Placental Changes in the serotonin transporter (Slc6a4) knockout mouse suggest a role for serotonin in controlling nutrient acquisition

Abstract

Introduction: The mouse placenta accumulates and possibly produces serotonin (5-hydroxytryptamine; 5-HT) in parietal trophoblast giant cells (pTGC) located at the interface between the placenta and maternal deciduum. However, the roles of 5-HT in placental function are unclear. This lack of information is unfortunate, given that selective serotonin-reuptake inhibitors are commonly used to combat depression in pregnant women. The high affinity 5-HT transporter SLC6A4 (also known as SERT) is the target of such drugs and likely controls much of 5-HT uptake into pTGC and other placental cells. We hypothesized that ablation of the Slc6a4 gene would result in morphological changes correlated with placental gene expression changes, especially for those involved in nutrient acquisition and metabolism, and thereby, provide insights into 5-HT placental function.

Methods: Placentas were collected at embryonic age (E) 12.5 from Slc6a4 knockout (KO) and wild-type (WT) conceptuses. Histological analyses, RNAseq, qPCR, and integrative correlation analyses were performed.

Results: Slc6a4 KO placentas had a considerable increased pTGC to spongiotrophoblast area ratio relative to WT placentas and significantly elevated expression of genes associated with intestinal functions, including nutrient sensing, uptake, and catabolism, and blood clotting. Integrative correlation analyses revealed upregulation of many of these genes was correlated with pTGC layer expansion. One other key gene was dopa decarboxylase (Ddc), which catalyzes conversion of L-5-hydroxytryptophan to 5-HT.

Discussion: Our studies possibly suggest a new paradigm relating to how 5-HT operates in the placenta, namely as a factor regulating metabolic functions and blood coagulation. We further suggest that pTGC might be functional analogs of enterochromaffin 5-HT-positive cells of the intestinal mucosa, which regulate similar activities within the gut. Further work, including proteomics and metabolomic studies, are needed to buttress our hypothesis.

Keywords: Conceptus; Gestation; Neurotransmitter; Nutrient acquisition; Nutrient metabolism; RNAseq; Serotonin; Trophoblast.

Fig. 1.

Placental histology of SERT KO and WT mice. Panels A and B show the placental histology for WT and SERT KO conceptuses, respectively. C) Depicts the results of the spongioTB to pTGC in both placentas. As shown, this ratio is reduced in SERT KO vs. WT placenta, which is likely attributed to increase thickness of the pTGC in the former. LA= labyrinth; spongioTB= spongiotrophoblast; pTGC= parietal trophoblast giant cells. N = 3 SERT KO and 3 WT placenta.

Fig. 2.

Placental gene expression in SERT KO vs. WT conceptuses. A) 3D PCA plot of RNAseq results from placenta of SERT KO males vs. WT males. B) Heatmap based on all transcripts identified in placenta of SERT KO males vs. WT males. d. C) Volcano plot analyses of gene transcripts identified in placenta of SERT KO males vs. WT males. Transcripts represented by Orange: FDR < 0.05; Transcripts represented by Red: log2FoldChange < −1 or > 1; Transcripts represented by Green: FDR < 0.05 AND log2FoldChange < −1 or > 1. N = 3 SERT KO and 3 WT placenta.

Fig. 3.

TissueEnrich results for SERT KO vs. WT placentas. Bar plot distribution of the tissue enrichment of all DE genes.

Fig. 4.

STRING and hub gene analyses for SERT KO males vs. WT males. The protein-protein-interactions (PPI) were determined by STRING analysis. The PPI files generated with STRING were imported into the cytohubba app [39] in Cytoscape [40] to examine for the top 10 hub genes. Within this program, hub genes were determined with MCC analysis, as recommended [39].

Fig. 5.

Pathways predicted to be affected in SERT KO vs. WT placenta. Functional enrichment analyses with the WEB-based Gene SeT AnaLysis Toolkit (WebGestalt) [41] and GO molecular function terms was performed based on DE genes to determine which pathways might be affected in SERT KO relative to WT placenta. As shown in this Fig., many of the pathways involve nutrient, enzyme, hormone binding activity, including for vitamins, lipids, carbohydrates, and proteins. Those categories surrounded by a dark blue box are significant based on a false discovery rate (FDR) ≤ 0.05, whereas categories surrounded by a light blue box are significant based on a p value ≤ 0.05. Connecting lines indicate inter-relationships between categories.

Fig. 6.

qPCR results for genes identified to be hub genes. The qPCR results largely confirmed the RNAseq data. Apoc3, Hgd, Kng1, Kng2, Proz, Serpinc1, Serpind1, and SerpinF2 were significantly upregulated in SERT KO mice relative to WT. Ftcd and Hpx also showed a trend to be elevated in SERT KO vs. WT mice. N = 3 SERT KO and 3 WT placenta.

Fig. 7.

Relationship of WGCNA results and placental morphometric assessments for SERT KO vs. WT conceptuses. Modules identified in Supplementary Fig. 1 were correlated with placental morphometric assessments (Fig. 1). Each row corresponds to a Module Eigengene (ME), and colors represent the correlation coefficient between the ME and one of the four placental histological measurements (LA to pTGC ratio; SpongioTB to pTGC ratio). Numbers at the top of rows represents degree of correlation, and values with a negative integer indicate an inverse correlation with one of the four placental measurements. A legend that ranges from blue to red shades (−1 to 1, respectively) is included to the right of the Figure.

Fig. 8.

TissueEnrich results for genes in the ME dark turquoise module that was shown to be significantly correlated with the placental histology results.

Fig. 9.

STRING and hub gene analyses for genes in the ME dark turquoise module. The protein-protein-interactions (PPI) were determined by STRING analysis. The PPI files generated with STRING were imported into the cytohubba app [39] in Cytoscape [40] to examine for the top 10 hub genes. Within this program, hub genes were determined with MCC analysis, as recommended [39].

Fig. 10.

Pathways predicted to be affected based on gene in the ME dark turquoise module. Functional enrichment analyses with the WEB-based Gene SeT AnaLysis Toolkit (WebGestalt) [41] and screened with KEGG and REACTOME pathways. The top pathways likely to be affected based on gene-sets in this module are serotonin and melatonin biosynthesis, intestinal hexose absorption, synthesis of 12-eicosatetraenoic acid derivatives, proton-coupled monocarboxylate transport, cellular hexose transport, and tryptophan metabolism. All categories listed are significant based on a p-value of ≤ 0.05.