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Evidence for the placenta-brain axis: multi-omic kernel aggregation predicts intellectual and social impairment in
Hudson P Santos1,2, Arjun Bhattacharya3, Robert M Joseph4
1Biobehavioral Laboratory, School of Nursing, University of North Carolina, 544 Carrington Hall, Campus Box 7460, Chapel Hill, NC, 27599-7460, USA. hsantosj@email.unc.edu.
Insights
Placental molecular profiles can predict intellectual and social impairments in extremely preterm infants. This multi-omic approach offers insights into neurodevelopmental disorders like Autism Spectrum Disorder (ASD).
Area of Science:
- Perinatal Medicine
- Neuroscience
- Genomics
Background:
- Extremely preterm infants face higher risks of intellectual and social impairments, including Autism Spectrum Disorder (ASD).
- The placenta plays a critical role in prenatal development, potentially influencing neurodevelopmental outcomes.
Purpose of the Study:
- To investigate associations between placental transcriptomic and epigenomic profiles and predict intellectual and social impairment in extremely preterm children.
- To explore the predictive power of placental multi-omics for neurodevelopmental outcomes and Autism Spectrum Disorder (ASD) risk.
Main Methods:
- Analysis of placental genome-wide mRNA, CpG methylation, and miRNA profiles in the Extremely Low Gestational Age Newborn (ELGAN) cohort (N=379).
- Utilized kernel aggregation regression to integrate multi-omic data for predicting intellectual ability (IQ) and social function (SRS).
- Examined associations between ASD status and multi-omic-predicted components of IQ and SRS.
Main Results:
- Genes involved in neurodevelopment and placental organization were linked to intellectual and social impairments.
- Placental multi-omics strongly predicted social function (12% variance in SRS) and intellectual ability (8% variance in IQ).
- Predicted SRS and IQ scores showed significant associations with ASD case-control status.
Conclusions:
- Integrating placental multi-omic biomarkers enhances prediction of social and intellectual abilities in extremely preterm children.
- Findings suggest an 'omnigenic' model for placenta-brain axis traits influencing neurodevelopment.
- This approach provides novel insights into the placental origins of neurodevelopmental impairments.
Background:
Children born extremely preterm are at heightened risk for intellectual and social impairment, including Autism Spectrum Disorder (ASD). There is increasing evidence for a key role of the placenta in prenatal developmental programming, suggesting that the placenta may, in part, contribute to origins of neurodevelopmental outcomes.
Methods:
We examined associations between placental transcriptomic and epigenomic profiles and assessed their ability to predict intellectual and social impairment at age 10 years in 379 children from the Extremely Low Gestational Age Newborn (ELGAN) cohort. Assessment of intellectual ability (IQ) and social function was completed with the Differential Ability Scales-II and Social Responsiveness Scale (SRS), respectively. Examining IQ and SRS allows for studying ASD risk beyond the diagnostic criteria, as IQ and SRS are continuous measures strongly correlated with ASD. Genome-wide mRNA, CpG methylation and miRNA were assayeds with the Illumina Hiseq 2500, HTG EdgeSeq miRNA Whole Transcriptome Assay, and Illumina EPIC/850 K array, respectively. We conducted genome-wide differential analyses of placental mRNA, miRNA, and CpG methylation data. These molecular features were then integrated for a predictive analysis of IQ and SRS outcomes using kernel aggregation regression. We lastly examined associations between ASD and the multi-omic-predicted component of IQ and SRS.
Results:
Genes with important roles in neurodevelopment and placental tissue organization were associated with intellectual and social impairment. Kernel aggregations of placental multi-omics strongly predicted intellectual and social function, explaining approximately 8% and 12% of variance in SRS and IQ scores via cross-validation, respectively. Predicted in-sample SRS and IQ showed significant positive and negative associations with ASD case-control status.
Limitations:
The ELGAN cohort comprises children born pre-term, and generalization may be affected by unmeasured confounders associated with low gestational age. We conducted external validation of predictive models, though the sample size (N = 49) and the scope of the available out-sample placental dataset are limited. Further validation of the models is merited.
Conclusions:
Aggregating information from biomarkers within and among molecular data types improves prediction of complex traits like social and intellectual ability in children born extremely preterm, suggesting that traits within the placenta-brain axis may be omnigenic.
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