Differential gene expression in bovine endometrial epithelial cells after challenge with LPS; specific implications for genes involved in embryo maternal interactions

Abstract

Lipopolysaccharide (LPS) expressed on the surface of Gram-negative bacteria activates pro-inflammatory pathways, dys-regulates the function of endometrial cells and is a key player in the mechanisms involved in endometritis. This study aimed to investigate the effects of LPS on bovine endometrial epithelial cells (bEEC) from whole transcriptome with a special focus on genes involved in embryo-maternal interactions. Following in vitro culture, bEEC from three cows were exposed to 0, 2, and 8 μg/mL LPS for 24h. RNA samples extracted at 0 and 24 hours were analyzed by RNA sequencing (RNA-seq). At 24h, 2035 differentially expressed genes (DEGs) were identified between controls and samples treated with 2 μg/mL LPS. Gene ontology analysis showed that over-expressed DEGs were associated to immune response, response to stress and external stimuli, catalytic activity, and cell cycle. Genes associated with cell membrane and cell adhesion pathways were under-expressed. LPS induced changes in expression of specific genes related to embryo-maternal interactions including under-expression of eight members of the cadherin superfamily, over-expression of six members of the mucin family, and differential expression of a large set of genes binding the above molecules and of more than 20 transcripts coding for cytokines and their receptors. Type I interferon-τ dependent genes were also over-expressed. From a sub-set of 19 genes, (biological replicates of bEEC from cows taken at time 6 (n = 3), 24 (n = 6) and 48 hours (n = 3), and 2 technical replicates per sample) differential gene expression was confirmed by RT2-qPCR (r2 between fold changes at 24 hours by RT2-qPCR and RNA-seq = 0.97). These results indicate that LPS affects the function of bEEC in many ways by differential transcription, glycolytic metabolism and oxidative stress. Many transcriptomic signatures related to implantation and embryo maternal interactions were strongly affected by LPS. These results pave the way for further studies to investigate the duration of these changes and their possible impact on endometrial function and fertility.

Fig 1. Morphology and purity of bovine endometrial epithelial cells.

A. Cell morphology of bovine endometrial epithelial cells (bEECs) B. Representative Flow cytometric histogram of bEECs expressing the epithelial-specific cytokeratin-18 showing their appearance to the epithelial cell population. (98% of cells are in M1 region corresponding to this population).

Fig 2. Comparison of differentially expressed genes.

A. Design of comparisons made between groups (shown above) and Venn diagram of differentially expressed genes (DEGs) between each LPS-treated groups and controls (below). B. Venn diagram including the distribution of DEGs found in both LPS treatments together (combined 2 and 8 μg/mL LPS) versus control cells showing that most of the genes which were not common in A expressed similar changes as becoming DEGs in the “combined” comparison. C. Correlation of log2 fold changes between the 2 and 8 μg/ml LPS-treatment. D. MA-plot of all DEGs identified in the different LPS treatments, log2 fold changes for each gene between two samples (on the y-axis) versus the mean expression signal (on the x-axis). Blue (2μg/ml LPS), green (8 μg/ml LPS) and red dots (common) represent the genes with significant differential expression (Adjusted P value <0.05 from Benjamini Hochberg correction). Significant mean positive log2 fold changes corresponds to increased expression, whereas negative values corresponds to decreased expression. E. Principal Component Analysis (PCA) from the top 500 most variably expressed genes at 24h post LPS. Symbols for each cow have a different shape and with a different color for each condition as indicated to the right. Axis 1 reflects individual variation in basic gene expression between individuals whereas axis 2 reflects the effect of LPS treatment.

Fig 3. Gene ontology (GO) and KEGG pathways analysis of over-expressed and under-expressed DEGs.

A. Overrepresented GO-terms pathways, * = pathways related to immune function. B. Underrepresented GO-terms pathways C. Overrepresented KEGG pathways D. Underrepresented KEGG pathways. Vertical red dotted bars indicate the cut-off level for significance (P < 0.05). P-values were adjusted by Benjamini-Hochberg correction for multiple testing.

Fig 4. Comparison of DEGs after challenge with LPS to GeneCards database.

Numbers of DEGs participating in acute inflammation (410 DEGs), innate immune (441 DEGs), immune tolerance in pregnancy (120 DEGs), allergy (153 DEGs), cell adhesion (626 DEGs) and embryo implantation (118 DEGs). The figure illustrates the relatively high number of DEGs common to two pathways and 24 common DEGs were involved in all above mentioned biological processes (http://www.genecards.org/).

Fig 5. Mean Log2 fold changes (mean ± sem) for DEGs related to implantation and maternal response to the embryo.

A. Changes in mRNA expression for genes of the bovine leukocyte antigen molecules (BoLA) family. B. Changes in expression for genes related to cell adhesion. C. Changes in expression for mucins, integrins and metalloproteinase genes. D. Changes in expression for interferon Tau induced genes. E. Changes in expression for genes of the galectin family. All changes significant p < 0.05 from adjusted p value.

Fig 6. Changes in gene expression from RT²-qPCR at 6, 24 and 48 hours following LPS challenge.

For each time point, z = (expression after LPS- expression in controls) / expression in controls) for a set of DEGs identified at 24 hours post LPS from RNA sequencing. The order of genes from left to right is organized following the amplitude of fold change at 6 hours from RT²-qPCR. NS: not significant, * p < 0.05, ** P < 0.01, *** p < 0.001.