Differential gene expression during placentation in pregnancies conceived with different fertility treatments compared with spontaneous pregnancies Bora Lee, Ph.D.,a Alex F. Koeppel, Ph.D.,b Erica T. Wang, M.D., M.A.S.,a,c Tania L. Gonzalez, Ph.D.,a Tianyanxin Sun, Ph.D.,a Lindsay Kroener, M.D.,c Yayu Lin, M.S.,a Nikhil V. Joshi, M.D.,a,c Tejal Ghadiali, B.S.,a Stephen D. Turner, Ph.D.,b Stephen S. Rich, Ph.D.,b Charles R. Farber, Ph.D.,b Jerome I. Rotter, M.D.,d Yii-Der Ida Chen, Ph.D.,d Mark O. Goodarzi, M.D., Ph.D.,e Seth Guller, Ph.D.,f Bryna Harwood, M.D.,g Tania B. Serna, M.D.,g John Williams III, M.D.,c,h and Margareta D. Pisarska, M.D.a,c a Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, California; b Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia; c Department of Obstetrics and Gynecology, University of California, Los Angeles (UCLA) David Geffen School of Medicine, Los Angeles, California; d LABiomed/Harbor-UCLA Medical Center, Torrance, California; e Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California; f Department of Obstetrics/Gynecology and Reproductive Sciences, Yale University School of Medicine, New Haven, Connecticut; g Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, California; and h Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, California Objective: To identify differences in the transcriptomic profiles during placentation from pregnancies conceived spontaneously vs. those with infertility using non-in vitro fertilization (IVF) fertility treatment (NIFT) or IVF. Design: Cohort study. Setting: Academic medical center. Patient(s): Women undergoing chorionic villus sampling at gestational age 11–13 weeks (n ¼ 141), with pregnancies that were conceived spontaneously (n ¼ 74), with NIFT (n ¼ 33), or with IVF (n ¼ 34), resulting in the delivery of viable offspring. Intervention(s): Collection of chorionic villus samples from women who conceived spontaneously, with NIFT, or with IVF for gene expression analysis using RNA sequencing. Main Outcome Measure(s): Baseline maternal, paternal, and fetal demographics, maternal medical conditions, pregnancy complica- tions, and outcomes. Differential gene expression of first-trimester placenta. Result(s): There were few differences in the transcriptome of first-trimester placenta from NIFT, IVF, and spontaneous pregnancies. There was one protein-coding differentially expressed gene (DEG) between the spontaneous and infertility groups, CACNA1I, one protein-coding DEG between the spontaneous and IVF groups, CACNA1I, and five protein-coding DEGs between the NIFT and IVF groups, SLC18A2, CCL21, FXYD2, PAEP, and DNER. Conclusion(s): This is the first and largest study looking at transcriptomic profiles of first-trimester placenta demonstrating similar transcriptomic profiles in pregnancies conceived using NIFT or IVF and spontaneous conceptions. Gene expression differences found to be highest in the NIFT group suggest that the underlying infertility, in addition to treatment-related factors, may Received May 30, 2018; revised November 3, 2018; accepted November 5, 2018; published online January 2, 2019. J.I.R. reports grants from the National Institutes of Health, during the conduct of the study. J.W. reports personal fees from Natera, Inc. outside the sub- mitted work. B.L. has nothing to disclose. A.F.K. has nothing to disclose. E.T.W. has nothing to disclose. T.L.G. has nothing to disclose. T.S. has nothing to disclose. L.K. has nothing to disclose. Y.L. has nothing to disclose. N.V.J. has nothing to disclose. T.G. has nothing to disclose. S.D.T. has nothing to disclose. S.S.R. has nothing to disclose. C.R.F. has nothing to disclose. Y.-D.I.C. has nothing to disclose. M.O.G. has nothing to disclose. S.G. has nothing to disclose. B.H. has nothing to disclose. T.B.S. has nothing to disclose. M.D.P. has nothing to disclose. Supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health under award number R01HD074368. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National In- stitutes of Health. Presented at the 65th Annual Meeting of the Society for Reproductive Investigation, San Diego, CA, March 6–10, 2018. Reprint requests: Margareta D. Pisarska, M.D., Cedars-Sinai Medical Center, Department of Obstetrics and Gynecology, Division of Reproductive Endocri- nology and Infertility, Los Angeles, California 90048 (E-mail: [email protected]). Fertility and Sterility® Vol. 111, No. 3, March 2019 0015-0282/$36.00 Copyright ©2018 American Society for Reproductive Medicine, Published by Elsevier Inc. https://doi.org/10.1016/j.fertnstert.2018.11.005 VOL. 111 NO. 3 / MARCH 2019 535 ORIGINAL ARTICLE: GENETICS contribute to the observed gene expression profiles. (Fertil SterilÒ 2019;111:535–46. Ó2018 by American Society for Reproductive Medicine.) El resumen está disponible en Español al final del artículo. Key Words: Non-IVF fertility treatment (NIFT), in vitro fertilization (IVF), placentation, RNA sequencing, transcriptome Discuss: You can discuss this article with its authors and other readers at https://www.fertstertdialog.com/users/16110-fertility- and-sterility/posts/40658-26402 nfertility affects approximately 6.1 million people in the couples with infertility—that are in the late first trimester of United States, equivalent to 10% of the reproductive-age pop- pregnancy at the time of chorionic villus sampling (CVS) I ulation (1). The use of assisted reproductive technology (ART), and followed until delivery. Non-IVF fertility treatment was including in vitro fertilization (IVF), contributes to 1.5% of live defined as treatment using either medications for ovulation births in the United States, and other non-IVF fertility treatments induction or controlled ovarian stimulation and intrauterine (NIFTs) contribute to 4.6% (2, 3). Adverse pregnancy outcomes, insemination (IUI). IVF pregnancies included fresh or frozen including low birth weight and small for gestational age babies, embryo transfers using either cleavage-stage or blastocyst- pre-eclampsia, retained placenta, placental abruption, placenta stage embryos. Our SMAART Study cohort consisted of 409 previa, preterm labor and delivery, and birth defects, have been singleton pregnancies, of which 208 were spontaneous con- associated with ART compared with pregnancies conceived spon- ceptions and 201 pregnancies conceived with a history of taneously (2, 4–11).However,itisunclearwhethertheseadverse infertility. Of the infertility group, 90 were conceived with outcomes are the result of the ART procedures, such as IVF, or the NIFT and 111 were conceived with IVF. All pregnancies had underlying infertility, because pregnancies conceived by couples a normal karyotype and delivered (20). utilizing other types of fertility treatments, NIFT, are also at Transcriptomic profiling was performed on a subset of increased risk of adverse outcomes, including placental 141 subjects who had chorionic villi available from the first abruption, fetal loss, and gestational diabetes (12).Furthermore, trimester. The SMAART Transcriptome cohort consisted of pregnancies conceived in couples with infertility regardless of 74 spontaneous conceptions and 67 pregnancies conceived treatment are at increased risk of adverse outcomes, including with a history of infertility. Of the infertility group, 33 were placenta accreta (13), earlier gestational age at delivery, late conceived with NIFT and 34 were conceived with IVF. preterm birth, and greater neonatal intensive care unit – admissions (11, 14), as well as birth defects (15 17). Demographics Statistical Analysis Because many of these adverse outcomes are related to t placentation, a better understanding of placental function A -test and analysis of variance were used for the baseline in pregnancies conceived in couples with infertility, utilizing and pregnancy outcome demographics. fertility treatments, may uncover the underlying pathology leading to adverse outcomes. CVS Collection Previous studies examining the transcriptome of human Chorionic villus samples were collected at 11–13 weeks' placenta have been limited to term placentas with specific gestation at the Cedars-Sinai Prenatal Diagnostic Center, as placenta pathologies, predominantly pre-eclampsia (18). Yet previously described (21, 22). Leftover tissue after clinical it is during the first trimester of a pregnancy, during placenta- genetic testing was collected from consenting patients per tion, when trophoblast proliferation, differentiation, and inva- institutional review board–approved protocol and placed in sion, as well as angiogenesis and vasculogenesis, crucial for RNAlater RNA Stabilization Reagent (Qiagen) and stored at laying the groundwork for successful placental function, are À80C in the Cedars-Sinai Prenatal Biorepository until taking place; this is a necessary time point to study placental processing. development (19). It is also the closest time point to conception, either spontaneously or through fertility treatments, that can RNA Extraction for RNA-Sequencing be studied to minimize placental changes due to placental pa- thology and not the mode of conception. This is the first and Chorionic