) were prepared and sequenced on GenomEast Platform (IGBMC, Cedex, France; http://genomeast.igbmc.fr/). Libraries were built using the Clontech SMART-Seq v4 Ultra Low Input RNA kit for Sequencing. Full length cDNA were generated from 4 ng of total RNA using Clontech SMART-Seq v4 Ultra Low Input RNA kit for Sequencing (Takara Bio Europe, Ozyme, Montigny-Le-Bretonneux, France) according to manufacturer's instructions, with 10 cycles of PCR for cDNA amplification by Seq-Amp polymerase.

10 Then, 600 pg of pre-amplified cDNA were then used as input for Tn5 transposon tagmentation using
11 the Nextera XT DNA Library Preparation Kit (Illumina, San Diego, CA) followed by 12 cycles of
12 library amplification. Following purification with Agencourt AMPure XP beads (Beckman-Coulter,
13 Roissy, France), the size and concentration of libraries were assessed by capillary
14 electrophoresis. Sequencing was performed on an Illumina HiSeq 4000 with 50 bp paired-end reads.
15 Image analysis and base calling were performed using RTA 2.7.3 and bcl2fastq 2.17.1.14. Gene level
16 exploratory analysis and differential expression were performed using the RNAseq workflow
17 described by [107] and the update version https://bioconductor.org/help/course-
18 materials/2017/CSAMA/labs/2-tuesday/lab-03-rnaseq/rnaseqGene_CSAMA2017.html). The Salmon
19 method [108] was used to quantify transcript abundance. The cDNA sequence database for *Bos taurus*
20 was obtained from Ensembl (release-98; Bos_taurus.ARS-UCD1.2.cdna.all.fa) and was used to build
21 a reference index for the bovine transcriptome (see details in [108]. After quantifying RNA-seq data,
22 tximport method [109] (R package version 1.8.0) was used to import Salmon's transcript-level
23 quantifications to the downstream DESeq2 package (R package, version 1.20.0) for analysis of
24 differential expressed genes (DEGs) with the statistical method proposed [110]. Principal component
25 analysis was performed with DESeq2 and with FactoMineR (R package, version 1.4.1) using the
26 variance stabilizing transformation output files from DESeq2. Heatmap was generated in R software
27 using the pheatmap package (version 1.0.12) and Venn diagrams were plotted with VennDiagram
1
2

obtained from LCM samples and from the full tissue samples issued from the same biopsy were
similar (paired T-test; Table S9).

3
4
5
6
7
8
9

Then, 600 pg of pre-amplified cDNA were then used as input for Tn5 transposon tagmentation using
the Nextera XT DNA Library Preparation Kit (Illumina, San Diego, CA) followed by 12 cycles of
library amplification. Following purification with Agencourt AMPure XP beads (Beckman-Coulter,
Roissy, France), the size and concentration of libraries were assessed by capillary
electrophoresis. Sequencing was performed on an Illumina HiSeq 4000 with 50 bp paired-end reads.
Image analysis and base calling were performed using RTA 2.7.3 and bcl2fastq 2.17.1.14. Gene level
exploratory analysis and differential expression were performed using the RNAseq workflow
described by [107] and the update version https://bioconductor.org/help/course-
materials/2017/CSAMA/labs/2-tuesday/lab-03-rnaseq/rnaseqGene_CSAMA2017.html). The Salmon
method [108] was used to quantify transcript abundance. The cDNA sequence database for Bos taurus
was obtained from Ensembl (release-98; Bos taurus.ARS-UCD1.2.cdna.all.fa) and was used to build
a reference index for the bovine transcriptome (see details in [108]. After quantifying RNA-seq data,
tximport method [109] (R package version 1.8.0) was used to import Salmon's transcript-level
quantifications to the downstream DESeq2 package (R package, version 1.20.0) for analysis of
differential expressed genes (DEGs) with the statistical method proposed [110]. Principal component
analysis was performed with DESeq2 and with FactoMineR (R package, version 1.4.1) using the
variance stabilizing transformation output files from DESeq2. Heatmap was generated in R software
using the pheatmap package (version 1.0.12) and Venn diagrams were plotted with VennDiagram
1

package (1.6.20). DEGs of specific-endometrial cell samples were identified in comparison between SNEB and MNEB group with an adjusted *p*-value of 0.05. Volcano plot was applied to gene lists of each endometrial cell type considering the log2 fold change between SNEB and MNEB on the *x* axis and the negative log10 of the adjusted *p*-value on the *y* axis.

2

3

4

5

*Gene ontology and KEGG Pathway Analysis.* Lists of genes expressed by the three types of endometrial cells as well as sets of over- or under-expressed DEGs between SNEB and MNEB were annotated into three categories of Gene Ontology (GO) pathways such as biological process (BP), cellular component (CC) and molecular function (MP) using PANTHER classification system (Protein Analysis Through Evolutionary Relationships version 14.0, http://pantherdb.org). PANTHER overrepresentation tests were performed using all genes from the whole Bos taurus genome or from specified list. Lists of GO terms were summarized and visualized in semantic space by REVIGO (http://revigo.irb.hr/) [111]. The SimRel semantic similarity score was used and the threshold was set at 0.15. Moreover, the analysis of enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was performed using Database for Annotation, Visualization and Integrated Discovery software (DAVID version 6.8, https://david.ncifcrf.gov/summary.jsp). If a KEGG pathway was determined to be significantly enriched (Benjamini- adjusted *p*-value < 0.05), this significant process/pathway was reported. By using DEGs which are involved in significant KEGG pathways, a molecular interaction network analysis was generated by using STRING database (STRING version 10.5, http://string-db.org/) (Szklarczyk et al. Nucleic Acids Res. 2015 43(Database issue):D447-52) at medium confidence level (0.4) for giving an overview of the genes networks and their interactions.

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

**Statistical analysis**

23

24

25

26

The statistical analyzes for phenotype parameters (BCS, NEFA concentrations) were performed using the Statistical Analysis System Software (SAS® version 9.4, SAS Institute Inc., Cary, NC, USA) and analyzed by mixed models with repeated measurement (Proc MIXED). All variables were checked for normality and data were log10-transformed if needed. The effect of the cow was considered as
1

package (1.6.20). DEGs of specific-endometrial cell samples were identified in comparison between SNEB and MNEB group with an adjusted *p*-value of 0.05. Volcano plot was applied to gene lists of each endometrial cell type considering the log2 fold change between SNEB and MNEB on the *x* axis and the negative log10 of the adjusted *p*-value on the *y* axis.

2

3

4

5

*Gene ontology and KEGG Pathway Analysis.* Lists of genes expressed by the three types of endometrial cells as well as sets of over- or under-expressed DEGs between SNEB and MNEB were annotated into three categories of Gene Ontology (GO) pathways such as biological process (BP), cellular component (CC) and molecular function (MP) using PANTHER classification system (Protein Analysis Through Evolutionary Relationships version 14.0, http://pantherdb.org).

6

7

8

9

10

PANTHER overrepresentation tests were performed using all genes from the whole Bos taurus genome or from specified list. Lists of GO terms were summarized and visualized in semantic space by REVIGO (http://revigo.irb.hr/) [111]. The SimRel semantic similarity score was used and the threshold was set at 0.15. Moreover, the analysis of enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was performed using Database for Annotation, Visualization and Integrated Discovery software (DAVID version 6.8, https://david.ncifcrf.gov/summary.jsp). If a KEGG pathway was determined to be significantly enriched (Benjamini- adjusted *p*-value < 0.05), this significant process/pathway was reported. By using DEGs which are involved in significant KEGG pathways, a molecular interaction network analysis was generated by using STRING database (STRING version 10.5, http://string-db.org/) (Szklarczyk et al. Nucleic Acids Res. 2015 43 Database issue):D447-52) at medium confidence level (0.4) for giving an overview of the genes networks and their interactions.

11

12

13

14

15

16

17

18

19

20

21

22

**Statistical analysis**

23

The statistical analyzes for phenotype parameters (BCS, NEFA concentrations) were performed using the Statistical Analysis System Software (SAS® version 9.4, SAS Institute Inc., Cary, NC, USA) and analyzed by mixed models with repeated measurement (Proc MIXED). All variables were checked for normality and data were log10-transformed if needed. The effect of the cow was considered as

24

25

26
random when running the models. The model included NEB group, diet group and time of sampling defined as fixed effects and their second order interactions. Non-significant effects were progressively removed from models. Scheffe's post hoc test was used for multiple comparisons and also the "estimate" and "contrast" statements under Proc MIXED were used for pairwise comparisons. Individual BCS loss from start of experiment until a nadir (the lowest postpartum value) and a nadir of feed residual intake (RFI) value after calving were recorded. Pearson correlation coefficients between the different variables were calculated using the Proc CORR function. The results of BCS, NEFA's concentration, and milk progesterone concentration are presented as LSmeans ± S.E.M. Differences with associated *p*-value < 0.05 were considered to be significant. In the statistical analysis of transcriptome profiles, generalized linear model was fitted and Wald test were performed to determine which of the observed fold changes were significantly different between severe and mild negative energy balance groups. *p*-values < 0.05 were considered to identify DEGs according to procedures described by [107].

14
random when running the models. The model included NEB group, diet group and time of sampling defined as fixed effects and their second order interactions. Non-significant effects were progressively removed from models. Scheffe's post hoc test was used for multiple comparisons and also the "estimate" and "contrast" statements under Proc MIXED were used for pairwise comparisons. Individual BCS loss from start of experiment until a nadir (the lowest postpartum value) and a nadir of feed residual intake (RFI) value after calving were recorded. Pearson correlation coefficients between the different variables were calculated using the Proc CORR function. The results of BCS, NEFA's concentration, and milk progesterone concentration are presented as LSmeans ± S.E.M. Differences with associated *p*-value < 0.05 were considered to be significant. In the statistical analysis of transcriptome profiles, generalized linear model was fitted and Wald test were performed to determine which of the observed fold changes were significantly different between severe and mild negative energy balance groups. *p*-values < 0.05 were considered to identify DEGs according to procedures described by [107].

14
1

References

2

1.

Butler W, Everett R, Coppock C: **The relationships between energy balance, milk production and ovulation in postpartum Holstein cows.** *Journal of animal science* 1981, **53**(3):742-748.

3

2.

Britt JH, Cushman RA, Dechow CD, Dobson H, Humblot P, Hutjens MF, Jones GA, Ruegg PS, Sheldon IM, Stevenson JS: **Invited review: Learning from the future—A vision for dairy farms and cows in 2067.** *Journal of Dairy Science* 2018, **101**(5):3722-3741.

5

6

3.

Harrison R, Ford S, Young J, Conley A, Freeman A: **Increased Milk Production Versus Reproductive and Energy Status of High Producing Dairy Cows1.** *Journal of Dairy Science* 1990, **73**(10):2749-2758.

8

9

10

4.

Butler WR: **Energy balance relationships with follicular development, ovulation and fertility in postpartum dairy cows.** *Livest Prod Sci* 2003, **83**:211-218.

11

5.

Barbat A, Le Mezec P, Ducrocq V, Mattalia S, Fritz S, Boichard D, Ponsart C, Humblot P: **Female fertility in French dairy breeds: current situation and strategies for improvement.** *Journal of Reproduction and Development* 2010, **56**(S):S15-S21.

13

14

6.

Grummer RR, Mashek DG, Hayirli A: **Dry matter intake and energy balance in the transition period.** *Veterinary Clinics: Food Animal Practice* 2004, **20**(3):447-470.

16

7.

Senatore E, Butler W, Oltenacu P: **Relationships between energy balance and post-partum ovarian activity and fertility in first lactation dairy cows.** *Animal Science* 1996, **62**(1):17-23.

18

8.

Swangchan-Uthai T, Chen QS, Kirton SE, Fenwick MA, Cheng ZR, Patton J, Fouladi-Nashta AA, Wathes DC: **Influence of energy balance on the antimicrobial peptides S100A8 and S100A9 in the endometrium of the post-partum dairy cow.** *Reproduction* 2013, **145**(5):527-539.

20

21

9.

Butler W, Smith R: **Interrelationships between energy balance and postpartum reproductive function in dairy cattle.** *Journal of dairy science* 1989, **72**(3):767-783.

23

24

10.

Esposito G, Irons PC, Webb EC, Chapwanya A: **Interactions between negative energy balance, metabolic diseases, uterine health and immune response in transition dairy cows.** *Anim Reprod Sci* 2014, **144**(3-4):60-71.

25

26

11.

Leroy JL, De Bie J, Jordaens L, Desmet K, Smits A, Marei WF, Bols PE, Van Hoeck V: **Negative energy balance and metabolic stress in relation to oocyte and embryo quality: an update on possible pathways reducing fertility in dairy cows.** In: *Animal Reproduction*. vol. 14; 2017: 497-506.

28

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Wathes DC, Cheng ZR, Chowdhury W, Fenwick MA, Fitzpatrick R, Morris DG, Patton J, Murphy JJ: **Negative energy balance alters global gene expression and immune responses in the uterus of postpartum dairy cows.** *Physiological Genomics* 2009, **39**(1):1-13.

32

33

13.

Wathes DC, Clemson AM, Pollott GE: **Associations between lipid metabolism and fertility in the dairy cow.** *Reprod Fert Develop* 2013, **25**(1):48-61.

35

36

14.

Martin L, Finn C: **Hormonal regulation of cell division in epithelial and connective tissues of the mouse uterus.** *Journal of Endocrinology* 1968, **41**(3):363-371.

37

38

15.

Bazer FW, Burghardt RC, Johnson GA, Spencer TE, Wu G: **Mechanisms for the establishment and maintenance of pregnancy: synergies from scientific collaborations†.** *Biology of Reproduction* 2018, **99**(1):225-241.

39

40

16.

Gray CA, Bartol FF, Tarleton BJ, Wiley AA, Johnson GA, Bazer FW, Spencer TE: **Developmental biology of uterine glands.** *Biol Reprod* 2001, **65**(5):1311-1323.

42

43

17.

Forde N, Lonergan P: **Transcriptomic analysis of the bovine endometrium: What is required to establish uterine receptivity to implantation in cattle?** *J Reprod Dev* 2012, **58**(2):189-195.

44

45

18.

Fortier M, Guilbault L, Grasso F: **Specific properties of epithelial and stromal cells from the endometrium of cows.** *Journal of Reproduction and Fertility* 1988, **83**(1):239-248.

46

47

19.

Schaefer TM, Desouza K, Fahey JV, Beagley KW, Wira CR: **Toll-like receptor (TLR) expression and TLR-mediated cytokine/chemokine production by human uterine epithelial cells.** *Immunology* 2004, **112**(3):428-436.

48

49
1

References

2

1.

Butler W, Everett R, Coppock C: **The relationships between energy balance, milk production and ovulation in postpartum Holstein cows.** *Journal of animal science* 1981, **53**(3):742-748.

3

2.

Britt JH, Cushman RA, Dechow CD, Dobson H, Humblot P, Hutjens MF, Jones GA, Ruegg PS, Sheldon IM, Stevenson JS: **Invited review: Learning from the future—A vision for dairy farms and cows in 2067.** *Journal of Dairy Science* 2018, **101**(5):3722-3741.

5

6

3.

Harrison R, Ford S, Young J, Conley A, Freeman A: **Increased Milk Production Versus Reproductive and Energy Status of High Producing Dairy Cows1.** *Journal of Dairy Science* 1990, **73**(10):2749-2758.

8

9

10

4.

Butler WR: **Energy balance relationships with follicular development, ovulation and fertility in postpartum dairy cows.** *Livest Prod Sci* 2003, **83**:211-218.

11

5.

Barbat A, Le Mezec P, Ducrocq V, Mattalia S, Fritz S, Boichard D, Ponsart C, Humblot P: **Female fertility in French dairy breeds: current situation and strategies for improvement.** *Journal of Reproduction and Development* 2010, **56**(S):S15-S21.

13

14

6.

Grummer RR, Mashek DG, Hayirli A: **Dry matter intake and energy balance in the transition period.** *Veterinary Clinics: Food Animal Practice* 2004, **20**(3):447-470.

16

7.

Senatore E, Butler W, Oltenacu P: **Relationships between energy balance and post-partum ovarian activity and fertility in first lactation dairy cows.** *Animal Science* 1996, **62**(1):17-23.

18

8.

Swangchan-Uthai T, Chen QS, Kirton SE, Fenwick MA, Cheng ZR, Patton J, Fouladi-Nashta AA, Wathes DC: **Influence of energy balance on the antimicrobial peptides S100A8 and S100A9 in the endometrium of the post-partum dairy cow.** *Reproduction* 2013, **145**(5):527-539.

20

21

9.

Butler W, Smith R: **Interrelationships between energy balance and postpartum reproductive function in dairy cattle.** *Journal of dairy science* 1989, **72**(3):767-783.

23

24

10.

Esposito G, Irons PC, Webb EC, Chapwanya A: **Interactions between negative energy balance, metabolic diseases, uterine health and immune response in transition dairy cows.** *Anim Reprod Sci* 2014, **144**(3-4):60-71.

25

26

11.

Leroy JL, De Bie J, Jordaens L, Desmet K, Smits A, Marei WF, Bols PE, Van Hoeck V: **Negative energy balance and metabolic stress in relation to oocyte and embryo quality: an update on possible pathways reducing fertility in dairy cows.** In: *Animal Reproduction*. vol. 14; 2017: 497-506.

28

29

30

12.

Wathes DC, Cheng ZR, Chowdhury W, Fenwick MA, Fitzpatrick R, Morris DG, Patton J, Murphy JJ: **Negative energy balance alters global gene expression and immune responses in the uterus of postpartum dairy cows.** *Physiological Genomics* 2009, **39**(1):1-13.

32

33

13.

Wathes DC, Clemson AM, Pollott GE: **Associations between lipid metabolism and fertility in the dairy cow.** *Reprod Fert Develop* 2013, **25**(1):48-61.

35

36

14.

Martin L, Finn C: **Hormonal regulation of cell division in epithelial and connective tissues of the mouse uterus.** *Journal of Endocrinology* 1968, **41**(3):363-371.

37

38

15.

Bazer FW, Burghardt RC, Johnson GA, Spencer TE, Wu G: **Mechanisms for the establishment and maintenance of pregnancy: synergies from scientific collaborations†.** *Biology of Reproduction* 2018, **99**(1):225-241.

40

41

16.

Gray CA, Bartol FF, Tarleton BJ, Wiley AA, Johnson GA, Bazer FW, Spencer TE: **Developmental biology of uterine glands.** *Biol Reprod* 2001, **65**(5):1311-1323.

42

43

17.

Forde N, Lonergan P: **Transcriptomic analysis of the bovine endometrium: What is required to establish uterine receptivity to implantation in cattle?** *J Reprod Dev* 2012, **58**(2):189-195.

45

18.

Fortier M, Guilbault L, Grasso F: **Specific properties of epithelial and stromal cells from the endometrium of cows.** *Journal of Reproduction and Fertility* 1988, **83**(1):239-248.

46

47

19.

Schaefer TM, Desouza K, Fahey JV, Beagley KW, Wira CR: **Toll-like receptor (TLR) expression and TLR-mediated cytokine/chemokine production by human uterine epithelial cells.** *Immunology* 2004, **112**(3):428-436.

48

49
1

2

3

Niklaus AL, Pollard JW: Mining the Mouse Transcriptome of Receptive Endometrium Reveals Distinct Molecular Signatures for the Luminal and Glandular Epithelium. *Endocrinology* 2006, **147**(7):3375-3390.

4

5

6

Gray CA, Taylor KM, Ramsey WS, Hill JR, Bazer FW, Bartol FF, Spencer TE: Endometrial glands are required for preimplantation conceptus elongation and survival. *Biol Reprod* 2001, **64**(6):1608-1613.

7

8

9

Demir R, Kayisli U, Celik-Ozenci C, Korgun E, Demir-Weusten A, Arici A: Structural differentiation of human uterine luminal and glandular epithelium during early pregnancy: an ultrastructural and immunohistochemical study. *Placenta* 2002, **23**(8-9):672-684.

10

11

12

13

Fazleabas A, Bazer F, Roberts RM: Purification and properties of a progesterone-induced plasmin/trypsin inhibitor from uterine secretions of pigs and its immunocytochemical localization in the pregnant uterus. *Journal of Biological Chemistry* 1982, **257**(12):6886-6897.

14

15

16

Scaravaggi I, Borel N, Romer R, Imboden I, Ulbrich SE, Zeng S, Bollwein H, Bauersachs S: Cell type-specific endometrial transcriptome changes during initial recognition of pregnancy in the mare. *Reproduction, Fertility and Development* 2018.

17

18

19

20

Hood BL, Liu B, Alkhas A, Shoji Y, Challa R, Wang G, Ferguson S, Oliver J, Mitchell D, Bateman NW *et al*: Proteomics of the Human Endometrial Glandular Epithelium and Stroma from the Proliferative and Secretory Phases of the Menstrual Cycle1. *Biology of Reproduction* 2015, **92**(4):106, 101-108-106, 101-108.

21

22

23

Brooks K, Burns GW, Moraes JG, Spencer TE: Analysis of the Uterine Epithelial and Conceptus Transcriptome and Luminal Fluid Proteome During the Peri-Implantation Period of Pregnancy in Sheep. *Biol Reprod* 2016, **95**(4):88.

24

25

26

Marchi T, Braakman RBH, Stingl C, Duijn MM, Smid M, Foekens JA, Luider TM, Martens JWM, Umar A: The advantage of laser-capture microdissection over whole tissue analysis in proteomic profiling studies. *PROTEOMICS* 2016, **16**(10):1474-1485.

27

28

29

30

Chankeaw W, Guo Y, Båge R, Svensson A, Andersson G, Humblot P: Elevated non-esterified fatty acids impair survival and promote lipid accumulation and pro-inflammatory cytokine production in bovine endometrial epithelial cells. *Reproduction, Fertility and Development* 2018.

31

32

33

34

Ohtsu A, Tanaka H, Seno K, Iwata H, Kuwayama T, Shirasuna K: Palmitic acid stimulates interleukin-8 via the TLR4/NF-κB/ROS pathway and induces mitochondrial dysfunction in bovine oviduct epithelial cells. *American Journal of Reproductive Immunology* 2017, **77**(6):e12642.

35

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Wagner GP, Kin K, Lynch VJ: Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. *Theory Biosci* 2012, **131**(4):281-285.

37

38

Adewuyi AA, Gruys E, van Eerdenburg FJ: Non esterified fatty acids (NEFA) in dairy cattle. A review. *Vet Q* 2005, **27**(3):117-126.

39

40

41

Ntallaris T, Humblot P, Bage R, Sjunnesson Y, Dupont J, Berglund B: Effect of energy balance profiles on metabolic and reproductive response in Holstein and Swedish Red cows. *Theriogenology* 2017, **90**:276-283.

42

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44

Butler ST, Marr AL, Pelton SH, Radcliff RP, Lucy MC, Butler WR: Insulin restores GH responsiveness during lactation-induced negative energy balance in dairy cattle: effects on expression of IGF-I and GH receptor 1A. *J Endocrinol* 2003, **176**(2):205-217.

45

46

47

Jorritsma R, Cesar ML, Hermans JT, Kruitwagen CL, Vos PL, Kruip TA: Effects of non-esterified fatty acids on bovine granulosa cells and developmental potential of oocytes in vitro. *Anim Reprod Sci* 2004, **81**(3-4):225-235.

48

49

50

Van Hoeck V, Sturmey RG, Bermejo-Alvarez P, Rizos D, Gutierrez-Adan A, Leese HJ, Bols PE, Leroy JL: Elevated non-esterified fatty acid concentrations during bovine oocyte maturation compromise early embryo physiology. *PLoS One* 2011, **6**(8):e23183.
1

20.

Niklaus AL, Pollard JW: Mining the Mouse Transcriptome of Receptive Endometrium Reveals Distinct Molecular Signatures for the Luminal and Glandular Epithelium.

2

3

*Endocrinology* 2006, **147**(7):3375-3390.

21.

Gray CA, Taylor KM, Ramsey WS, Hill JR, Bazer FW, Bartol FF, Spencer TE: Endometrial glands are required for preimplantation conceptus elongation and survival. *Biol Reprod* 2001, **64**(6):1608-1613.

5

6

22.

Demir R, Kayisli U, Celik-Ozenci C, Korgun E, Demir-Weusten A, Arici A: Structural differentiation of human uterine luminal and glandular epithelium during early pregnancy: an ultrastructural and immunohistochemical study. *Placenta* 2002, **23**(8-9):672-684.

8

9

23.

Fazleabas A, Bazer F, Roberts RM: Purification and properties of a progesterone-induced plasmin/trypsin inhibitor from uterine secretions of pigs and its immunocytochemical localization in the pregnant uterus. *Journal of Biological Chemistry* 1982, **257**(12):6886-6897.

10

11

12

13

24.

Scaravaggi I, Borel N, Romer R, Imboden I, Ulbrich SE, Zeng S, Bollwein H, Bauersachs S: Cell type-specific endometrial transcriptome changes during initial recognition of pregnancy in the mare. *Reproduction, Fertility and Development* 2018.

15

16

25.

Hood BL, Liu B, Alkhas A, Shoji Y, Challa R, Wang G, Ferguson S, Oliver J, Mitchell D, Bateman NW *et al*: Proteomics of the Human Endometrial Glandular Epithelium and Stroma from the Proliferative and Secretory Phases of the Menstrual Cycle1. *Biology of Reproduction* 2015, **92**(4):106, 101-108-106, 101-108.

18

19

20

26.

Brooks K, Burns GW, Moraes JG, Spencer TE: Analysis of the Uterine Epithelial and Conceptus Transcriptome and Luminal Fluid Proteome During the Peri-Implantation Period of Pregnancy in Sheep. *Biol Reprod* 2016, **95**(4):88.

22

23

27.

Marchi T, Braakman RBH, Stingl C, Duijn MM, Smid M, Foekens JA, Luider TM, Martens JWM, Umar A: The advantage of laser-capture microdissection over whole tissue analysis in proteomic profiling studies. *PROTEOMICS* 2016, **16**(10):1474-1485.

24

25

26

28.

Chankeaw W, Guo Y, Båge R, Svensson A, Andersson G, Humblot P: Elevated non-esterified fatty acids impair survival and promote lipid accumulation and pro-inflammatory cytokine production in bovine endometrial epithelial cells. *Reproduction, Fertility and Development* 2018.

28

29

30

29.

Ohtsu A, Tanaka H, Seno K, Iwata H, Kuwayama T, Shirasuna K: Palmitic acid stimulates interleukin-8 via the TLR4/NF-κB/ROS pathway and induces mitochondrial dysfunction in bovine oviduct epithelial cells. *American Journal of Reproductive Immunology* 2017, **77**(6):e12642.

31

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35

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Adewuyi AA, Gruys E, van Eerdenburg FJ: Non esterified fatty acids (NEFA) in dairy cattle. A review. *Vet Q* 2005, **27**(3):117-126.

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Ntallaris T, Humblot P, Bage R, Sjunnesson Y, Dupont J, Berglund B: Effect of energy balance profiles on metabolic and reproductive response in Holstein and Swedish Red cows. *Theriogenology* 2017, **90**:276-283.

39

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Butler ST, Marr AL, Pelton SH, Radcliff RP, Lucy MC, Butler WR: Insulin restores GH responsiveness during lactation-induced negative energy balance in dairy cattle: effects on expression of IGF-I and GH receptor 1A. *J Endocrinol* 2003, **176**(2):205-217.

42

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Jorritsma R, Cesar ML, Hermans JT, Kruitwagen CL, Vos PL, Kruip TA: Effects of non-esterified fatty acids on bovine granulosa cells and developmental potential of oocytes in vitro. *Anim Reprod Sci* 2004, **81**(3-4):225-235.

45

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Van Hoeck V, Sturmey RG, Bermejo-Alvarez P, Rizos D, Gutierrez-Adan A, Leese HJ, Bols PE, Leroy JL: Elevated non-esterified fatty acid concentrations during bovine oocyte maturation compromise early embryo physiology. *PLoS One* 2011, **6**(8):e23183.

48

49

50
<table>
  <tr>
    <td>1</td>
    <td>36.</td>
    <td>Fenwick MA, Llewellyn S, Fitzpatrick R, Kenny DA, Murphy JJ, Patton J, Wathes DC: Negative energy balance in dairy cows is associated with specific changes in IGF-binding protein expression in the oviduct. Reproduction 2008, 135(1):63-75.</td>
  </tr>
  <tr>
    <td>2</td>
    <td>37.</td>
    <td>Wathes DC, Cheng Z, Chowdhury W, Fenwick MA, Fitzpatrick R, Morris DG, Patton J, Murphy JJ: Negative energy balance alters global gene expression and immune responses in the uterus of postpartum dairy cows. Physiol Genomics 2009, 39(1):1-13.</td>
  </tr>
  <tr>
    <td>3</td>
    <td>38.</td>
    <td>Wathes DC, Cheng Z, Fenwick MA, Fitzpatrick R, Patton J: Influence of energy balance on the somatotrophic axis and matrix metalloproteinase expression in the endometrium of the postpartum dairy cow. Reproduction 2011, 141(2):269-281.</td>
  </tr>
  <tr>
    <td>4</td>
    <td>39.</td>
    <td>Yanaihara A, Otsuka Y, Iwasaki S, Koide K, Aida T, Okai T: Comparison in gene expression of secretory human endometrium using laser microdissection. Reprod Biol Endocrinol 2004, 2:66.</td>
  </tr>
  <tr>
    <td>5</td>
    <td>40.</td>
    <td>Scaravaggi I, Borel N, Romer R, Imboden I, Ulbrich SE, Zeng S, Bollwein H, Bauersachs S: Cell type-specific endometrial transcriptome changes during initial recognition of pregnancy in the mare. Reprod Fertil Dev 2019, 31(3):496-508.</td>
  </tr>
  <tr>
    <td>6</td>
    <td>41.</td>
    <td>Cerri RL, Thompson IM, Kim IH, Ealy AD, Hansen PJ, Staples CR, Li JL, Santos JE, Thatcher WW: Effects of lactation and pregnancy on gene expression of endometrium of Holstein cows at day 17 of the estrous cycle or pregnancy. J Dairy Sci 2012, 95(10):5657-5675.</td>
  </tr>
  <tr>
    <td>7</td>
    <td>42.</td>
    <td>Uhlen M, Fagerberg L, Hallstrom BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson A, Kampf C, Sjostedt E, Asplund A et al: Proteomics. Tissue-based map of the human proteome. Science 2015, 347(6220):1260419.</td>
  </tr>
  <tr>
    <td>8</td>
    <td>43.</td>
    <td>Zeng S, Bick J, Ulbrich SE, Bauersachs S: Cell type-specific analysis of transcriptome changes in the porcine endometrium on Day 12 of pregnancy. BMC Genomics 2018, 19(1):459.</td>
  </tr>
  <tr>
    <td>9</td>
    <td>44.</td>
    <td>Xia HF, Ma JJ, Sun J, Yang Y, Peng JP: Retinoic acid metabolizing enzyme CYP26A1 is implicated in rat embryo implantation. Hum Reprod 2010, 25(12):2985-2998.</td>
  </tr>
  <tr>
    <td>10</td>
    <td>45.</td>
    <td>Dobrzyn K, Szeszko K, Kiezun M, Kisielewska K, Rytelewska E, Gudelska M, Wyrebek J, Bors K, Kaminski T, Smolinska N: In vitro effect of orexin A on the transcriptomic profile of the endometrium during early pregnancy in pigs. Anim Reprod Sci 2019, 200:31-42.</td>
  </tr>
  <tr>
    <td>11</td>
    <td>46.</td>
    <td>Smolinska N, Kiezun M, Dobrzyn K, Szeszko K, Maleszka A, Kaminski T: Expression of the orexin system in the porcine uterus, conceptus and trophoblast during early pregnancy. Animal 2015, 9(11):1820-1831.</td>
  </tr>
  <tr>
    <td>12</td>
    <td>47.</td>
    <td>Scolari SC, Pugliesi G, Strefezzi RF, Andrade SC, Coutinho LL, Binelli M: Dynamic remodeling of endometrial extracellular matrix regulates embryo receptivity in cattle. Reproduction 2016.</td>
  </tr>
  <tr>
    <td>13</td>
    <td>48.</td>
    <td>Hein S, Yamamoto SY, Okazaki K, Jourdan-LeSaux C, Csiszar K, Bryant-Greenwood GD: Lysyl oxidases: expression in the fetal membranes and placenta. Placenta 2001, 22(1):49-57.</td>
  </tr>
  <tr>
    <td>14</td>
    <td>49.</td>
    <td>Liu C, Tong H, Li S, Yan Y: Effect of ECM2 expression on bovine skeletal muscle-derived satellite cell differentiation. Cell Biol Int 2018, 42(5):525-532.</td>
  </tr>
  <tr>
    <td>15</td>
    <td>50.</td>
    <td>Gibbs GM, Roelants K, O'Bryan MK: The CAP superfamily: cysteine-rich secretory proteins, antigen 5, and pathogenesis-related 1 proteins--roles in reproduction, cancer, and immune defense. Endocr Rev 2008, 29(7):865-897.</td>
  </tr>
  <tr>
    <td>16</td>
    <td>51.</td>
    <td>Gurates B, Sebastian S, Yang S, Zhou J, Tamura M, Fang Z, Suzuki T, Sasano H, Bulun SE: WT1 and DAX-1 inhibit aromatase P450 expression in human endometrial and endometriotic stromal cells. J Clin Endocrinol Metab 2002, 87(9):4369-4377.</td>
  </tr>
  <tr>
    <td>17</td>
    <td>52.</td>
    <td>Hayashi K, Spencer TE: WNT pathways in the neonatal ovine uterus: potential specification of endometrial gland morphogenesis by SFRP2. Biol Reprod 2006, 74(4):721-733.</td>
  </tr>
  <tr>
    <td>18</td>
    <td>53.</td>
    <td>Gargett CE, Schwab KE, Zillwood RM, Nguyen HP, Wu D: Isolation and culture of epithelial progenitors and mesenchymal stem cells from human endometrium. Biol Reprod 2009, 80(6):1136-1145.</td>
  </tr>
</table>
<table>
  <tr>
    <td>1</td>
    <td>36.</td>
    <td>Fenwick MA, Llewellyn S, Fitzpatrick R, Kenny DA, Murphy JJ, Patton J, Wathes DC: Negative energy balance in dairy cows is associated with specific changes in IGF-binding protein expression in the oviduct. Reproduction 2008, 135(1):63-75.</td>
  </tr>
  <tr>
    <td>2</td>
    <td>37.</td>
    <td>Wathes DC, Cheng Z, Chowdhury W, Fenwick MA, Fitzpatrick R, Morris DG, Patton J, Murphy JJ: Negative energy balance alters global gene expression and immune responses in the uterus of postpartum dairy cows. Physiol Genomics 2009, 39(1):1-13.</td>
  </tr>
  <tr>
    <td>3</td>
    <td>38.</td>
    <td>Wathes DC, Cheng Z, Fenwick MA, Fitzpatrick R, Patton J: Influence of energy balance on the somatotrophic axis and matrix metalloproteinase expression in the endometrium of the postpartum dairy cow. Reproduction 2011, 141(2):269-281.</td>
  </tr>
  <tr>
    <td>4</td>
    <td>39.</td>
    <td>Yanaihara A, Otsuka Y, Iwasaki S, Koide K, Aida T, Okai T: Comparison in gene expression of secretory human endometrium using laser microdissection. Reprod Biol Endocrinol 2004, 2:66.</td>
  </tr>
  <tr>
    <td>5</td>
    <td>40.</td>
    <td>Scaravaggi I, Borel N, Romer R, Imboden I, Ulbrich SE, Zeng S, Bollwein H, Bauersachs S: Cell type-specific endometrial transcriptome changes during initial recognition of pregnancy in the mare. Reprod Fertil Dev 2019, 31(3):496-508.</td>
  </tr>
  <tr>
    <td>6</td>
    <td>41.</td>
    <td>Cerri RL, Thompson IM, Kim IH, Ealy AD, Hansen PJ, Staples CR, Li JL, Santos JE, Thatcher WW: Effects of lactation and pregnancy on gene expression of endometrium of Holstein cows at day 17 of the estrous cycle or pregnancy. J Dairy Sci 2012, 95(10):5657-5675.</td>
  </tr>
  <tr>
    <td>7</td>
    <td>42.</td>
    <td>Uhlen M, Fagerberg L, Hallstrom BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson A, Kampf C, Sjostedt E, Asplund A et al: Proteomics. Tissue-based map of the human proteome. Science 2015, 347(6220):1260419.</td>
  </tr>
  <tr>
    <td>8</td>
    <td>43.</td>
    <td>Zeng S, Bick J, Ulbrich SE, Bauersachs S: Cell type-specific analysis of transcriptome changes in the porcine endometrium on Day 12 of pregnancy. BMC Genomics 2018, 19(1):459.</td>
  </tr>
  <tr>
    <td>9</td>
    <td>44.</td>
    <td>Xia HF, Ma JJ, Sun J, Yang Y, Peng JP: Retinoic acid metabolizing enzyme CYP26A1 is implicated in rat embryo implantation. Hum Reprod 2010, 25(12):2985-2998.</td>
  </tr>
  <tr>
    <td>10</td>
    <td>45.</td>
    <td>Dobrzyn K, Szeszko K, Kiezun M, Kisielewska K, Rytelewska E, Gudelska M, Wyrebek J, Bors K, Kaminski T, Smolinska N: In vitro effect of orexin A on the transcriptomic profile of the endometrium during early pregnancy in pigs. Anim Reprod Sci 2019, 200:31-42.</td>
  </tr>
  <tr>
    <td>11</td>
    <td>46.</td>
    <td>Smolinska N, Kiezun M, Dobrzyn K, Szeszko K, Maleszka A, Kaminski T: Expression of the orexin system in the porcine uterus, conceptus and trophoblast during early pregnancy. Animal 2015, 9(11):1820-1831.</td>
  </tr>
  <tr>
    <td>12</td>
    <td>47.</td>
    <td>Scolari SC, Pugliesi G, Strefezzi RF, Andrade SC, Coutinho LL, Binelli M: Dynamic remodeling of endometrial extracellular matrix regulates embryo receptivity in cattle. Reproduction 2016.</td>
  </tr>
  <tr>
    <td>13</td>
    <td>48.</td>
    <td>Hein S, Yamamoto SY, Okazaki K, Jourdan-LeSaux C, Csiszar K, Bryant-Greenwood GD: Lysyl oxidases: expression in the fetal membranes and placenta. Placenta 2001, 22(1):49-57.</td>
  </tr>
  <tr>
    <td>14</td>
    <td>49.</td>
    <td>Liu C, Tong H, Li S, Yan Y: Effect of ECM2 expression on bovine skeletal muscle-derived satellite cell differentiation. Cell Biol Int 2018, 42(5):525-532.</td>
  </tr>
  <tr>
    <td>15</td>
    <td>50.</td>
    <td>Gibbs GM, Roelants K, O'Bryan MK: The CAP superfamily: cysteine-rich secretory proteins, antigen 5, and pathogenesis-related 1 proteins--roles in reproduction, cancer, and immune defense. Endocr Rev 2008, 29(7):865-897.</td>
  </tr>
  <tr>
    <td>16</td>
    <td>51.</td>
    <td>Gurates B, Sebastian S, Yang S, Zhou J, Tamura M, Fang Z, Suzuki T, Sasano H, Bulun SE: WT1 and DAX-1 inhibit aromatase P450 expression in human endometrial and endometriotic stromal cells. J Clin Endocrinol Metab 2002, 87(9):4369-4377.</td>
  </tr>
  <tr>
    <td>17</td>
    <td>52.</td>
    <td>Hayashi K, Spencer TE: WNT pathways in the neonatal ovine uterus: potential specification of endometrial gland morphogenesis by SFRP2. Biol Reprod 2006, 74(4):721-733.</td>
  </tr>
  <tr>
    <td>18</td>
    <td>53.</td>
    <td>Gargett CE, Schwab KE, Zillwood RM, Nguyen HP, Wu D: Isolation and culture of epithelial progenitors and mesenchymal stem cells from human endometrium. Biol Reprod 2009, 80(6):1136-1145.</td>
  </tr>
</table>
1 54. Yu J, Berga SL, Zou W, Sun HY, Johnston-MacAnanny E, Yalcinkaya T, Sidell N, Bagchi IC, Bagchi MK, Taylor RN: **Gap junction blockade induces apoptosis in human endometrial stromal cells.** *Mol Reprod Dev* 2014, **81**(7):666-675.

4 55. Yotova I, Hsu E, Do C, Gaba A, Schzabolcs M, Dekan S, Kenner L, Wenzl R, Tycko B: **Epigenetic Alterations Affecting Transcription Factors and Signaling Pathways in Stromal Cells of Endometriosis.** *PLoS One* 2017, **12**(1):e0170859.

7 56. Rodriguez-Alonso B, Hamdi M, Sanchez JM, Maillo V, Gutierrez-Adan A, Lonergan P, Rizos D: **An approach to study the local embryo effect on gene expression in the bovine oviduct epithelium in vivo.** *Reprod Domest Anim* 2019, **54**(12):1516-1523.

10 57. Lim W, Bae H, Bazer FW, Song G: **Brain-derived neurotrophic factor improves proliferation of endometrial epithelial cells by inhibition of endoplasmic reticulum stress during early pregnancy.** *J Cell Physiol* 2017, **232**(12):3641-3651.

13 58. Wessels JM, Wu L, Leyland NA, Wang H, Foster WG: **The brain-uterus connection: brain derived neurotrophic factor (BDNF) and its receptor (Ntrk2) are conserved in the mammalian uterus.** *PLoS One* 2014, **9**(4):e94036.

16 59. Xu B, Geerts D, Bu Z, Ai J, Jin L, Li Y, Zhang H, Zhu G: **Regulation of endometrial receptivity by the highly expressed HOXA9, HOXA11 and HOXD10 HOX-class homeobox genes.** *Hum Reprod* 2014, **29**(4):781-790.

19 60. Daftary GS, Taylor HS: **Implantation in the human: the role of HOX genes.** *Semin Reprod Med* 2000, **18**(3):311-320.

21 61. Zhang Y, Zhang L, Yu C, Du X, Liu X, Liu J, An X, Wang J, Song Y, Li G *et al*: **Effects of interferon tau on endometrial epithelial cells in caprine in vitro.** *Gene Expr Patterns* 2017, **25-26**:142-148.

24 62. Yan Q, Huang C, Jiang Y, Shan H, Jiang R, Wang J, Liu J, Ding L, Yan G, Sun H: **Calpain7 impairs embryo implantation by downregulating beta3-integrin expression via degradation of HOXA10.** *Cell Death Dis* 2018, **9**(3):291.

27 63. Du F, Yang R, Ma HL, Wang QY, Wei SL: **Expression of transcriptional repressor Slug gene in mouse endometrium and its effect during embryo implantation.** *Appl Biochem Biotechnol* 2009, **157**(2):346-355.

30 64. Mishra B, Koshi K, Kizaki K, Ushizawa K, Takahashi T, Hosoe M, Sato T, Ito A, Hashizume K: **Expression of ADAMTS1 mRNA in bovine endometrium and placenta during gestation.** *Domest Anim Endocrinol* 2013, **45**(1):43-48.

33 65. Genis S, Aris A, Kaur M, Cerri RLA: **Effect of metritis on endometrium tissue transcriptome during puerperium in Holstein lactating cows.** *Theriogenology* 2018, **122**:116-123.

35 66. Bauersachs S, Mitko K, Ulbrich SE, Blum H, Wolf E: **Transcriptome studies of bovine endometrium reveal molecular profiles characteristic for specific stages of estrous cycle and early pregnancy.** *Exp Clin Endocrinol Diabetes* 2008, **116**(7):371-384.

38 67. Moran B, Butler ST, Moore SG, MacHugh DE, Creevey CJ: **Differential gene expression in the endometrium reveals cytoskeletal and immunological genes in lactating dairy cows genetically divergent for fertility traits.** *Reprod Fertil Dev* 2017, **29**(2):274-282.

41 68. Kimmins S, MacLaren LA: **Cyclic modulation of integrin expression in bovine endometrium.** *Biol Reprod* 1999, **61**(5):1267-1274.

43 69. Lessey BA, Castelbaum AJ, Buck CA, Lei Y, Yowell CW, Sun J: **Further characterization of endometrial integrins during the menstrual cycle and in pregnancy.** *Fertility and sterility* 1994, **62**(3):497-506.

46 70. Chen G, Xin A, Liu Y, Shi C, Chen J, Tang X, Chen Y, Yu M, Peng X, Li L: **Integrins β1 and β3 are biomarkers of uterine condition for embryo transfer.** *Journal of translational medicine* 2016, **14**(1):303.

49 71. Spencer TE, Bazer FW: **Uterine and placental factors regulating conceptus growth in domestic animals.** *J Anim Sci* 2004, **82 E-Suppl**:E4-13.
1 54. Yu J, Berga SL, Zou W, Sun HY, Johnston-MacAnanny E, Yalcinkaya T, Sidell N, Bagchi IC, Bagchi MK, Taylor RN: Gap junction blockade induces apoptosis in human endometrial stromal cells. *Mol Reprod Dev* 2014, **81**(7):666-675.

4 55. Yotova I, Hsu E, Do C, Gaba A, Schzabolcs M, Dekan S, Kenner L, Wenzl R, Tycko B: Epigenetic Alterations Affecting Transcription Factors and Signaling Pathways in Stromal Cells of Endometriosis. *PLoS One* 2017, **12**(1):e0170859.

7 56. Rodriguez-Alonso B, Hamdi M, Sanchez JM, Maillo V, Gutierrez-Adan A, Lonergan P, Rizos D: An approach to study the local embryo effect on gene expression in the bovine oviduct epithelium in vivo. *Reprod Domest Anim* 2019, **54**(12):1516-1523.

10 57. Lim W, Bae H, Bazer FW, Song G: Brain-derived neurotrophic factor improves proliferation of endometrial epithelial cells by inhibition of endoplasmic reticulum stress during early pregnancy. *J Cell Physiol* 2017, **232**(12):3641-3651.

13 58. Wessels JM, Wu L, Leyland NA, Wang H, Foster WG: The brain-uterus connection: brain derived neurotrophic factor (BDNF) and its receptor (Ntrk2) are conserved in the mammalian uterus. *PLoS One* 2014, **9**(4):e94036.

16 59. Xu B, Geerts D, Bu Z, Ai J, Jin L, Li Y, Zhang H, Zhu G: Regulation of endometrial receptivity by the highly expressed HOXA9, HOXA11 and HOXD10 HOX-class homeobox genes. *Hum Reprod* 2014, **29**(4):781-790.

19 60. Daftary GS, Taylor HS: Implantation in the human: the role of HOX genes. *Semin Reprod Med* 2000, **18**(3):311-320.

21 61. Zhang Y, Zhang L, Yu C, Du X, Liu X, Liu J, An X, Wang J, Song Y, Li G et al: Effects of interferon tau on endometrial epithelial cells in caprine in vitro. *Gene Expr Patterns* 2017, **25-26**:142-148.

24 62. Yan Q, Huang C, Jiang Y, Shan H, Jiang R, Wang J, Liu J, Ding L, Yan G, Sun H: Calpain7 impairs embryo implantation by downregulating beta3-integrin expression via degradation of HOXA10. *Cell Death Dis* 2018, **9**(3):291.

27 63. Du F, Yang R, Ma HL, Wang QY, Wei SL: Expression of transcriptional repressor Slug gene in mouse endometrium and its effect during embryo implantation. *Appl Biochem Biotechnol* 2009, **157**(2):346-355.

30 64. Mishra B, Koshi K, Kizaki K, Ushizawa K, Takahashi T, Hosoe M, Sato T, Ito A, Hashizume K: Expression of ADAMTS1 mRNA in bovine endometrium and placenta during gestation. *Domest Anim Endocrinol* 2013, **45**(1):43-48.

33 65. Genis S, Aris A, Kaur M, Cerri RLA: Effect of metritis on endometrium tissue transcriptome during puerperium in Holstein lactating cows. *Theriogenology* 2018, **122**:116-123.

35 66. Bauersachs S, Mitko K, Ulbrich SE, Blum H, Wolf E: Transcriptome studies of bovine endometrium reveal molecular profiles characteristic for specific stages of estrous cycle and early pregnancy. *Exp Clin Endocrinol Diabetes* 2008, **116**(7):371-384.

38 67. Moran B, Butler ST, Moore SG, MacHugh DE, Creevey CJ: Differential gene expression in the endometrium reveals cytoskeletal and immunological genes in lactating dairy cows genetically divergent for fertility traits. *Reprod Fertil Dev* 2017, **29**(2):274-282.

41 68. Kimmins S, MacLaren LA: Cyclic modulation of integrin expression in bovine endometrium. *Biol Reprod* 1999, **61**(5):1267-1274.

43 69. Lessey BA, Castelbaum AJ, Buck CA, Lei Y, Yowell CW, Sun J: Further characterization of endometrial integrins during the menstrual cycle and in pregnancy. *Fertility and sterility* 1994, **62**(3):497-506.

46 70. Chen G, Xin A, Liu Y, Shi C, Chen J, Tang X, Chen Y, Yu M, Peng X, Li L: Integrins β1 and β3 are biomarkers of uterine condition for embryo transfer. *Journal of translational medicine* 2016, **14**(1):303.

49 71. Spencer TE, Bazer FW: Uterine and placental factors regulating conceptus growth in domestic animals. *J Anim Sci* 2004, **82 E-Suppl**:E4-13.