Author ORCID Identifier
Document Type
Article
Publication Date
5-27-2019
Abstract
The process of parturition involves the transformation of the quiescent myometrium (uterine smooth muscle) to the highly contractile laboring state. This is thought to be driven by changes in gene expression in myometrial cells. Despite the existence of multiple myometrial gene expression studies, the transcriptional programs that initiate labor are not known. Here, we integrated three transcriptome datasets, one novel (NCBI Gene Expression Ominibus: GSE80172) and two existing, to characterize the gene expression changes in myometrium associated with the onset of labor at term. Computational analyses including classification, singular value decomposition, pathway enrichment, and network inference were applied to individual and combined datasets. Outcomes across studies were integrated with multiple protein and pathway databases to build a myometrial parturition signaling network. A high-confidence (significant across all studies) set of 126 labor genes were identified and machine learning models exhibited high reproducibility between studies. Labor signatures included both known (interleukins, cytokines) and unknown (apoptosis, MYC, cell proliferation/differentiation) pathways while cyclic AMP signaling and muscle relaxation were associated with non-labor. These signatures accurately classified and characterized the stages of labor. The data-derived parturition signaling networks provide new genes/signaling interactions to understand phenotype-specific processes and aid in future studies of parturition.
Keywords
parturition, myometrium, gene expression networks, classification, inflammation
Publication Title
Frontiers in Genetics
Rights
© 2019 Stanfield, Lai, Lei, Johnson, Blanks, Romero, Chance, Mesiano and Koyutürk. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Stanfield Z, Lai PF, Lei K, Johnson MR, Blanks AM, Romero R, Chance MR, Mesiano S and Koyutürk M (2019) Myometrial Transcriptional Signatures of Human Parturition. Front. Genet. 10:185. doi: 10.3389/fgene.2019.00185
Manuscript Version
Final Publisher Version
Comments
A Corrigendum on Myometrial Transcriptional Signatures of Human Parturition by Stanfield, Z., Lai, P. F., Lei, K., Johnson, M. R., Blanks, A. M., Romero, R., et al. (2019). Front. Genet. 10:185. doi: 10.3389/fgene.2019.00185. “Pei F. Lai” and “Kaiyu Lei” were not included as authors in the published article. Due to the addition of authors, the list of affiliations had been updated accordingly. The corrected Author Contributions Statement appears below. Acknowledgments “Conception, design, and execution of the RNA-seq experiment was undertaken by PL, KL and MJ. ZS performed all data analysis, which was conceived by ZS, SM, and MK. MK supervised application of computational methods. SM supervised biological interpretation and conceptualization of results. ZS drafted the manuscript. MK and SM edited and commented on earlier drafts. All authors read and approved the final manuscript.” The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.