Fetus-derived DLK1 is required for maternal metabolic adaptations to pregnancy and is associated with fetal growth restriction.

Pregnancy is a state of high metabolic demand. Fasting diverts metabolism to fatty acid oxidation, and the fasted response occurs much more rapidly in pregnant women than in non-pregnant women. The product of the imprinted DLK1 gene (delta-like homolog 1) is an endocrine signaling molecule that reaches a high concentration in the maternal circulation during late pregnancy. By using mouse models with deleted Dlk1, we show that the fetus is the source of maternal circulating DLK1. In the absence of fetally derived DLK1, the maternal fasting response is impaired. Furthermore, we found that maternal circulating DLK1 levels predict embryonic mass in mice and can differentiate healthy small-for-gestational-age (SGA) infants from pathologically small infants in a human cohort. Therefore, measurement of DLK1 concentration in maternal blood may be a valuable method for diagnosing human disorders associated with impaired DLK1 expression and to predict poor intrauterine growth and complications of pregnancy.M.A.M.C. was supported by a PhD studentship from the Cambridge Centre for Trophoblast Research. Research was supported by grants from the MRC (MR/J001597/1 and MR/L002345/1), the Medical College of Saint Bartholomew's Hospital Trust, a Wellcome Trust Investigator Award, EpigeneSys (FP7 Health-257082), EpiHealth (FP7 Health-278414), a Herchel Smith Fellowship (N.T.) and NIH grant RO1 DK89989. The contents are the authors' sole responsibility and do not necessarily represent official NIH views. We thank G. Burton for invaluable support, and M. Constância and I. Sandovici (University of Cambridge) for the Meox2-cre mice. We are extremely grateful to all of the participants in the Pregnancy Outcome Prediction study. This work was supported by the NIHR Cambridge Comprehensive Biomedical Research Centre (Women's Health theme) and project grants from the MRC (G1100221) and Sands (Stillbirth and Neonatal Death Charity). The study was also supported by GE Healthcare (donation of two Voluson i ultrasound systems for this study) and by the NIHR Cambridge Clinical Research Facility, where all research visits took place.This is the author accepted manuscript. The final version is available from Nature Publishing Group via https://doi.org/10.1038/ng.369

Integrating Diverse Datasets Improves Developmental Enhancer Prediction

Gene-regulatory enhancers have been identified using various approaches, including evolutionary conservation, regulatory protein binding, chromatin modifications, and DNA sequence motifs. To integrate these different approaches, we developed EnhancerFinder, a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity. EnhancerFinder uses a multiple kernel learning approach to integrate DNA sequence motifs, evolutionary patterns, and diverse functional genomics datasets from a variety of cell types. In contrast with prediction approaches that define enhancers based on histone marks or p300 sites from a single cell line, we trained EnhancerFinder on hundreds of experimentally verified human developmental enhancers from the VISTA Enhancer Browser. We comprehensively evaluated EnhancerFinder using cross validation and found that our integrative method improves the identification of enhancers over approaches that consider a single type of data, such as sequence motifs, evolutionary conservation, or the binding of enhancer-associated proteins. We find that VISTA enhancers active in embryonic heart are easier to identify than enhancers active in several other embryonic tissues, likely due to their uniquely high GC content. We applied EnhancerFinder to the entire human genome and predicted 84,301 developmental enhancers and their tissue specificity. These predictions provide specific functional annotations for large amounts of human non-coding DNA, and are significantly enriched near genes with annotated roles in their predicted tissues and lead SNPs from genome-wide association studies. We demonstrate the utility of EnhancerFinder predictions through in vivo validation of novel embryonic gene regulatory enhancers from three developmental transcription factor loci. Our genome-wide developmental enhancer predictions are freely available as a UCSC Genome Browser track, which we hope will enable researchers to further investigate questions in developmental biology. © 2014 Erwin et al

Urinary peptide profiling identifies a panel of putative biomarkers for diagnosing and staging endometriosis.

OBJECTIVE: To identify a potential diagnostic endometriosis marker using matrix-enhanced laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS)-based urinary proteomics. DESIGN: Prospective randomized pilot study. SETTING: University hospital, tertiary referral center for endometriosis. PATIENT(S): 53 women undergoing laparoscopic surgery for pain and/or infertility comprising 30 women without endometriosis and 23 with endometriosis. INTERVENTION(S): Laparoscopy and urine specimens. MAIN OUTCOME MEASURE(S): Urinary peptide profiles. RESULT(S): We observed distinct patterns of peptide profiles in the urine samples of women presenting with typical clinical symptoms of endometriosis. Six statistically significant putative peptide markers were identified (four during the periovulatory phase and two during the luteal phase) by comparing controls with moderate/severe endometriosis patients. The periovulatory peptide mass of 1,767.1 Da and the luteal peptide mass of 1,824.3 Da both showed a sensitivity of 75% and a specificity of 85% and 71%, respectively. Also detected were seven peptide markers (two during the periovulatory phase and five during the luteal phase) by comparing the urinary peptide profiles of patients with minimal/mild to moderate/severe endometriosis. The periovulatory peptide mass of 3,280.9 Da and the luteal peptide mass of 1,933.8 Da showed a sensitivity of 82% and 75% and a specificity of 88% and 75%, respectively. CONCLUSION(S): Urinary proteomic analysis may provide a novel method of diagnosing and staging endometriosis