%0 Journal Article %1 stelzer2021integrated %A Stelzer, Ina A. %A Ghaemi, Mohammad S. %A Han, Xiaoyuan %A Ando, Kazuo %A Hedou, Julien J. %A Feyaerts, Dorien %A Peterson, Laura S. %A Rumer, Kristen K. %A Tsai, Eileen S. %A Ganio, Edward A. %A Gaudilliere, Dyani K. %A Tsai, Amy S. %A Choisy, Benjamin %A Gaigne, Lea P. %A Verdonk, Franck %A Jacobsen, Danielle %A Gavasso, Sonia %A Traber, Gavin M. %A Ellenberger, Mathew %A Stanley, Natalie %A Becker, Martin %A Culos, Anthony %A Fallahzadeh, Ramin %A Wong, Ronald J. %A Darmstadt, Gary L. %A Druzin, Maurice L. %A Winn, Virginia D. %A Gibbs, Ronald S. %A Ling, Xuefeng B. %A Sylvester, Karl %A Carvalho, Brendan %A Snyder, Michael P. %A Shaw, Gary M. %A Stevenson, David K. %A Contrepois, Kevin %A Angst, Martin S. %A Aghaeepour, Nima %A Gaudilliere, Brice %D 2021 %I American Association for the Advancement of Science %J Science Translational Medicine %K data labor learning machine mining multiomics myown of onset paper:cordis pregnancy project:bmbf %N 592 %R 10.1126/scitranslmed.abd9898 %T Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset %U https://stm.sciencemag.org/content/13/592/eabd9898 %V 13 %X Current methods to predict spontaneous labor are fairly inaccurate. To provide better estimates and biomarkers of labor onset, the biological processes that lead up to labor need to be better understood. Stelzer et al. performed metabolome, proteome, and immunome studies on blood samples from 63 women in the 100 days before delivery. They identified a surge in IL-1R4 and steroid hormones in the weeks before delivery, which was coordinated with a switch from immune activation to regulation of inflammatory responses. A model was then constructed to predict time to labor independent of gestational age. These results may be helpful for development of more accurate methods to predict labor.Estimating the time of delivery is of high clinical importance because pre- and postterm deviations are associated with complications for the mother and her offspring. However, current estimations are inaccurate. As pregnancy progresses toward labor, major transitions occur in fetomaternal immune, metabolic, and endocrine systems that culminate in birth. The comprehensive characterization of maternal biology that precedes labor is key to understanding these physiological transitions and identifying predictive biomarkers of delivery. Here, a longitudinal study was conducted in 63 women who went into labor spontaneously. More than 7000 plasma analytes and peripheral immune cell responses were analyzed using untargeted mass spectrometry, aptamer-based proteomic technology, and single-cell mass cytometry in serial blood samples collected during the last 100 days of pregnancy. The high-dimensional dataset was integrated into a multiomic model that predicted the time to spontaneous labor R = 0.85, 95\% confidence interval (CI) 0.79 to 0.89, P = 1.2 × 10-40, N = 53, training set; R = 0.81, 95\% CI 0.61 to 0.91, P = 3.9 × 10-7, N = 10, independent test set. Coordinated alterations in maternal metabolome, proteome, and immunome marked a molecular shift from pregnancy maintenance to prelabor biology 2 to 4 weeks before delivery. A surge in steroid hormone metabolites and interleukin-1 receptor type 4 that preceded labor coincided with a switch from immune activation to regulation of inflammatory responses. Our study lays the groundwork for developing blood-based methods for predicting the day of labor, anchored in mechanisms shared in preterm and term pregnancies.

@article{stelzer2021integrated, abstract = {Current methods to predict spontaneous labor are fairly inaccurate. To provide better estimates and biomarkers of labor onset, the biological processes that lead up to labor need to be better understood. Stelzer et al. performed metabolome, proteome, and immunome studies on blood samples from 63 women in the 100 days before delivery. They identified a surge in IL-1R4 and steroid hormones in the weeks before delivery, which was coordinated with a switch from immune activation to regulation of inflammatory responses. A model was then constructed to predict time to labor independent of gestational age. These results may be helpful for development of more accurate methods to predict labor.Estimating the time of delivery is of high clinical importance because pre- and postterm deviations are associated with complications for the mother and her offspring. However, current estimations are inaccurate. As pregnancy progresses toward labor, major transitions occur in fetomaternal immune, metabolic, and endocrine systems that culminate in birth. The comprehensive characterization of maternal biology that precedes labor is key to understanding these physiological transitions and identifying predictive biomarkers of delivery. Here, a longitudinal study was conducted in 63 women who went into labor spontaneously. More than 7000 plasma analytes and peripheral immune cell responses were analyzed using untargeted mass spectrometry, aptamer-based proteomic technology, and single-cell mass cytometry in serial blood samples collected during the last 100 days of pregnancy. The high-dimensional dataset was integrated into a multiomic model that predicted the time to spontaneous labor [R = 0.85, 95\% confidence interval (CI) [0.79 to 0.89], P = 1.2 {\texttimes} 10-40, N = 53, training set; R = 0.81, 95\% CI [0.61 to 0.91], P = 3.9 {\texttimes} 10-7, N = 10, independent test set]. Coordinated alterations in maternal metabolome, proteome, and immunome marked a molecular shift from pregnancy maintenance to prelabor biology 2 to 4 weeks before delivery. A surge in steroid hormone metabolites and interleukin-1 receptor type 4 that preceded labor coincided with a switch from immune activation to regulation of inflammatory responses. Our study lays the groundwork for developing blood-based methods for predicting the day of labor, anchored in mechanisms shared in preterm and term pregnancies.}, added-at = {2021-06-20T19:47:25.000+0200}, author = {Stelzer, Ina A. and Ghaemi, Mohammad S. and Han, Xiaoyuan and Ando, Kazuo and Hedou, Julien J. and Feyaerts, Dorien and Peterson, Laura S. and Rumer, Kristen K. and Tsai, Eileen S. and Ganio, Edward A. and Gaudilliere, Dyani K. and Tsai, Amy S. and Choisy, Benjamin and Gaigne, Lea P. and Verdonk, Franck and Jacobsen, Danielle and Gavasso, Sonia and Traber, Gavin M. and Ellenberger, Mathew and Stanley, Natalie and Becker, Martin and Culos, Anthony and Fallahzadeh, Ramin and Wong, Ronald J. and Darmstadt, Gary L. and Druzin, Maurice L. and Winn, Virginia D. and Gibbs, Ronald S. and Ling, Xuefeng B. and Sylvester, Karl and Carvalho, Brendan and Snyder, Michael P. and Shaw, Gary M. and Stevenson, David K. and Contrepois, Kevin and Angst, Martin S. and Aghaeepour, Nima and Gaudilliere, Brice}, biburl = {https://www.bibsonomy.org/bibtex/2aef69a352b237b0beac5adea807f8c2f/becker}, description = {impactfactor = {17.956}, impactfactor-year = 2021, impactfactor-source = {https://academic-accelerator.com/Impact-of-Journal/Science-Translational-Medicine}}, doi = {10.1126/scitranslmed.abd9898}, elocation-id = {eabd9898}, eprint = {https://stm.sciencemag.org/content/13/592/eabd9898.full.pdf}, interhash = {b1da0d5f33ae4766e20917e700ea5db7}, intrahash = {aef69a352b237b0beac5adea807f8c2f}, issn = {1946-6234}, journal = {Science Translational Medicine}, keywords = {data labor learning machine mining multiomics myown of onset paper:cordis pregnancy project:bmbf}, number = 592, publisher = {American Association for the Advancement of Science}, timestamp = {2022-03-17T02:58:40.000+0100}, title = {Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset}, url = {https://stm.sciencemag.org/content/13/592/eabd9898}, volume = 13, year = 2021 }