Heather Talbott

ATF3 Expression in the Corpus Luteum: Possible Role in Luteal Regression†

The present study investigated the induction and possible role of activating transcription factor 3 (ATF3) in the corpus luteum. Postpubertal cattle were treated at midcycle with prostaglandin F2α(PGF) for 0-4 hours. Luteal tissue was processed for immunohistochemistry, in situ hybridization, and isolation of protein and RNA. Ovaries were also collected from midluteal phase and first-trimester pregnant cows. Luteal cells were prepared and sorted by centrifugal elutriation to obtain purified small (SLCs) and large luteal cells (LLCs). Real-time PCR and in situ hybridization showed that ATF3 mRNA increased within 1 hour of PGF treatment in vivo. Western blot and immunohistochemistry demonstrated that ATF3 protein was expressed in the nuclei of LLC within 1 hour and was maintained for at least 4 hours. PGF treatment in vitro increased ATF3 expression only in LLC, whereas TNF induced ATF3 in both SLCs and LLCs. PGF stimulated concentration- and time-dependent increases in ATF3 and phosphorylation of MAPKs in LLCs. Combinations of MAPK inhibitors suppressed ATF3 expression in LLCs. Adenoviral-mediated expression of ATF3 inhibited LH-stimulated cAMP response element reporter luciferase activity and progesterone production in LLCs and SLCs but did not alter cell viability or change the expression or activity of key regulators of progesterone synthesis. In conclusion, the action of PGF in LLCs is associated with the rapid activation of stress-activated protein kinases and the induction of ATF3, which may contribute to the reduction in steroid synthesis during luteal regression. ATF3 appears to affect gonadotropin-stimulated progesterone secretion at a step or steps downstream of PKA signaling and before cholesterol conversion to progesterone.

Effects of IL8 and immune cells on the regulation of luteal progesterone secretion

Recent studies have suggested that chemokines may mediate the luteolytic action of prostaglandin F2α (PGF). Our objective was to identify chemokines induced by PGF in vivo and to determine the effects of interleukin 8 (IL8) on specific luteal cell types in vitro. Mid-cycle cows were injected with saline or PGF, ovaries were removed after 0.5-4 h, and expression of chemokine was analyzed by qPCR. In vitro expression of IL8 was analyzed after PGF administration and with cell signaling inhibitors to determine the mechanism of PGF-induced chemokine expression. Purified neutrophils were analyzed for migration and activation in response to IL8 and PGF. Purified luteal cell types (steroidogenic, endothelial, and fibroblast cells) were used to identify which cells respond to chemokines. Neutrophils and peripheral blood mononuclear cells (PBMCs) were cocultured with steroidogenic cells to determine their effect on progesterone production. IL8, CXCL2, CCL2, and CCL8 transcripts were rapidly increased following PGF treatment in vivo. The stimulatory action of PGF on IL8 mRNA expression in vitro was prevented by inhibition of p38 and JNK signaling. IL8, but not PGF, TNF, or TGFB1, stimulated neutrophil migration. IL8 had no apparent action in purified luteal steroidogenic, endothelial, or fibroblast cells, but stimulated ERK phosphorylation in neutrophils. In coculture experiments neither IL8 nor activated neutrophils altered basal or LH-stimulated luteal cell progesterone synthesis. In contrast, activated PBMCs inhibited LH-stimulated progesterone synthesis from cultured luteal cells. These data implicate a complex cascade of events during luteolysis, involving chemokine signaling, neutrophil recruitment, and immune cell action within the corpus luteum.

Gene expression profiling of bovine ovarian follicular and luteal cells provides insight into cellular identities and functions.

After ovulation, somatic cells of the ovarian follicle (theca and granulosa cells) become the small and large luteal cells of the corpus luteum. Aside from known cell type-specific receptors and steroidogenic enzymes, little is known about the differences in the gene expression profiles of these four cell types. Analysis of the RNA present in each bovine cell type using Affymetrix microarrays yielded new cell-specific genetic markers, functional insight into the behavior of each cell type via Gene Ontology Annotations and Ingenuity Pathway Analysis, and evidence of small and large luteal cell lineages using Principle Component Analysis. Enriched expression of select genes for each cell type was validated by qPCR. This expression analysis offers insight into cell-specific behaviors and the differentiation process that transforms somatic follicular cells into luteal cells.

Lipid Droplets and Metabolic Pathways Regulate Steroidogenesis in the Corpus Luteum

This review focuses on recent advances in the understanding of metabolic processes used by the corpus luteum to control steroidogenesis and other cellular functions. The corpus luteum has abundant lipid droplets that are believed to store cholesteryl esters and triglycerides. Recent studies in other tissues indicate that cytoplasmic lipid droplets serve as platforms for cell signaling and interactions with other organelles. Lipid droplets are also critical organelles for controlling cellular metabolism. Emerging evidence demonstrates that LH via activation of the cAMP and the protein kinase A (PKA) signaling pathway stimulates the phosphorylation and activation of hormone-sensitive lipase (HSL), an enzyme that hydrolyzes cholesteryl esters stored in lipid droplets to provide cholesterol for steroidogenesis and fatty acids for utilization by mitochondria for energy production. The energy sensor AMP-activated protein kinase (AMPK) can inhibit steroidogenesis by interrupting metabolic pathways that provide cholesterol to the mitochondria or the expression of genes required for steroidogenesis. In addition to lipid droplets, autophagy also contributes to the regulation of the metabolic balance of the cell by eliminating damaged organelles and providing cells with essential nutrients during starvation. Autophagy in luteal cells is regulated by signaling pathways that impact AMPK activity and lipid droplet homeostasis. In summary, a number of signaling pathways converge on luteal lipid droplets to regulate steroidogenesis and metabolism. Knowledge of metabolic pathways in luteal cells is fundamental to understanding events that control the function and lifespan of the corpus luteum.

Transcriptomes of bovine ovarian follicular and luteal cells

Affymetrix Bovine GeneChip® Gene 1.0 ST Array RNA expression analysis was performed on four somatic ovarian cell types: the granulosa cells (GCs) and theca cells (TCs) of the dominant follicle and the large luteal cells (LLCs) and small luteal cells (SLCs) of the corpus luteum. The normalized linear microarray data was deposited to the NCBI GEO repository (GSE83524). Subsequent ANOVA determined genes that were enriched (≥2 fold more) or decreased (≤−2 fold less) in one cell type compared to all three other cell types, and these analyzed and filtered datasets are presented as tables. Genes that were shared in enriched expression in both follicular cell types (GCs and TCs) or in both luteal cells types (LLCs and SLCs) are also reported in tables. The standard deviation of the analyzed array data in relation to the log of the expression values is shown as a figure. These data have been further analyzed and interpreted in the companion article “Gene expression profiling of ovarian follicular and luteal cells provides insight into cellular identities and functions” (Romereim et al., 2017) [1].

Confident difference criterion: a new Bayesian differentially expressed gene selection algorithm with applications

BackgroundRecently, the Bayesian method becomes more popular for analyzing high dimensional gene expression data as it allows us to borrow information across different genes and provides powerful estimators for evaluating gene expression levels. It is crucial to develop a simple but efficient gene selection algorithm for detecting differentially expressed (DE) genes based on the Bayesian estimators.ResultsIn this paper, by extending the two-criterion idea of Chen et al. (Chen M-H, Ibrahim JG, Chi Y-Y. A new class of mixture models for differential gene expression in DNA microarray data. J Stat Plan Inference. 2008;138:387–404), we propose two new gene selection algorithms for general Bayesian models and name these new methods as the confident difference criterion methods. One is based on the standardized differences between two mean expression values among genes; the other adds the differences between two variances to it. The proposed confident difference criterion methods first evaluate the posterior probability of a gene having different gene expressions between competitive samples and then declare a gene to be DE if the posterior probability is large. The theoretical connection between the proposed first method based on the means and the Bayes factor approach proposed by Yu et al. (Yu F, Chen M-H, Kuo L. Detecting differentially expressed genes using alibrated Bayes factors. Statistica Sinica. 2008;18:783–802) is established under the normal-normal-model with equal variances between two samples. The empirical performance of the proposed methods is examined and compared to those of several existing methods via several simulations. The results from these simulation studies show that the proposed confident difference criterion methods outperform the existing methods when comparing gene expressions across different conditions for both microarray studies and sequence-based high-throughput studies. A real dataset is used to further demonstrate the proposed methodology. In the real data application, the confident difference criterion methods successfully identified more clinically important DE genes than the other methods.ConclusionThe confident difference criterion method proposed in this paper provides a new efficient approach for both microarray studies and sequence-based high-throughput studies to identify differentially expressed genes.

Transcriptomic and bioinformatics analysis of the early time-course of the response to prostaglandin F2 alpha in the bovine corpus luteum

RNA expression analysis was performed on the corpus luteum tissue at five time points after prostaglandin F2 alpha treatment of midcycle cows using an Affymetrix Bovine Gene v1 Array. The normalized linear microarray data was uploaded to the NCBI GEO repository (GSE94069). Subsequent statistical analysis determined differentially expressed transcripts ± 1.5-fold change from saline control with P ≤ 0.05. Gene ontology of differentially expressed transcripts was annotated by DAVID and Panther. Physiological characteristics of the study animals are presented in a figure. Bioinformatic analysis by Ingenuity Pathway Analysis was curated, compiled, and presented in tables. A dataset comparison with similar microarray analyses was performed and bioinformatics analysis by Ingenuity Pathway Analysis, DAVID, Panther, and String of differentially expressed genes from each dataset as well as the differentially expressed genes common to all three datasets were curated, compiled, and presented in tables. Finally, a table comparing four bioinformatics tools’ predictions of functions associated with genes common to all three datasets is presented. These data have been further analyzed and interpreted in the companion article “Early transcriptome responses of the bovine mid-cycle corpus luteum to prostaglandin F2 alpha includes cytokine signaling” [1].

Trafficking of Cholesterol from Lipid Droplets to Mitochondria in Bovine Luteal Cells: Acute Control of Progesterone Synthesis

The corpus luteum (CL) is a transient endocrine gland that synthesizes and secretes the steroid hormone, progesterone. Progesterone biosynthesis is a complex process, converting cholesterol via a series of enzymatic reactions, into progesterone. Lipid droplets in luteal cells store cholesterol in the form of cholesterol esters, which can be utilized for steroidogenesis. In small luteal cells, luteinizing hormone (LH) increases intracellular cAMP concentrations leading to activation of protein kinase A (PKA), which phosphorylates downstream proteins, such as hormone sensitive lipase (HSL). Phosphorylation of HSL at Ser563 leads to increased HSL activation and association with lipid droplets, events which theoretically release cholesterol, which can be used for progesterone synthesis. Bovine CL were obtained from a local abattoir, dispersed, and luteal cells were enriched for SLC via centrifugal elutriation. Our results reveal that LH, forskolin, and cAMP induce HSL phosphorylation at Ser563and Ser660. Moreover, inhibiting HSL activity attenuates LH-induced P4 synthesis. Confocal analysis revealed that LH stimulates translocation of HSL to lipid droplets and mitochondria. Furthermore, LH increased trafficking of cholesterol from the lipid droplets to the mitochondria which was dependent on both PKA and HSL activation. These results demonstrate cholesterol stored in lipid droplets are utilized for LH-induced progesterone biosynthesis. Likewise, PKA-induced activation of HSL is required for release and trafficking of cholesterol from the lipid droplets to the mitochondria. Taken together, these findings support a role for a PKA/HSL signaling pathway in response to LH and demonstrate the dynamic relationship between PKA, HSL, and the lipid droplets in the synthesis of progesterone. Highlights LH and PKA induce HSL phosphorylation at Ser563and Ser660 HSL is required for optimal LH-induced P4 synthesis LH stimulates translocation of HSL to lipid droplets and mitochondria LH stimulated trafficking of cholesterol from lipid droplets to mitochondria