Cell Reports Medicine (Jun 2021)
Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth
- Adi L. Tarca,
- Bálint Ármin Pataki,
- Roberto Romero,
- Marina Sirota,
- Yuanfang Guan,
- Rintu Kutum,
- Nardhy Gomez-Lopez,
- Bogdan Done,
- Gaurav Bhatti,
- Thomas Yu,
- Gaia Andreoletti,
- Tinnakorn Chaiworapongsa,
- Sonia S. Hassan,
- Chaur-Dong Hsu,
- Nima Aghaeepour,
- Gustavo Stolovitzky,
- Istvan Csabai,
- James C. Costello
Affiliations
- Adi L. Tarca
- Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health and Human Services, Detroit, MI 48201, USA; Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI 48201 USA; Department of Computer Science, Wayne State University College of Engineering, Detroit, MI 48202, USA; Corresponding author
- Bálint Ármin Pataki
- Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Budapest, Hungary
- Roberto Romero
- Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health and Human Services, Detroit, MI 48201, USA; Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA; Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI 48201, USA; Detroit Medical Center, Detroit, MI 48201, USA; Department of Obstetrics and Gynecology, Florida International University, Miami, FL 33199, USA; Corresponding author
- Marina Sirota
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA 94143, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Rintu Kutum
- Informatics and Big Data Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
- Nardhy Gomez-Lopez
- Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health and Human Services, Detroit, MI 48201, USA; Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI 48201 USA; Department of Biochemistry, Microbiology, and Immunology, Wayne State University School of Medicine, Detroit, MI 48201 USA
- Bogdan Done
- Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health and Human Services, Detroit, MI 48201, USA; Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI 48201 USA
- Gaurav Bhatti
- Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health and Human Services, Detroit, MI 48201, USA; Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI 48201 USA
- Thomas Yu
- Sage Bionetworks, Seattle, WA, USA
- Gaia Andreoletti
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA 94143, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Tinnakorn Chaiworapongsa
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI 48201 USA
- Sonia S. Hassan
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI 48201 USA; Office of Women’s Health, Integrative Biosciences Center, Wayne State University, Detroit, MI 48202, USA; Department of Physiology, Wayne State University School of Medicine, Detroit, MI 48201, USA
- Chaur-Dong Hsu
- Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health and Human Services, Detroit, MI 48201, USA; Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI 48201 USA; Department of Physiology, Wayne State University School of Medicine, Detroit, MI 48201, USA
- Nima Aghaeepour
- Department of Anesthesiology, Perioperative, and Pain Medicine, Department of Pediatrics, and Department of Biomedical Data Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
- Gustavo Stolovitzky
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA; Corresponding author
- Istvan Csabai
- Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Budapest, Hungary
- James C. Costello
- Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Corresponding author
- Journal volume & issue
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Vol. 2,
no. 6
p. 100323
Abstract
Summary: Identification of pregnancies at risk of preterm birth (PTB), the leading cause of newborn deaths, remains challenging given the syndromic nature of the disease. We report a longitudinal multi-omics study coupled with a DREAM challenge to develop predictive models of PTB. The findings indicate that whole-blood gene expression predicts ultrasound-based gestational ages in normal and complicated pregnancies (r = 0.83) and, using data collected before 37 weeks of gestation, also predicts the delivery date in both normal pregnancies (r = 0.86) and those with spontaneous preterm birth (r = 0.75). Based on samples collected before 33 weeks in asymptomatic women, our analysis suggests that expression changes preceding preterm prelabor rupture of the membranes are consistent across time points and cohorts and involve leukocyte-mediated immunity. Models built from plasma proteomic data predict spontaneous preterm delivery with intact membranes with higher accuracy and earlier in pregnancy than transcriptomic models (AUROC = 0.76 versus AUROC = 0.6 at 27–33 weeks of gestation).
