# Prediction, mechanisms and causality: a systems biology approach to elucidate the role of the dynamic interplay between maternal and microbial systems in the pathobiology of perinatal depression

> **NIH NIH R01** · UNIVERSITY OF ILLINOIS AT CHICAGO · 2024 · $689,409

## Abstract

PROJECT SUMMARY
Perinatal depression (PND), defined as depression during pregnancy and up to one year postpartum, affects
more than 20% of pregnancies and disproportionately impacts Black Women and Latinas. PND increases risk
of preterm birth and infant neurodevelopment deficits. Yet we still do not fully understand the pathobiology of
PND, which limits efforts to improve its prevention, identification and treatment. Interactions between the host
and microbial communities that reside in the gut are essential for human health. Gut microorganisms play an
important role in producing beneficial metabolites, including the neurotransmitters serotonin and gamma-
aminobutyric acid (GABA). The gut microbiota bidirectionally communicate with the brain, an interaction
mediated by the neurological, immune, and endocrine systems and coined as the microbiota-gut-brain axis
(MGBA). Our initial pilot results from a longitudinal study of low-income women of color (n=42) early in pregnancy,
which showed that multiple attributes of the MGBA were associated with depressive symptom severity, including
production of short chain fatty-acids, metabolism of tryptophan and GABA, and systemic inflammation mediated
by bile acid metabolism. Although our initial data points to new MGBA signatures linked to depressive symptom
severity in the first and second trimesters, additional work is needed to determine whether these signatures and
their interactions extend beyond the second trimester. Further, microbial metabolism is driven both by the
interactions between metabolites and by the interplay between microbial metabolic systems with maternal
inflammatory system and metabolism. Thus, determining the causal influence of these new MGBA signatures
on depressive symptom severity during the perinatal period requires using approaches that can establish the
effect of systems (networks) coupling in MGBA functioning. Here, we propose a clinical translational study to
determine the role of MGBA in PND by using a systems biology framework and an experimental animal model
to assess causality. To test this, we will draw from our research infrastructure to recruit 158 women (55% Black,
30% Latina) early in pregnancy (<16 gestational weeks) and follow them bimonthly for up to 6 weeks postpartum.
We will assess mood; lifestyle (diet, physical activity, sleep); and heart-rate variability (a proxy for stress) and
will measure microbial genome and meta-metabolome and maternal blood transcriptome and metabolome. In
Aim 1, we will employ interpretable machine learning models to predict depressive symptom severity
concurrently and prospectively. In Aim 2, we will establish coupling mechanisms that regulate symptom severity
by modeling the interplay between microbial and maternal metabolic, genetic and regulatory systems using
network theory. In Aim 3, we will determine the causal role of gut microbiota in symptom severity in a female
pregnant germ-free mouse model using fecal microbiota transplant...

## Key facts

- **NIH application ID:** 10979065
- **Project number:** 1R01HD113809-01A1
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT CHICAGO
- **Principal Investigator:** Beatriz Penalver Bernabe
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $689,409
- **Award type:** 1
- **Project period:** 2024-09-16 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10979065

## Citation

> US National Institutes of Health, RePORTER application 10979065, Prediction, mechanisms and causality: a systems biology approach to elucidate the role of the dynamic interplay between maternal and microbial systems in the pathobiology of perinatal depression (1R01HD113809-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10979065. Licensed CC0.

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