Persistent effects on bovine granulosa cell transcriptome after resolution of uterine disease

in Reproduction
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  • 1 Department of Animal Sciences, University of Florida, Gainesville, Florida, USA
  • 2 Institute of Life Science, Swansea University Medical School, Swansea, UK

Correspondence should be addressed to J J Bromfield; Email: jbromfield@ufl.edu

Metritis is associated with reduced fertility in dairy cows, but the mechanisms are unclear because the disease resolves several weeks before insemination. One hypothesis is that metritis causes persistent changes in granulosa cells during follicle development, which might be evident in the transcriptome of granulosa cells from dominant follicles weeks after parturition. To test this hypothesis, we collected the follicular fluid and granulosa cells from dominant follicles 63 days post partum from cows previously diagnosed with metritis, at least 6 weeks after resolution of the disease and from cows not diagnosed with metritis (control cows). Bacterial lipopolysaccharide was detected in follicular fluid, and concentrations were associated with follicular fluid IL-8 and glucose concentrations. Transcriptome analysis using RNAseq revealed 177 differentially expressed genes in granulosa cells collected from cows that had metritis compared with control cows. The most upregulated genes were ITLN1, NCF2, CLRN3, FSIP2 and ANKRD17, and the most downregulated genes were ACSM1, NR4A2, GHITM, CBARP and NR1I3. Pathway analysis indicated that the differentially expressed genes were involved with immune function, cell–cell communication, cell cycle and cellular metabolism. Predicted upstream regulators of the differentially expressed genes included NFκB, IL-21 and lipopolysaccharide, which are associated with infection and immunity. Our data provide evidence for a persistent effect of metritis on the transcriptome of granulosa cells in ovarian follicles after the resolution of disease.

Abstract

Metritis is associated with reduced fertility in dairy cows, but the mechanisms are unclear because the disease resolves several weeks before insemination. One hypothesis is that metritis causes persistent changes in granulosa cells during follicle development, which might be evident in the transcriptome of granulosa cells from dominant follicles weeks after parturition. To test this hypothesis, we collected the follicular fluid and granulosa cells from dominant follicles 63 days post partum from cows previously diagnosed with metritis, at least 6 weeks after resolution of the disease and from cows not diagnosed with metritis (control cows). Bacterial lipopolysaccharide was detected in follicular fluid, and concentrations were associated with follicular fluid IL-8 and glucose concentrations. Transcriptome analysis using RNAseq revealed 177 differentially expressed genes in granulosa cells collected from cows that had metritis compared with control cows. The most upregulated genes were ITLN1, NCF2, CLRN3, FSIP2 and ANKRD17, and the most downregulated genes were ACSM1, NR4A2, GHITM, CBARP and NR1I3. Pathway analysis indicated that the differentially expressed genes were involved with immune function, cell–cell communication, cell cycle and cellular metabolism. Predicted upstream regulators of the differentially expressed genes included NFκB, IL-21 and lipopolysaccharide, which are associated with infection and immunity. Our data provide evidence for a persistent effect of metritis on the transcriptome of granulosa cells in ovarian follicles after the resolution of disease.

Introduction

Bacterial infections cause metritis in 10–20% of dairy cows within 21 days post partum (Huzzey et al. 2007, Sheldon et al. 2009, LeBlanc et al. 2011, Bromfield et al. 2018). Metritis is important because the disease is associated with reduced conception rates and infertility (Ribeiro et al. 2016). However, the mechanisms linking metritis and fertility are unclear because the disease resolves several weeks before animals are inseminated. As each ovulatory dominant follicle takes 120 days to develop from a primordial follicle (Britt et al. 2018), one possibility is that metritis causes a persistent change in the granulosa cell transcriptome that impairs ovulatory follicles several weeks after parturition.

Postpartum uterine disease is associated with infection of the uterus with a wide range of gram-negative and gram-positive pathogenic bacteria (Sheldon et al. 2002, Bicalho et al. 2012). The most prevalent pathogenic bacteria associated with uterine disease are Escherichia coli, Trueperella pyogenes, Prevotella sp. and Fusobacterium sp. (Sheldon et al. 2002, 2010, Williams et al. 2005, Amos et al. 2014). Sequencing techniques have further advanced our understanding of the uterine microbiome and bacterial diversity and associations with uterine health and disease (Santos & Bicalho 2012, Jeon et al. 2016). These pathogens express multiple pathogen-associated molecular patterns, including lipopolysaccharide (LPS) and lipopeptides, which are sensed by immune cells and endometrial cells (Moresco et al. 2011, Turner et al. 2014). Activation of innate immunity leads to nuclear translocation of transcription factors, including NFκB, stimulating secretion of cytokines such as interleukin (IL)-1 and IL-6 and chemokines such as IL-8. In addition, the pathogens produce pore-forming toxins that cause cell damage, leading to further pathology in the endometrium (Amos et al. 2014). Although the bacteria are confined to the genital tract, infected animals have slower growth of the dominant follicle and altered steroidogenesis during the active disease and are less likely to ovulate (Sheldon et al. 2002, Herath et al. 2007). Additionally, LPS is found in the follicular fluid of dairy cows that previously had uterine infection (Herath et al. 2007).

Granulosa cells determine follicle growth, steroidogenesis, oocyte development and ovulation (Canipari 2000, Gioacchini et al. 2018), coordinating these events by modulating gene expression throughout development and growth of the follicle (Hatzirodos et al. 2014). In vitro, granulosa cells isolated from growing and dominant follicles respond to LPS by producing inflammatory mediators and reducing estradiol synthesis by downregulating CYP19A1 expression (Bromfield & Sheldon 2011, Price et al. 2013). Additionally, LPS negatively impacts the ability of the oocyte to complete meiosis to metaphase II and subsequently form healthy embryos (Soto et al. 2003, Bromfield & Sheldon 2011, Sheldon et al. 2014). The phenotypic effects of uterine infection on ovarian function and the capacity of granulosa cells to respond to pathogen-associated molecules imply that the ovary could contribute to reduced fertility after the resolution of metritis. However, it is unclear if the ovary retains a molecular imprint of uterine infection to modulate ovarian function after the clearance of disease.

One hypothesis is that metritis causes persistent changes in granulosa cells during follicle development, which might be evident in the transcriptome of granulosa cells from dominant follicles several weeks after parturition. Here, we used RNA sequencing (RNAseq) of granulosa cells collected from dominant follicles around the expected time of insemination for dairy cows and more than 6 weeks after the resolution of metritis. Compared with control, granulosa cells isolated from cows with metritis had 177 differentially regulated genes, which were associated with 39 canonical pathways, including multiple pathways linked with immune responses. These data provide evidence that postpartum metritis has a persistent effect on the granulosa cell transcriptome, which could negatively impact oocyte quality, ovarian function and fertility.

Materials and methods

Ethical statement

Animal procedures were approved by the University of Florida Institutional Animal Care and Use Committee (protocol 201508884).

Study design and animal procedures

The design was a prospective cohort study conducted from June 2016 to February 2017 at the University of Florida Dairy Unit using a group of 45 lactating Holstein Bos taurus taurus dairy cows fed a total mixed ration and housed in barns with fans and sprinklers for cow comfort.

Milk yield (AfiFlo milk meters, S.A.E. Afikim, Israel) and concentrations of fat, true protein and lactose (AfiLab online real-time milk analyzer, S.A.E. Afikim) were recorded at each milking, and cows were weighed on a walk through scale (AfiWeigh, S.A.E. Afikim) immediately after each milking as they left the milking parlor. Data were collected for the first 280 days postpartum or until the cow died or was culled, whichever occurred first.

Cows were evaluated every 48 h for the first 21 days after parturition for signs of metritis as defined by a fetid, red-brown watery vaginal discharge and rectal temperature >39.5°C (Sheldon et al. 2006). Cows with metritis received antimicrobial treatment with 2.2 mg/kg body weight ceftiofur hydrochloride (Excenel, Zoetis, Parsippany, NJ, USA) for 5 consecutive days. The veterinarians and farm staff recorded other diseases diagnosed within 21 days post partum, including mastitis, ketosis, displaced abomasum and lameness. Ketosis was diagnosed by evaluating elevated urine ketones, mastitis was defined as any clinical infection of the udder, displaced abomasum was diagnosed by auditory examination of the abdomen and lameness was defined as cows with any number of foot/hoof conditions impacting ambulatory movement. Cows were examined for clinical endometritis, as determined by a purulent vaginal discharge after day 21 post partum, and for subclinical endometritis, as determined by >5% neutrophils in endometrial cytology samples collected on the day of follicle aspiration, at 63 days postpartum. Cows with post partum conditions beyond 21 days after parturition were excluded from analysis.

Cows started an ovulation synchronization protocol with the double Ovsynch program (Souza et al. 2008) using 100 mg GnRH (gonadorelin diacetate tetrahydrate; Ovacyst, Bayer Animal Health, Whippany, NJ, USA) i.m. 53 ± 3 days post partum followed by 25 mg prostaglandin (PG) F (dinoprost tromethamine; Prostamate, Bayer) i.m. 60 ± 3 days post partum. The contents of the pre-ovulatory follicle were aspirated by transvaginal ultrasonography on d 63 ± 3 postpartum (see below for details), immediately before cows received a second GnRH injection 72 h after PGF. A second Ovsynch protocol was then started 7 days later, and the final GnRH injection was administered approximately 16 h before timed AI performed at 80 days postpartum. Pregnancy was diagnosed by transrectal ultrasonography 32 days after timed AI.

Blood was collected from the coccygeal vessels into evacuated tubes containing sodium heparin (Vacutainer, Becton Dickson) on days 7, 21, 35 and 50 post partum. Blood samples were placed on ice prior to centrifugation at 2000 g for 10 min to collect plasma, which was stored at −20°C.

Granulosa cell isolation by ultrasound-guided transvaginal follicle aspiration

The dominant follicle was sampled 63 ± 3 days post partum following a caudal epidural injection of 60 mg of lidocaine hydrochloride 2% (Aspen Veterinary Resources, Greely, CO, USA). The perineum and vulva were cleaned and disinfected with povidone iodine followed by 70% ethanol. Vagina lavage with 100 mL of 0.2% chlorohexidine was followed by two washes with 100 mL sterile 0.9% saline. An oocyte pick-up instrument including a 7.5 MHz convex ultrasound probe (Choice Medical, South Pasadena, FL, USA) was covered in a sanitary sleeve (IMV Technologies, Normandy, France) and introduced into the vagina with sterile lubricant. The ovary was visualized by ultrasound (Aloka SSD-500, Hitachi Healthcare Americas, Twinsburg, OH, USA), and the internal diameter of the dominant follicle was measured based on the average of two measurements at 90° angle of the diameters from the larger cross-sectional area of the follicle. An 18 gauge needle attached to a tygon tube connected to a vacuum pump was introduced into the oocyte pick-up instrument and the dominant follicle aspirated into collection medium (Medium 199 with Earle’s Salts, 0.5% BSA, 20 mM HEPES, 2 mM sodium pyruvate, 10 IU/mL heparin 100 U/mL penicillin and 100 μg/mL streptomycin; all Fisher Scientific). The needle was maintained inside the follicle and a total of 10 mL of collection medium were infused through the needle to flush the follicle in 2 mL increments. The volume of aspirate was recorded prior to centrifugation at 300 g for 10 min to separate the follicular fluid from granulosa cells, which were washed twice in sterile PBS. The follicular fluid and granulosa cells were stored at −80°C.

Quantification of LPS, interleukin-8, estradiol, progesterone, cholesterol and glucose

Follicular fluid samples were thawed, vortexed for 3 min and diluted 1:10 in PBS (Gibco 10010-015, pH 7.35). To exclude potential suppression of the assay by endogenous factors, samples were heat-treated at 75°C for 30 min, as described previously (Herath et al. 2007). The concentrations of LPS were determined using a Pierce Chromogenic Endotoxin Quant Kit (Thermo Scientific) according to the manufacturer’s instructions, with minor modifications. Briefly, 50 μL/well standards (from 0.1 to 5 EU/mL LPS), a medium blank and samples were added to a 96-well plate (Nunclon; Thermo Fisher Scientific) on a heating block at 37°C. Then, 50 μL/well ameobcyte lysate reagent was added and the plate incubated for 14 min, prior to addition of 100 μL/well chromogenic substrate reagent for 6 min. The reaction was stopped using 50 μL/well 25% acetic acid, and the optical density at 405 nm was measured using a microplate reader (POLARstar Omega; BMG Labtech, Offenburg, Germany). Concentrations of LPS were determined using linear regression calculated by the microplate reader software. Where samples exceeded the highest standard, samples were further diluted in PBS and assayed until OD405 were within the standard range. The follicular fluid collection medium did not interfere with the assay, and no LPS was detected in three control samples of follicular fluid, obtained from ovaries collected from 2-year-old beef heifers with no gross evidence of genital disease or microbial infections, after they were slaughtered and processed as part of the normal work of an abattoir. These control samples were also spiked with a range of concentrations of LPS from 1 to 50 EU/mL, and processed by dilution and heating exactly as for the experimental samples. The control follicular fluid did not contain detectable LPS, and the recovery of spiked LPS was >90%. The intra- and inter-assay CV was 4.0 and 7.2% respectively and the limit of detection was 0.1 EU/mL.

Follicular fluid IL-8 was measured by bovine specific ELISA as previously described (Cronin et al. 2015), using monoclonal mouse anti-ovine IL-8 capture antibody (MCA1660; Bio-Rad), recombinant bovine IL-8 protein (RP0023B; Kingfisher Biotech, Saint Paul, USA), polyclonal rabbit anti-sheep IL-8 detection antibody (AHP425; Bio-Rad) and HRP-conjugated goat anti-rabbit (P0448; Dako). The intra- and inter-assay CVs were 3.2 and 5.4% respectively, and the limit of detection was 62.5 pg/mL.

Follicular fluid glucose was analyzed using a colorimetric method (Randox Daytona Plus, Randox Laboratories Ltd., Crumlin, UK). Glucose concentrations were determined after enzymatic oxidation in the presence of glucose oxidase; the intensity of the final color is directly proportional to the glucose concentration, measured at OD505. The reportable range for glucose on the RX Daytona Plus is 0.2 to 46.5 mM/L.

Follicular fluid total cholesterol concentrations were analyzed with the cholesterol oxidase-enzymatic endpoint method (Randox Daytona Plus). The concentration of cholesterol is determined after enzymatic hydrolysis and oxidation. The reportable range for cholesterol on the RX Daytona Plus is 0.65 to 16 mM/L.

Follicular fluid estradiol and plasma progesterone were quantified using a commercial ELISA according to the manufacturer’s instructions (DRG International, Springfield, NJ, USA). The estradiol and progesterone assays were validated using spike-in/recovery performance based on actual and expected recovery of standards supplied with the kits. Recovery of spike-in estradiol was 88.9–107.4% of expected estradiol, and recovery of spike-in progesterone was 89–101.8% of expected progesterone. The intra-assay CV was 6.5% for estradiol and 7.4% for progesterone.

Isolation, purification and sequencing of granulosa cell RNA

Granulosa cells were thawed and resuspended in RLT buffer for RNA extraction using the RNeasy Micro kit (Qiagen) according to the manufacturer’s instructions. Total RNA concentration was determined using a Qubit 2.0 Fluorometer (Thermo Fisher Scientific) and RNA quality was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies). Total RNA with a 28S to 18S ratio >1 and RNA integrity number (RIN) ≥7 were used for RNAseq library construction.

To produce RNAseq libraries, 200 ng of total RNA was used to isolate mRNA using NEBNext Poly(A) mRNA magnetic isolation module (New England Biolabs, Ipswich, MA, USA) and RNA library construction with NEBNext Ultra RNA Library Prep Kit for Illumina (New England Biolabs) according to the manufacturer’s instructions. Briefly, extracted mRNA was fragmented in NEBNext first-strand synthesis buffer by heating at 94°C, followed by first-strand cDNA synthesis using reverse transcriptase and random primers. Synthesis of double-stranded cDNA was performed using the 2nd strand master mix provided with the kit. The resulting double-stranded cDNA was end-repaired, dA-tailing and ligated with NEBNext adaptors. Finally, libraries were enriched by 12 cycles of amplification and purified by Meg-Bind RxnPure Plus beads (Omega Biotek, Norcross, GA, USA).

Barcoded libraries were sized on a bioanalyzer, quantitated by Qubit and qPCR (Kapa Biosystems Wilmington, MA, USA). Eighteen individual libraries were pooled at equal molar concentrations (20 nM), and total of four lanes of 2 × 100 bp were run on an Illumina HiSeq3000 (Illumina). The RNA library construction and sequencing was performed at the Interdisciplinary Center for Biotechnology Research (ICBR), University of Florida.

Read mapping, gene expression analysis and pathway analysis of differentially expressed genes

Reads acquired from the sequencing platform were cleaned with the Cutadapt program (Martin 2011) to trim off sequencing adaptors, low-quality bases and potential errors introduced during sequencing or library preparation. Reads with a quality Phred-like score <20 and read length <40 bases were excluded from RNAseq analysis.

The transcripts of Bos taurus (80,896 sequences) retrieved from the NCBI RefSeq database were used as reference sequences for RNAseq analysis. The cleaned reads of each sample were mapped individually to the reference sequences using the bowtie2 mapper (v. 2.2.3) with a ‘3 mismatches a read’ allowance (Langmead & Salzberg 2012). The mapping results were processed with the samtools and scripts developed in house at the University of Florida ICBR to remove potential PCR duplicates and choose uniquely mapped reads for gene expression analysis. Gene expression between the control and treatment group was assessed by counting the number of mapped reads for each transcript (Yao & Yu 2011). Significant up and downregulated genes were selected using the P value and fold-change. Adjusted P values were all above the 0.10 cut-off, as such all data presented for differentially expressed genes are non-adjusted P values.

Pathway analysis was performed using Ingenuity Pathway Analysis (Qiagen). Differentially expressed genes with a P value ≤0.05 and log2 FC <−2 or >2 were used for analysis. Represented canonical pathways with a -log P value >1.3 were determined with corresponding z-scores to describe predicted activation status. Represented gene networks were determined by assessing the number of differentially expressed genes in a given gene network. Upstream regulators of specific gene networks and upstream regulators of differentially expressed genes were predicted using IPA algorithms. Predicted upstream regulators of differentially expressed genes were assigned an activation z-score to predict the appropriate upregulation or downregulation of various downstream differentially regulated genes. A z-score ≥2 or ≤−2 was assumed to be a significant prediction of activation or inhibition, respectively. All RNAseq data are reported as log2 FC.

Statistical analysis

Disease incidence, parity and fertility were analyzed using the GLIMMIX procedure of SAS v. 9.4 (SAS Institute) and the model included the fixed effect of group (control vs metritis). Plasma progesterone concentration was used to determine ovarian cyclicity. Each cow was considered as an individual observation and cyclicity was analyzed as a binary variable at the first plasma sample with progesterone concentration ≥1 ng/mL (0 = did not occur; 1 = occurred) and survival analysis was performed using the Gehan–Breslow–Wilcoxon test. Dominant follicle diameter and follicular fluid parameters were analyzed using the GLM procedure of SAS and the model included the fixed effect of disease (metritis). Yields of milk and milk components, concentration of components in milk and body weight were analyzed using the MIXED procedure of SAS and the models included the fixed effects of group (control vs metritis), day of measurement and their interaction. Body weight was used as a covariate to evaluate milk yield. Cow nested within group was considered as a random effect. First-order autoregressive covariance structure AR (1) was used as the covariate structure. Principal component analysis plots and heatmaps were generated using online ClustVis tools (Metsalu & Vilo 2015). Values are reported as LSM ± s.e.m. for all parameters except for the RNAseq gene expression analysis. Differences with P ≤ 0.05 were considered statistically significant.

Results

Health, milk production and follicle parameters in cows with metritis

Of the 45 cows in the study, 17 were diagnosed with metritis within 21 days of parturition, and the remaining 28 cows were considered controls. Lactation number did not differ (P = 0.94) between cows with metritis and control cows (2.2 ± 0.3 vs 2.2 ± 0.2). The cows in the two groups had a similar incidence of dystocia (3/17 vs 1/28, P = 0.11) and lameness (0/17 vs 3/28). However, cows that had metritis had greater (P < 0.01) incidence of ketosis than control cows (70.6% (12/17) vs 21.4% (6/28)). Other diseases including mastitis and displaced abomasum were combined into a single group and were not different between the two groups of cows (25.0% (8/17) vs 47.1% (7/28)). During the first 280 days after parturition, the body weight of cows with metritis was 21.0 ± 6.6 kg lighter (P < 0.05) than that of control cows (Supplementary Table 1, see section on supplementary data given at the end of this article). In addition, cows diagnosed with metritis produced less (P < 0.05) milk (4.6 kg/day), and less (P < 0.01) individual components in the milk including fat (0.10 kg/day), true protein (0.10 kg/day) and lactose (0.20 kg/day) than control cows (Supplementary Table 1).

The median days to the return of ovarian cyclic activity was longer (P = 0.02) for cows that had metritis than control cows (35 vs 50 days, Supplementary Fig. 1). The subsequent pregnancies as a proportion of inseminations was 28.9% in metritis cows, compared with 39.7% in control cows (P = 0.43), although the number of cows examined does not present statistical power to observe differences in pregnancy rates between groups. The diameter of the aspirated dominant follicle did not differ between groups (Fig. 1A). At the time of follicle aspiration the number of cows at diestrus did not differ between groups (76.5% (13/17) vs 85.7% (24/28)). A total of 34 out of the 45 cows underwent successful follicle aspiration (control n = 19; metritis n = 15) and endometrial cytology confirmed that no cows in the study presented subclinical endometritis at the time of follicle aspiration. Follicular fluid aspirate concentrations of estradiol, IL-8, LPS, glucose and cholesterol did not differ between metritis and control cows (Fig. 1B, C, D, E and F). However, there was a positive association between the concentrations of IL-8 and LPS or glucose (P < 0.05), but not cholesterol (Fig. 1G, H and I).

Figure 1
Figure 1

Follicle evaluation 63 days post partum. Follicular fluid was aspirated on day 63 post partum from control cows (gray bar, n = 19) and cows that had metritis during the first 21 days post partum (black bars, n = 15). The diameter of each dominant follicle was measured (A). Follicular fluid was evaluated for estradiol (B), IL-8 (C), LPS (D), glucose (E) and cholesterol concentrations (F). Data are presented as LSM + s.e.m. and analyzed using the generalized linear mixed model. Associations between the follicular fluid concentrations of IL-8 and LPS (G), glucose (H) or cholesterol (I) are described.

Citation: Reproduction 158, 1; 10.1530/REP-19-0037

Differentially expressed genes associated with metritis

RNAseq technology was employed to explore whether metritis had long-term effects on gene expression of granulosa cells. Only clean follicle aspirates with no visible blood contamination were subjected to RNAseq. A total of seven granulosa cell preparations from cows that had resolved metritis by day 21 were compared with granulosa cell preparations from nine control cows. The granulosa cell samples yielded 1.5 ± 0.5 μg RNA with RNA integrity number (RIN) >7.5 (mean 9.0 ± 0.2). Following RNAseq and read processing, a total of 574,407,346 high-quality reads were used for analysis (approximately 26 million reads per sample). An average of 34% high-quality reads was aligned to the reference genome (Supplementary Table 2). A total of 23,308 GeneBank identifiers were identified in at least one sample. The data discussed here have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession number GSE128697 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128697). The highest expressed genes in granulosa cells from both metritis and control cows were INHBA (inhibin beta A, 316,390 base mean reads), SERPINE2 (serpin family E member 2, 270,521 base mean reads), GSTA3 (glutathione S-transferase alpha 3, 213,755 base mean reads), LRP8 (LDL receptor related protein 8, 134,090 base mean reads) and CYP19A1 (cytochrome P450, family 19, subfamily A, polypeptide 1, 116,836 base mean reads).

Unbiased analysis of the RNAseq data for granulosa cells collected from metritis cows yielded 177 genes that had >2-log2 FC, of which 53 genes had >10-FC, compared with control cows. Of the 177 differentially expressed genes, 144 were upregulated and 33 were down regulated genes, as detailed in Supplementary Table 3, and presented in a volcano plot highlighting their relative log2 FC (Fig. 2A). Principal component analysis of the 177 differentially expressed genes clustered the metritis cows and control cows into two distinct clusters (Fig. 2B). A heatmap and hierarchical clustering of differentially expressed genes demonstrates the relatedness of samples is shown in Fig. 3. The five most upregulated genes in granulosa cells from metritis cows were ITLN1, NCF2, CLRN3, FSIP2 and ANKRD17, and the most downregulated genes in granulosa cells from animals with metritis were ACSM1, NR4A2, GHITM, CBARP and NR1I3 (Table 1).

Figure 2
Figure 2

Differentially expressed genes in granulosa cells of cows with metritis. A volcano plot (A) represents each gene detected using RNAseq as an individual dot. Genes that were differentially regulated in granulosa cells of metritis cows are shown in orange (P ≤ 0.05), and non-significantly affected genes are shown in blue. Expression of genes is based on the log2 fold-change (x-axis) plotted against the P value for each gene. (B) Plot for principal component analysis of RNAseq data from granulosa cells of cows that had metritis (blue) or control cows (red).

Citation: Reproduction 158, 1; 10.1530/REP-19-0037

Figure 3
Figure 3

Heatmap of differentially expressed granulosa cell genes in metritis cows. A heatmap and hierarchical clustering of differentially expressed genes in metritis cows (blue) and control cows (red). Each differentially expressed gene is represented by a single row, and each cow is represented by a single column. Both rows and columns are clustered using correlation distance and average linkage.

Citation: Reproduction 158, 1; 10.1530/REP-19-0037

Table 1

Greatest differentially expressed genes in granulosa cells of cows with metritis.

GeneGene nameControl*Metritis*Log2 FCP value
ITLN1Intelectin 10.3193.09.50.002
NCF2Neutrophil cytosolic factor 20.122.77.90.006
CLRN3Clarin 30.412.55.00.038
FSIP2Fibrous sheath-interacting protein 20.38.44.90.0001
ANKRD17Ankyrin repeat domain 170.12.74.80.035
ACSM1Acyl-CoA synthetase medium chain family member 14.70.1-5.10.044
NR4A2Nuclear receptor subfamily 4 group A member 25.80.2-4.70.028
GHITMGrowth hormone-inducible transmembrane protein3.00.1-4.50.018
CBARPCACN subunit beta-associated regulatory protein2.40.1-4.40.040
NR1I3Nuclear receptor subfamily 1 group I member 32.30.1-4.30.038

*Base mean values determined by RNAseq read number.

FC, fold-change.

Selective inspection of the RNAseq data for genes encoding inflammatory cytokines revealed a 3.1 log2 FC increased (P = 0.037) in IL18 expression in granulosa cells from metritis cows and a tendency (P = 0.083) for a 6.7 log2 FC increase in expression of CXCL2 and a 2.7 log2 FC increase (P = 0.099) in expression of IL1A. Among the genes involved in steroid hormone synthesis, metritis was associated with a 2.3 log2 FC increase (P = 0.048) in expression of HSD17B2 and a non-significant reduction in the expression of CYP11A1, CYP19A1 and STAR (−0.5, −0.8 and −1.3 log2 FC, respectively). Analysis of genes involved in cell viability revealed a 2.8 log2 FC increased (P = 0.055) expression of BCL2L12 in granulosa cells from cows with metritis, and a non-significant increase in expression of CCNB2, CCND1 and CASP4 (0.6, 0.6 and 0.5 log2 FC, respectively). In addition, a −2.1 log2 FC reduction (P = 0.053) of LHCGR was observed in cows with metritis. A full list of selected genes is described in Supplementary Table 4.

Annotation and pathway analysis of differentially expressed genes

Significantly affected canonical pathways enriched by genes differentially expressed in granulosa cells of cows with metritis are shown in Fig. 4. Of the 39 significantly affected canonical pathways, 8 pathways had significant positive z-scores suggesting an upregulation of the pathway due to the distinct pattern of differentially regulated genes. These pathways included Th1 pathway, cholecystokinin/gastrin-mediated signaling, neuro-inflammation signaling, thrombin signaling, actin cytoskeleton signaling, phospholipase C signaling, CXCR4 signaling and signaling by Rho family GTPases. Of the significantly affected canonical pathways, 18 canonical pathways are involved in immunity or inflammation.

Figure 4
Figure 4

Canonical pathways derived from differentially expressed genes in granulosa cells of cows with metritis. Depiction of affected canonical pathways identified based on differentially expressed genes in granulosa cells of cows with metritis compared with cells from control cows, with a -log P value >1.3. Solid bars represent canonical pathways with a positive z-score, gray bars represent canonical pathways with no calculable z-score.

Citation: Reproduction 158, 1; 10.1530/REP-19-0037

Further analysis revealed 17 gene networks that were impacted in the granulosa cells collected from cows with metritis (Supplementary Table 5). The highest scored gene networks included (1) developmental disorder, neurological disease, cardiovascular disease; 2) cell-to-cell signaling and interaction, hematological system development and function, immune cell trafficking; (3) cell signaling, molecular transport, nucleic acid metabolism; (4) cell cycle, cellular assembly and organization, DNA replication, recombination and repair; (5) cancer, cellular development, organismal injury and abnormalities and (6) cellular function and maintenance, cellular growth and proliferation, amino acid metabolism (Table 2). The involvement of specific differentially regulated genes in the cell-to-cell signaling and interaction, hematological system development and function, immune cell trafficking is shown in Fig. 5A; cell signaling, molecular transport, nucleic acid metabolism network is shown in Fig. 5B; and developmental disorder, neurological disease, cardiovascular disease is shown in Fig. 5C.

Figure 5
Figure 5

Differentially regulated gene networks in granulosa cells of cows with metritis. Affected gene networks based on differentially regulated genes of granulosa cells from cows with metritis. (A) Cell-to-cell signaling and interaction, hematological system development and function, immune cell trafficking; (B) cell signaling, molecular transport, nucleic acid metabolism and (C) developmental disorder, neurological disease, cardiovascular disease. Symbols in red are upregulated, and symbols in green are downregulated in granulosa cells of metritis cows.

Citation: Reproduction 158, 1; 10.1530/REP-19-0037

Table 2

Gene networks enriched among genes differentially expressed in granulosa cells from cows with metritis.

Gene network*Network scoreGenes differentially expressed
UpregulatedDownregulated
Developmental Disorder, Neurological Disease, Cardiovascular Disease36ALDOB, ARHGEF2, BAZ2A, CAP1, HGFAC, KCNQ3, MYL3, PKN3, SERPINA12, SLC2A4, SOX5, SSH1, THBS4, TRIB3, TRIM40, VSNL1DTNA, NR4A1, PIDD1, SIK1
Cell-to-cell signaling and interaction, hematological system development and function, immune cell trafficking34CALB1, CDKN2B, CLEC4G, GZMA, HLAB, IL18, KLRC1, KLRD1, KLRK1, MST1, NCR1, PLA2G2A, RASGRP3, RGS5, SELENOS, SYNE2, ZNF292ID4, NR4A2
Cell signaling, molecular transport, nucleic acid metabolism32ADCYAP1, CHGA, GABBR1, GCG, MAPKAP1, MRPS6, NCF2, NDNF, NPY2R, PPY, SSTCHAC1, CRYM, KLHL24, LHCGR, MN1, NR5A2, TTR
Cell cycle, cellular assembly and organization, DNA replication, recombination, and repair25A4GNT, ARPP19, CCDC63, CLRN3, FAM131A, FAM207A, FANCE, GPR25, HOXC11, PAN3, RAB5C, SLC27A6, TKTL2, VGLL4, HELQ
Cancer, cellular development, organismal injury and abnormalities23C20orf196, CHP2, CRCT1, EIF5A2, FGF14, PCTP, POC5, PRICKLE1, SIX4, SMG8, SPATC1LACADM, ACSM1, STX17
Cellular function and maintenance, cellular growth and proliferation, amino acid metabolism21ANKRD17, ATOH1, BTN1A1, CD28, CDX1, CNIH4, F3, FGFR3, HSD17B2, MAT1A, PCDH7, VCLPDLIM3

*Enriched gene networks determined by Ingenuity Pathway Analysis using significantly differentially expressed genes only. Network score is derived from a P value and indicates the likelihood of the genes in a network being found together due to random chance. A network score of 2 or greater gives a 99% confidence the network and genes not being generated by random chance alone. Genes are differentially regulated in metritis cows.

Twelve molecules were predicted as significant upstream regulators of differentially expressed genes in metritis animals (Table 3). The predicted upstream regulators of affected gene networks included IL-12 and IL-21, which are involved in stimulation of lymphocyte and cytolysis gene networks, respectively (Fig. 6A and B), and NF-κB and E. coli LPS, which are involved in innate immunity (Fig. 6C and D).

Figure 6
Figure 6

Predicted upstream regulators in granulosa cells of cows with metritis. The Predicted upstream regulators of affected gene networks included IL-12 (A) and IL-21 (B), which are involved in stimulation of lymphocyte and cytolysis gene networks, respectively. NF-κB (C) and E. coli LPS (D) are predicted upstream regulators of differentially expressed genes. Symbols in red are upregulated, and symbols in green are downregulated in granulosa cells of metritis cows. Symbols in orange are predicted upstream regulators of function.

Citation: Reproduction 158, 1; 10.1530/REP-19-0037

Table 3

Predicted upstream regulators of differentially expressed genes.

Upstream regulatorPredicted activation stateZ-scoreDifferentially expressed target molecules
NFκB (complex)Activated2.908CALB1, CDX1, F3, HGFAC, IL18, LHCGR, NCF2, PLA2G2A, SLC2A4, SOX5, TRIB3, VSNL1
TCF7L2Activated2.414CAP1, GCG, ID4, PRICKLE1, TRIM59, UGT8
IL21Activated2.407CD28, GZMA, KLRC1, KLRD1, KLRK1, NCR1
E. coli B5 lipopolysaccharideActivated2.219F3, IL18, MAT1A, NCF2, TTR
ITKActivated2.219KLRC1, KLRD1, KLRK1, NCR1, PRKCB
NEUROG3Activated2.2CHGA, EN1, GCG, PPY, SST
IL12 (complex)Activated2.176CD28, GZMA, IL18, KLRC1, KLRD1, NCR1
RASActivated2ARHGEF2, CDKN2B, CDX1, GCG
FGF1Inhibited−2.236EN1, NDNF, NR4A2, PRICKLE1, TTR
MethylprednisoloneInhibited−2.216ALDOB, AQP8, C9, F3, HSD17B2, PCTP
CycloheximideInhibited−2.213ADCYAP1, GCG, NR4A1, PLA2G2A, SST
HES1Inhibited−2ATOH1, CHGA, GCG, SST

Discussion

Here, we examined whether metritis was associated with changes in the transcriptome of granulosa cells from dominant follicles of dairy cows 6 weeks after the resolution of disease. The granulosa cells from metritis cows had 177 differentially expressed genes compared with control cows, and these were associated with pathways such as immune function, cell-cell communication, cell cycle and cellular metabolism. Interestingly, the predicted upstream gene regulators included NFκB, IL-21 and LPS, which are all associated with immune responses. Taken together, these data provide evidence that metritis leaves a persistent mark on the granulosa cell transcriptome, which could negatively impact ovarian function, oocyte health and fertility.

We evaluated granulosa cells from dominant follicles 63 days post partum, which means the follicles were in their early stages of development during active disease. The RNAseq analysis of cells aspirated from dominant follicles revealed INHBA and CYP19A1 were among the five highest expressed genes in samples, confirming that the majority of cells sequenced were granulosa cells of large follicles (Hatzirodos et al. 2014, 2017). Among the differentially expressed granulosa cell genes, 144 were upregulated and 33 genes were downregulated. The highest upregulated gene in metritis cows was intelectin 1 (ITLN1), a carbohydrate-binding protein which recognizes bacterial glycans (Wesener et al. 2015). The increased ITLN1 expression may reflect an effect of metritis on the transcriptome that persisted after resolution of disease or from persistent exposure to pathogen-associated molecular patterns. It was notable that LPS was detectable in follicular fluid of all cows in concentrations that did not differ between groups, although they were lower than those in follicular fluid from cows with active endometritis (Herath et al. 2007). Because these cows were 63 ± 3 days postpartum when follicles were aspirated, at least 6 weeks after the resolution of the disease, it is not surprising that concentrations of LPS in the follicular fluid did not differ between groups. We suggest that differences that might have occurred early in lactation during active disease (Herath et al. 2007) likely equalized over time and the values detected at 63 days postpartum likely reflected the background exposure from LPS of other origins such as rumen or miscellaneous bacterial infections. Nevertheless, granulosa cells increase secretion of IL-8 in response to LPS (Bromfield & Sheldon 2011), and in the present study, there was a positive association between IL-8 and LPS in follicular fluid. The responsiveness of cells to LPS is also glucose dependent (Turner et al. 2016), and we also noted a positive association between IL-8 and glucose in follicular fluid. Interestingly, predictions for upstream regulators of differentially expressed genes in the metritis cows included LPS and NFκB. Granulosa cells respond to LPS via the innate immune receptor Toll-like receptor 4 (Herath et al. 2007, Bromfield & Sheldon 2011), and NFκB is central to innate immune responses (Moresco et al. 2011).

The analysis of 177 differentially regulated genes described 39 significantly affected canonical pathways, including 18 pathways associated with immune response, including Th1 pathway, Th1 and Th2 activation, natural killer cell signaling and cross-talk between dendritic cells. Th1 and Th2 cells are involved in the adaptive immune response to bacteria and parasites, respectively, as well as in the transition from innate to cell-mediated immunity (Romagnani 1999). Knowledge about the adaptive immune response during metritis is less advanced than for innate immunity, which initially dominates the endometrial response to infection (Herath et al. 2009, Sheldon & Roberts 2010). Prediction of downstream pathways regulated by differentially expressed genes also included stimulation of lymphocytes, further implicating a role of the adaptive immune response in cows with metritis after the resolution of disease. Although the ovarian follicle is normally deplete of hematopoietic immune cells prior to ovulation (Bromfield & Sheldon 2013), we identified canonical pathways and gene networks associated with immune cell trafficking. While not investigated here, it may be prudent to further explore whether immune cells traffic into follicles of cows with metritis in future studies.

When evaluating other genes involved in immunity, most presented a non-significant numerical increase in expression in metritis cows, including ankyrin repeat domain-containing protein 17 (ANKRD17), chemokine ligand 2 (CXCL2) and IL18. The gene ANKRD17 was upregulated 4.8-log2 fold in granulosa cells of metritis cows and is associated with antibacterial innate immune activation by the nucleotide‐binding domain and leucine‐rich repeat (NLR) receptors, NOD1 and NOD2 (Menning & Kufer 2013). As a chemokine, CXCL2 is a neutrophil chemoattractant secreted in response to LPS (Wolpe & Cerami 1989) and may increase the recruitment of innate immune cells to the ovary of cows with metritis. The cytokine IL-18 is involved in the initiation of cell-mediated immunity and increases interferon production by natural killer cells (Netea et al. 2015). Expression of neutrophil cytosolic factor 2 (NCF2) was increased 7.9-log2 fold in metritis cows. Neutrophil cytosolic factor 2 is a cofactor of NADPH oxidase, important in immune cell function and phagocytosis. Increased NCF2 activity likely also increases the potential for reactive oxygen accumulation, which reduces oocyte developmental competence (Tamura et al. 2008, Lai et al. 2018). Increased granulosa cell NCF2 expression has been implicated in poorer follicle quality of patients with polycystic ovarian syndrome (Kaur et al. 2012).

Ovulation has been described as an inflammatory event (Espey 1980, Spanel-Borowski 2011). It is possible that the immunological events described here by RNAseq may be a reflection in the differential capacity of cows with metritis to ovulate in a timely manner in response to estrous synchronization. Cows with uterine inflammation have slower growing follicles and are less likely to ovulate (Suzuki et al. 2001, Sheldon et al. 2002, Williams et al. 2007, 2008). In the present study, expression of the LH receptor (LHCGR) was −2.1 log2-fold lower in granulosa cells of metritis cows than control cows; a decrease in expression which may alter the capacity of the dominant follicle to respond to ovulatory LH. While pathway analysis did not highlight endocrine function, we evaluated the expression of genes involved in steroidogenesis because of their importance in granulosa cell function (Adashi et al. 1985). In vitro, granulosa cells decrease estradiol secretion when exposed to LPS as a result of reduced CYP19A1 expression (Herath et al. 2007). In the present study, granulosa cells from metritis cows had non-significant reductions in expression of genes involved in steroidogenesis, including CYP11A1, CYP19A1, HSD3B1 and STAR, but follicular fluid estradiol concentrations did not differ significantly from control cows. It may be that granulosa cell endocrine function in cows is no longer effected after the resolution of disease. This is in contrast to previous investigations using cows with resolved subclinical endometritis that had reduced estradiol in 8–10 mm follicles, follicles considerably smaller than those assessed here (Green et al. 2011).

Analysis of gene networks enriched among differentially expressed genes in metritis cows revealed that cell–cell signaling, molecular transport, cell cycle, DNA repair and cellular metabolism were downregulated. Granulosa cell function is critical in providing oocyte with required nutrition in the form of carbohydrates and lipids, in addition to maintaining oocyte meiotic arrest and the induction of oocyte maturation (Sugiura et al. 2007, Su et al. 2008, Bromfield & Piersanti 2019). Furthermore, communication between granulosa cells and the oocyte is facilitated by soluble secreted factors and direct cell–cell communication via transzonal projections (Matzuk et al. 2002, Albertini & Barrett 2003). Perturbing granulosa cell metabolism or communication could reduce oocyte quality and the capacity to develop through the blastocyst stage. Alterations to granulosa cell function have been associated with reduced fertility in aging women (Tatone & Amicarelli 2013), while it has been suggested that other human pathologies have granulosa function as a component of infertility (Toya et al. 2000, Gioacchini et al. 2018). Collectively, we suggest that the alterations in granulosa cell transcriptome may contribute to reduced fertility typically observed in cows previously diagnosed with metritis.

In conclusion, our data provide evidence that postpartum metritis has an enduring effect on the granulosa cell transcriptome of dominant follicles 6 weeks after resolution of the disease. Collectively, we propose that these disease-dependent changes in the granulosa cell transcriptome could impair the follicle microenvironment and oocyte health. Understanding the mechanisms by which disease impacts long-term granulosa cell transcriptome is an important step in attempting to minimize the negative consequences of uterine disease on fertility.

Supplementary data

This is linked to the online version of the paper at https://doi.org/10.1530/REP-19-0037.

Declaration of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Funding

Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Number R01HD084316. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Acknowledgements

The authors would like to thank Todd Pritchard, Miguel Torrado and the staff at the University of Florida Dairy Research Unit. In addition, we would like to extend our gratitude to the University of Florida ICBR staff, Dr Fahong Yu and Yanping Zhang, for assistance with RNAseq and bioinformatics. They would also like to thank Jason Rizo, Paula Molinari and Amanda Teixeira for their contributions during the study.

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    Follicle evaluation 63 days post partum. Follicular fluid was aspirated on day 63 post partum from control cows (gray bar, n = 19) and cows that had metritis during the first 21 days post partum (black bars, n = 15). The diameter of each dominant follicle was measured (A). Follicular fluid was evaluated for estradiol (B), IL-8 (C), LPS (D), glucose (E) and cholesterol concentrations (F). Data are presented as LSM + s.e.m. and analyzed using the generalized linear mixed model. Associations between the follicular fluid concentrations of IL-8 and LPS (G), glucose (H) or cholesterol (I) are described.

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    Differentially expressed genes in granulosa cells of cows with metritis. A volcano plot (A) represents each gene detected using RNAseq as an individual dot. Genes that were differentially regulated in granulosa cells of metritis cows are shown in orange (P ≤ 0.05), and non-significantly affected genes are shown in blue. Expression of genes is based on the log2 fold-change (x-axis) plotted against the P value for each gene. (B) Plot for principal component analysis of RNAseq data from granulosa cells of cows that had metritis (blue) or control cows (red).

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    Heatmap of differentially expressed granulosa cell genes in metritis cows. A heatmap and hierarchical clustering of differentially expressed genes in metritis cows (blue) and control cows (red). Each differentially expressed gene is represented by a single row, and each cow is represented by a single column. Both rows and columns are clustered using correlation distance and average linkage.

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    Canonical pathways derived from differentially expressed genes in granulosa cells of cows with metritis. Depiction of affected canonical pathways identified based on differentially expressed genes in granulosa cells of cows with metritis compared with cells from control cows, with a -log P value >1.3. Solid bars represent canonical pathways with a positive z-score, gray bars represent canonical pathways with no calculable z-score.

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    Differentially regulated gene networks in granulosa cells of cows with metritis. Affected gene networks based on differentially regulated genes of granulosa cells from cows with metritis. (A) Cell-to-cell signaling and interaction, hematological system development and function, immune cell trafficking; (B) cell signaling, molecular transport, nucleic acid metabolism and (C) developmental disorder, neurological disease, cardiovascular disease. Symbols in red are upregulated, and symbols in green are downregulated in granulosa cells of metritis cows.

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    Predicted upstream regulators in granulosa cells of cows with metritis. The Predicted upstream regulators of affected gene networks included IL-12 (A) and IL-21 (B), which are involved in stimulation of lymphocyte and cytolysis gene networks, respectively. NF-κB (C) and E. coli LPS (D) are predicted upstream regulators of differentially expressed genes. Symbols in red are upregulated, and symbols in green are downregulated in granulosa cells of metritis cows. Symbols in orange are predicted upstream regulators of function.