Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; \u3c37 \u3eweeks) or (2) early preterm birth (ePTB; \u3c32 \u3eweeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth

Transcriptional changes through menstrual cycle reveal a global transcriptional derepression underlying the molecular mechanism involved in the window of implantation

The human endometrium is a dynamic tissue that only is receptive to host the embryo during a brief time in the middle secretory phase, called the window of implantation (WOI). Despite its importance, regulation of the menstrual cycle remains incompletely understood. The aim of this study was to characterize the gene cooperation and regulation of menstrual cycle progression, to dissect the molecular complexity underlying acquisition of endometrial receptivity for a successful pregnancy, and to provide the scientific community with detailed gene co-expression information throughout the menstrual cycle on a user-friendly web-tool database. A retrospective gene co-expression analysis was performed based on the endometrial receptivity array (ERarray) gene signature from 523 human endometrial samples collected across the menstrual cycle, including during the WOI. Gene co-expression analysis revealed the WOI as having the significantly smallest proportion of negative correlations for transcriptional profiles associated with successful pregnancies compared to other cycle stages, pointing to a global transcriptional derepression being involved in acquisition of endometrial receptivity. Regulation was greatest during the transition between proliferative and secretory endometrial phases. Further, we prioritized nuclear hormone receptors as major regulators of this derepression and proved that some genes and transcription factors involved in this process were dysregulated in patients with recurrent implantation failure. We also compiled the wealth of gene co-expression data to stimulate hypothesis-driven single-molecule endometrial studies in a user-friendly database: Menstrual Cycle Gene Co-expression Network (www.menstrualcyclegcn.com). This study revealed a global transcriptional repression across the menstrual cycle, which relaxes when the WOI opens for transcriptional profiles associated with successful pregnancies. These findings suggest that a global transcriptional derepression is needed for embryo implantation and early development.Research supported by the Instituto de Salud Carlos III through Health Research Project (PI19/00537) and Miguel Servet program (CP20/00118) granted to Patricia Diaz-Gimeno (Spanish Government) cofunded by FEDER; and by the IVI-RMA IVI Foundation (1401-FIVI-002-CS). Almudena Devesa Peiro (FPU/15/01398) and Antonio Parraga Leo (FPU18/01777) are granted by the predoctoral program fellowship from the Ministry of Science, Innovation and Universities (Spanish Government).Peer reviewe

Deciphering a shared transcriptomic regulation and the relative contribution of each regulator type through endometrial gene expression signatures

Abstract Backgorund While various endometrial biomarkers have been characterized at the transcriptomic and functional level, there is generally a poor overlap among studies, making it unclear to what extent their upstream regulators (e.g., ovarian hormones, transcription factors (TFs) and microRNAs (miRNAs)) realistically contribute to menstrual cycle progression and function. Unmasking the intricacies of the molecular interactions in the endometrium from a novel systemic point of view will help gain a more accurate perspective of endometrial regulation and a better explanation the molecular etiology of endometrial-factor infertility. Methods An in-silico analysis was carried out to identify which regulators consistently target the gene biomarkers proposed in studies related to endometrial progression and implantation failure (19 gene lists/signatures were included). The roles of these regulators, and of genes related to progesterone and estrogens, were then analysed in transcriptomic datasets compiled from samples collected throughout the menstrual cycle (n = 129), and the expression of selected TFs were prospectively validated in an independent cohort of healthy participants (n = 19). Results A total of 3,608 distinct genes from the 19 gene lists were associated with endometrial progression and implantation failure. The lists’ regulation was significantly favoured by TFs (89% (17/19) of gene lists) and progesterone (47% (8 /19) of gene lists), rather than miRNAs (5% (1/19) of gene lists) or estrogen (0% (0/19) of gene lists), respectively (FDR < 0.05). Exceptionally, two gene lists that were previously associated with implantation failure and unexplained infertility were less hormone-dependent, but primarily regulated by estrogen. Although endometrial progression genes were mainly targeted by hormones rather than non-hormonal contributors (odds ratio = 91.94, FDR < 0.05), we identified 311 TFs and 595 miRNAs not previously associated with ovarian hormones. We highlight CTCF, GATA6, hsa-miR-15a-5p, hsa-miR-218-5p, hsa-miR-107, hsa-miR-103a-3p, and hsa-miR-128-3p, as overlapping novel master regulators of endometrial function. The gene expression changes of selected regulators throughout the menstrual cycle (FDR < 0.05), dually validated in-silico and through endometrial biopsies, corroborated their potential regulatory roles in the endometrium. Conclusions This study revealed novel hormonal and non-hormonal regulators and their relative contributions to endometrial progression and pathology, providing new leads for the potential causes of endometrial-factor infertility

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; &lt;37&nbsp;weeks) or (2) early preterm birth (ePTB; &lt;32&nbsp;weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth