Serious neonatal morbidities are associated with differences in DNA methylation among very preterm infants

Abstract

Background Infants born very preterm are more likely to experience neonatal morbidities compared to their term peers. Variations in DNA methylation (DNAm) associated with these morbidities may yield novel information about the processes impacted by these morbidities. Methods This study included 532 infants born < 30 weeks gestation, participating in the Neonatal Neurobehavior and Outcomes in Very Preterm Infants study. We used a neonatal morbidity risk score, which was an additive index of the number of morbidities experienced during the NICU stay, including bronchopulmonary dysplasia (BPD), severe brain injury, serious neonatal infections, and severe retinopathy of prematurity. DNA was collected from buccal cells at discharge from the NICU, and DNAm was measured using the Illumina MethylationEPIC. We tested for differential methylation in association with the neonatal morbidity risk score then tested for differentially methylated regions (DMRs) and overrepresentation of biological pathways. Results We identified ten differentially methylated CpGs (α Bonferroni-adjusted for 706,278 tests) that were associated with increasing neonatal morbidity risk scores at three intergenic regions and at HPS4, SRRD, FGFR1OP, TNS3, TMEM266, LRRC3B, ZNF780A, and TENM2. These mostly followed dose–response patterns, for 8 CpGs increasing DNAm associated with increased numbers of morbidities, while for 2 CpGs the risk score was associated with decreasing DNAm. BPD was the most substantial contributor to differential methylation. We also identified seven potential DMRs and over-representation of genes involved in Wnt signaling; however, these results were not significant after Bonferroni adjustment for multiple testing. Conclusions Neonatal DNAm, within genes involved in fibroblast growth factor activities, cellular invasion and migration, and neuronal signaling and development, are sensitive to the neonatal health complications of prematurity. We hypothesize that these epigenetic features may be representative of an integrated marker of neonatal health and development and are promising candidates to integrate with clinical information for studying developmental impairments in childhood.

Prenatal risk factors and neonatal DNA methylation in very preterm infants

“…As prematurity is a systemic condition impacting nearly all organ systems, peripheral tissues may be particularly relevant to study in this sample. Finally, although the differences in DNAm we observed were small (1–6%), they are consistent with what has been reported in other epidemiological studies investigating peripheral DNAm as it relates to other prenatal risk factors (e.g., smoking [ 70 ]), as well as previous studies in our sample [ 37 , 45 ]. However, small effects in DNAm are likely important [ 71 ], as they open a potential window into understanding mechanisms driving child health.…”

“…Pre-processing of data followed a modified workflow described elsewhere [ 37 ]. Array data were normalized via Noob normalization [ 38 , 39 ] and samples with more than 5% of probes yielding detection p -values > 1.0E-5 or mismatch between reported and predicted sex were excluded.…”

NEOage clocks - Epigenetic clocks to estimate post-menstrual and postnatal age in preterm infants

“…Children were included in this analysis if DNAm data was available. PMA in NOVI was calculated by adding PNA at buccal collection to the estimated gestational age at birth which was obtained via an established process [18, 19] and is described in detail by Everson et al [15].…”

“…DNA samples were plated randomly across 96-well plates and provided to the Emory University Integrated Genomics Core for bisulfite modification using the EZ DNA Methylation Kit (Zymo Research, Irvine, CA), and subsequent assessment of genome-wide DNAm using the Illumina MethylationEPIC Beadarray (Illumina, San Diego, CA) following standardized methods based on the manufacturer’s protocol. The pre-processing of the data followed a modified workflow described by Everson et al [15]. Array data were normalized via Noob normalization [20, 21] and samples with more than 5% of probes yielding detection p-values□>□1.0E-5 or mismatch between reported and predicted sex were excluded.…”

DNA methylation and brain dysmaturation in preterm infants

“…26,27 The neonatal methylome is sensitive to prenatal factors such as maternal smoking, 28 maternal body mass index, 29 as well as birth weight. 30 It is altered in association with co-morbidities of preterm birth, 26,31,32 and there is some evidence for legacy differences in the methylome two decades after preterm birth. 33 A meta-analysis investigating DNAm from umbilical cord blood identified widespread differential methylation associated with gestational age at birth across the gestational age range of 27-42 weeks, as measured on the Illumina 450k array.…”