Placental microRNA expression associates with birthweight through control of adipokines: results from two independent cohorts

Abstract

MicroRNAs are non-coding RNAs that regulate gene expression post-transcriptionally. In the placenta, the master regulator of fetal growth and development, microRNAs shape the basic processes of trophoblast biology and specific microRNA have been associated with fetal growth.To comprehensively assess the role of microRNAs in placental function and fetal development, we have performed small RNA sequencing to profile placental microRNAs from two independent mother-infant cohorts: the Rhode Island Child Health Study (n=225) and the New Hampshire Birth Cohort Study (n=317). We modeled microRNA counts on infant birthweight percentile (BWP) in each cohort, while accounting for race, sex, parity and technical factors, using negative binomial generalized linear models. We identified microRNAs that were differentially expressed (DEmiRs) with BWP at false discovery rate (FDR) less than 0.05 in both cohorts. hsa-miR-532-5p (miR-532) was positively associated with BWP in both cohorts. By integrating parallel whole transcriptome and small RNA sequencing in the RICHS cohort, we identified putative targets of miR-532. These targets are enriched for pathways involved in adipogenesis, adipocytokine signaling, energy metabolism and hypoxia response, and included Leptin, which we further demonstrated to have decreasing expression with increasing BWP, particularly in male infants. Overall, we have shown a robust and reproducible association of miR-532 with BWP, which could influence BWP through regulation of adipocytokines Leptin and Adiponectin.

Variation in Placental microRNA Expression Associates with Familial Cardiovascular Disease

“…The 66 predicted DEmiR targets were tested for pathway overrepresentation within ConsensusPathDB (CPDB) [24-26] . Pathways related to TGFβ signaling, cellular metabolism, and immunomodulation were overrepresented among DEmiR targets (q-value < 0.05).…”

“…However, given that miRNA do not require perfect complementarity with a target mRNA and can only target mRNAs expressed in the examined tissue, bioinformatic target prediction is prone to generating false positives. To enhance our dataset with true miRNA:mRNA relationships we utilized parallel total RNA sequencing data in RICHS [24] . Only miRNA:mRNA pairs where the transcript abundance of a DEmiR and its miRDIP predicted target were negatively correlated (Pearson's correlation, p-value < 0.05) were considered in downstream pathway analyses.…”

“…The RNA was quantitated on a NanoDrop (Thermo Fisher, Waltham, MA, USA) and quality checked on Agilent Bioanalyzer using the Agilent RNA 6000 Nano kit (Agilent, Santa Clara, CA, USA). Single end, 1 x 50 bp next generation sequencing of placental miRNA was performed by Omega Bioservices (Norcross, Georgia) as previously described [24] .…”

“…Normalized counts were then exported from DESeq2 for Surrogate Variable Analysis (SVA). The Variance Stabilization Transformation (VST) was applied to count matrices to yield approximately normalized and log 2 -transformed abundances, which were utilized in correlation analyses [24] .…”

“…CPDB utilizes 12 separate biological pathway databases, and calculates an enrichment p-value from the hypergeometric distribution of genes in the list of miRNA targets and the pathway gene set. Only mRNAs that were expressed >1cpm in at least 10% of the RICHS samples were included as the background for the pathway analysis [24] . False discovery rates were calculated from the enrichment p-values, and a q-value less than 0.05 was considered a significant enrichment of miRNA targets in the tested pathway.…”