Investigating Neonatal Health Risk Variables Through Cell-type Specific Methylome-wide Association Studies

Overview

Journal Clin Epigenetics

Publisher Biomed Central

Specialty Genetics

Date 2024 May 22

PMID 38778395

Authors

Thomas L Campbell

Lin Y Xie

Ralen H Johnson

Christina M Hultman

Edwin J C G van den Oord

Karolina A Aberg

Affiliations

Soon will be listed here.

Abstract

Adverse neonatal outcomes are a prevailing risk factor for both short- and long-term mortality and morbidity in infants. Given the importance of these outcomes, refining their assessment is paramount for improving prevention and care. Here we aim to enhance the assessment of these often correlated and multifaceted neonatal outcomes. To achieve this, we employ factor analysis to identify common and unique effects and further confirm these effects using criterion-related validity testing. This validation leverages methylome-wide profiles from neonatal blood. Specifically, we investigate nine neonatal health risk variables, including gestational age, Apgar score, three indicators of body size, jaundice, birth diagnosis, maternal preeclampsia, and maternal age. The methylomic profiles used for this research capture data from nearly all 28 million methylation sites in human blood, derived from the blood spot collected from 333 neonates, within 72 h post-birth. Our factor analysis revealed two common factors, size factor, that captured the shared effects of weight, head size, height, and gestational age and disease factor capturing the orthogonal shared effects of gestational age, combined with jaundice and birth diagnosis. To minimize false positives in the validation studies, validation was limited to variables with significant cumulative association as estimated through an in-sample replication procedure. This screening resulted in that the two common factors and the unique effects for gestational age, jaundice and Apgar were further investigated with full-scale cell-type specific methylome-wide association analyses. Highly significant, cell-type specific, associations were detected for both common effect factors and for Apgar. Gene Ontology analyses revealed multiple significant biologically relevant terms for the five fully investigated neonatal health risk variables. Given the established links between adverse neonatal outcomes and both immediate and long-term health, the distinct factor effects (representing the common and unique effects of the risk variables) and their biological profiles confirmed in our work, suggest their potential role as clinical biomarkers for assessing health risks and enhancing personalized care.

References

Chan R, Shabalin A, Xie L, Adkins D, Zhao M, Turecki G . Enrichment methods provide a feasible approach to comprehensive and adequately powered investigations of the brain methylome. Nucleic Acids Res. 2017; 45(11):e97. PMC: 5499761. DOI: 10.1093/nar/gkx143. View

He H, Yu Y, Wang H, Obel C, Li F, Li J . Five-Minute Apgar Score and the Risk of Mental Disorders During the First Four Decades of Life: A Nationwide Registry-Based Cohort Study in Denmark. Front Med (Lausanne). 2022; 8:796544. PMC: 8795588. DOI: 10.3389/fmed.2021.796544. View

Greenberg M, Bourchis D . The diverse roles of DNA methylation in mammalian development and disease. Nat Rev Mol Cell Biol. 2019; 20(10):590-607. DOI: 10.1038/s41580-019-0159-6. View

Razaz N, Cnattingius S, Joseph K . Association between Apgar scores of 7 to 9 and neonatal mortality and morbidity: population based cohort study of term infants in Sweden. BMJ. 2019; 365:l1656. PMC: 6503461. DOI: 10.1136/bmj.l1656. View

Galal M, Symonds I, Murray H, Petraglia F, Smith R . Postterm pregnancy. Facts Views Vis Obgyn. 2014; 4(3):175-87. PMC: 3991404. View

Pienkowski V, Kucharczyk M, Rydzanicz M, Poszewiecka B, Pachota K, Mlynek M . Breakpoint Mapping of Symptomatic Balanced Translocations Links the , and Genes to Novel Disease Phenotype. J Clin Med. 2020; 9(5). PMC: 7287862. DOI: 10.3390/jcm9051245. View

Beck S, Wojdyla D, Say L, Betran A, Merialdi M, Requejo J . The worldwide incidence of preterm birth: a systematic review of maternal mortality and morbidity. Bull World Health Organ. 2010; 88(1):31-8. PMC: 2802437. DOI: 10.2471/BLT.08.062554. View

Shabalin A, Hattab M, Clark S, Chan R, Kumar G, Aberg K . RaMWAS: fast methylome-wide association study pipeline for enrichment platforms. Bioinformatics. 2018; 34(13):2283-2285. PMC: 6022807. DOI: 10.1093/bioinformatics/bty069. View

Houseman E, Kelsey K, Wiencke J, Marsit C . Cell-composition effects in the analysis of DNA methylation array data: a mathematical perspective. BMC Bioinformatics. 2015; 16:95. PMC: 4392865. DOI: 10.1186/s12859-015-0527-y. View

10.

Aberg K, Chan R, van den Oord E . MBD-seq - realities of a misunderstood method for high-quality methylome-wide association studies. Epigenetics. 2019; 15(4):431-438. PMC: 7153543. DOI: 10.1080/15592294.2019.1695339. View

11.

Zheng S, Breeze C, Beck S, Teschendorff A . Identification of differentially methylated cell types in epigenome-wide association studies. Nat Methods. 2018; 15(12):1059-1066. PMC: 6277016. DOI: 10.1038/s41592-018-0213-x. View

12.

Chan R, Turecki G, Shabalin A, Guintivano J, Zhao M, Xie L . Cell Type-Specific Methylome-wide Association Studies Implicate Neurotrophin and Innate Immune Signaling in Major Depressive Disorder. Biol Psychiatry. 2020; 87(5):431-442. PMC: 9933050. DOI: 10.1016/j.biopsych.2019.10.014. View

13.

Shields B, Knight B, Powell R, Hattersley A, Wright D . Assessing newborn body composition using principal components analysis: differences in the determinants of fat and skeletal size. BMC Pediatr. 2006; 6:24. PMC: 1562417. DOI: 10.1186/1471-2431-6-24. View

14.

So J, Ningappa M, Glessner J, Min J, Ashokkumar C, Ranganathan S . Biliary-Atresia-Associated Mannosidase-1-Alpha-2 Gene Regulates Biliary and Ciliary Morphogenesis and Laterality. Front Physiol. 2020; 11:538701. PMC: 7662016. DOI: 10.3389/fphys.2020.538701. View

15.

Chan R, Shabalin A, Montano C, Hannon E, Hultman C, Fallin M . Independent Methylome-Wide Association Studies of Schizophrenia Detect Consistent Case-Control Differences. Schizophr Bull. 2019; 46(2):319-327. PMC: 7442362. DOI: 10.1093/schbul/sbz056. View

16.

Titus A, Gallimore R, Salas L, Christensen B . Cell-type deconvolution from DNA methylation: a review of recent applications. Hum Mol Genet. 2017; 26(R2):R216-R224. PMC: 5886462. DOI: 10.1093/hmg/ddx275. View

17.

Shen-Orr S, Tibshirani R, Khatri P, Bodian D, Staedtler F, Perry N . Cell type-specific gene expression differences in complex tissues. Nat Methods. 2010; 7(4):287-9. PMC: 3699332. DOI: 10.1038/nmeth.1439. View

18.

Zajkowicz A, Gdowicz-Klosok A, Krzesniak M, Janus P, Lasut B, Rusin M . The Alzheimer's disease-associated TREM2 gene is regulated by p53 tumor suppressor protein. Neurosci Lett. 2018; 681:62-67. DOI: 10.1016/j.neulet.2018.05.037. View

19.

Allis C, Jenuwein T . The molecular hallmarks of epigenetic control. Nat Rev Genet. 2016; 17(8):487-500. DOI: 10.1038/nrg.2016.59. View

20.

Guintivano J, Aberg K, Clark S, Rubinow D, Sullivan P, Meltzer-Brody S . Transcriptome-wide association study for postpartum depression implicates altered B-cell activation and insulin resistance. Mol Psychiatry. 2022; 27(6):2858-2867. PMC: 9156403. DOI: 10.1038/s41380-022-01525-7. View