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Non-invasive biomarkers of fetal brain development reflecting prenatal stress: An integrative multi-scale multi-species perspective on data collection and analysis

  • Martin G. Frasch
  • , Silvia M. Lobmaier
  • , Tamara Stampalija
  • , Paula Desplats
  • , María Eugenia Pallarés
  • , Verónica Pastor
  • , Marcela A. Brocco
  • , Hau tieng Wu
  • , Jay Schulkin
  • , Christophe L. Herry
  • , Andrew J.E. Seely
  • , Gerlinde A.S. Metz
  • , Yoram Louzoun
  • , Marta C. Antonelli
  • University of Washington
  • Technical University of Munich
  • IRCCS Ospedale Infantile Burlo Garofolo - Trieste
  • University of California at San Diego
  • Universidad de Buenos Aires
  • Consejo Nacional de Investigaciones Científicas y Técnicas
  • Duke University
  • National Tsing Hua University
  • University of Ottawa
  • University of Lethbridge

Research output: Contribution to journalReview articlepeer-review

30 Scopus citations

Abstract

Prenatal stress (PS) impacts early postnatal behavioural and cognitive development. This process of ‘fetal programming’ is mediated by the effects of the prenatal experience on the developing hypothalamic–pituitary–adrenal (HPA) axis and autonomic nervous system (ANS). We derive a multi-scale multi-species approach to devising preclinical and clinical studies to identify early non-invasively available pre- and postnatal biomarkers of PS. The multiple scales include brain epigenome, metabolome, microbiome and the ANS activity gauged via an array of advanced non-invasively obtainable properties of fetal heart rate fluctuations. The proposed framework has the potential to reveal mechanistic links between maternal stress during pregnancy and changes across these physiological scales. Such biomarkers may hence be useful as early and non-invasive predictors of neurodevelopmental trajectories influenced by the PS as well as follow-up indicators of success of therapeutic interventions to correct such altered neurodevelopmental trajectories. PS studies must be conducted on multiple scales derived from concerted observations in multiple animal models and human cohorts performed in an interactive and iterative manner and deploying machine learning for data synthesis, identification and validation of the best non-invasive detection and follow-up biomarkers, a prerequisite for designing effective therapeutic interventions.

Original languageEnglish
Pages (from-to)165-183
Number of pages19
JournalNeuroscience and Biobehavioral Reviews
Volume117
DOIs
StatePublished - Oct 2020

Bibliographical note

Publisher Copyright:
© 2018 Elsevier Ltd

Keywords

  • ANS
  • Epigenetics
  • Guinea pig
  • HRV
  • Human
  • Machine learning
  • Microbiome
  • Omics
  • Rat
  • Sheep

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