Integrative analysis of noncoding mutations identifies the druggable genome in preterm birth

Area of Science:

• Genetics and genomics
• Reproductive biology
• Computational biology

Background:

• Spontaneous preterm birth (sPTB) affects approximately 10% of US pregnancies, with known familial associations but elusive genetic underpinnings.
• Identifying specific genetic loci contributing to sPTB has remained a significant challenge in reproductive medicine.

Purpose of the Study:

• To identify novel genetic factors associated with spontaneous preterm birth (sPTB) using advanced computational models.
• To investigate the role of identified genes in myometrial function and their regulation by progesterone.
• To explore the potential of identified genetic variants as predictors of treatment response to progestin prophylaxis.

Main Methods:

• Integrated deep learning and graphical models with pregnant myometrial epigenome and large-scale genomic data from diverse cohorts.
• Analyzed base-resolution mutational effects to identify sPTB-associated genes.
• Screened approximately 4000 compounds to identify therapeutic candidates targeting identified genes.

Main Results:

• Uncovered novel sPTB genes involved in myometrial muscle relaxation and inflammatory responses, regulated by progesterone receptors.
• Demonstrated that mutation burden in identified genes predicts response to progestin treatment in pregnant women.
• Experimentally validated therapeutic effects of candidate molecules on labor regulation.

Conclusions:

• The study reveals a druggable genomic landscape for preterm birth intervention.
• The integrative approach provides a generalizable framework for dissecting complex diseases with genetic components.
• Identified genes and therapeutic targets offer new avenues for preventing spontaneous preterm birth.