Methylation analysis by targeted bisulfite sequencing in large for gestational age (LGA) newborns: the LARGAN cohort

Abstract

Background: In 1990, David Barker proposed that prenatal nutrition is directly linked to adult cardiovascular disease. Since then, the relationship between adult cardiovascular risk, metabolic syndrome and birth weight has been widely documented. Here, we used the TruSeq Methyl Capture EPIC platform to compare the methylation patterns in cord blood from large for gestational age (LGA) vs adequate for gestational age (AGA) newborns from the LARGAN cohort.

Results: We found 1672 differentially methylated CpGs (DMCs) with a nominal p < 0.05 and 48 differentially methylated regions (DMRs) with a corrected p < 0.05 between the LGA and AGA groups. A systems biology approach identified several biological processes significantly enriched with genes in association with DMCs with FDR < 0.05, including regulation of transcription, regulation of epinephrine secretion, norepinephrine biosynthesis, receptor transactivation, forebrain regionalization and several terms related to kidney and cardiovascular development. Gene ontology analysis of the genes in association with the 48 DMRs identified several significantly enriched biological processes related to kidney development, including mesonephric duct development and nephron tubule development. Furthermore, our dataset identified several DNA methylation markers enriched in gene networks involved in biological pathways and rare diseases of the cardiovascular system, kidneys, and metabolism.

Conclusions: Our study identified several DMCs/DMRs in association with fetal overgrowth. The use of cord blood as a material for the identification of DNA methylation biomarkers gives us the possibility to perform follow-up studies on the same patients as they grow. These studies will not only help us understand how the methylome responds to continuum postnatal growth but also link early alterations of the DNA methylome with later clinical markers of growth and metabolic fitness.

Keywords: Cardiovascular; Development; Epigenome; Kidney; Markers; Neonatal.

Fig. 1

Identification of DMCs in LGA newborns. (A) Distribution of DMCs represented as the distance to the closest TSS, shown in 500 bp bins. Red bars denote the number of hypermethylated DMCs per bin, and blue bars denote hypomethylated DMCs per bin. (B) Enrichment analysis of the Gene Ontology (GO) category Biological Process (BP) of all hypermethylated DMCs with nominal p < 0.05. Only BPs with corrected p < 0.05 are shown. (C) Gene networks of selected BPs: Norepinephrine, Kidney Development and Cardiovascular Development. The methylation difference between LGA and AGA is shown as a graded color scale, where white is no change and red is hypermethylation. Genes (nodes) are shown either as circles or diamonds, where circles are those showing transcriptional activity according to GO regulation of transcription, DNA-templated (GO:0006355). Edges (connections/lines between nodes) represent co-expression, pathways, colocalization, shared protein domains or physical interactions between the two genes/proteins in the network according to GeneMANIA

Fig. 2

Identification of DMRs in LGA newborns. (A) Enrichment analysis of Gene Ontology (GO) category Biological Process (BP) of all DMRs with corrected p < 0.05 are shown. Percent methylation of DMRs associated with genes involved in kidney development (B), diabetic pathologies (C), metabolism and appetite (D) and cell division (E)

Fig. 3

Identification of common Gene IDs identified by our DMC and DMR analysis in comparison with that of Küpers et al. [22], who used body weight at birth as a continuous variable. The Venn diagram shows the overlap between datasets with the numbers of Gene IDs in each. The table shows the intersection of gene IDs identified by our DMR and DMC analysis as well as DMR and genes from the Küpers et al. paper