Control of hormone-driven organ disassembly by ECM remodeling and Yorkie-dependent apoptosis

Epithelia grow and shape into functional structures during organogenesis. Although most of the focus on organogenesis has been drawn to the building of biological structures, the disassembly of pre-existing structures is also an important event to reach a functional adult organ. Examples of disassembly processes include the regression of the Müllerian or Wolffian ducts during gonad development and mammary gland involution during the post-lactational period in adult females. To date, it is unclear how organ disassembly is controlled at the cellular level. Here, we follow the Drosophila larval trachea through metamorphosis and show that its disassembly is a hormone-driven and precisely orchestrated process. It occurs in two phases: first, remodeling of the apical extracellular matrix (aECM), mediated by matrix metalloproteases and independent of the actomyosin cytoskeleton, results in a progressive shortening of the entire trachea and a nuclear-to-cytoplasmic relocalization of the Hippo effector Yorkie (Yki). Second, a decreased transcription of the Yki target, Diap1, in the posterior metameres and the activation of caspases result in the apoptotic loss of the posterior half of the trachea while the anterior half escapes cell death. Thus, our work unravels a mechanism by which hormone-driven ECM remodeling controls sequential tissue shortening and apoptotic cell removal through the transcriptional activity of Yki, leading to organ disassembly during animal development.The research leading to the results has received funding from the Spanish Ministry of Science and Innovation PID2019-109117GB-100 and PGC2018-094254-B-I00. We acknowledge the support of the CERCA Programme/Generalitat de Catalunya, the Fundaciòn Biofisika Bizkaia, and the Basque Excellence Research Centre of the Basque Government

#ESHREjc report: on the road to preconception and personalized counselling with machine learning models

Extract Introduction Artificial intelligence (AI) is a tool thought to revolutionize the field of reproductive medicine in the years to come. Specifically, machine learning (ML), which is a subset of AI methods used to detect patterns and make predictions based on large datasets, has been used in ART to predict implantation outcome, embryo transfer strategies or adverse outcomes for IVF treatments (for a review see, Wang et al., 2019). However, ML methods have not yet been widely adopted in medically assisted reproduction (MAR). This slow uptake could be due to a lack of in-depth interdisciplinary communication between ML experts and clinicians/embryologists, as well as some uncertainty on how ML models could be generalized for different populations. The February edition of the ESHRE Journal Club discussed a paper from Yland et al. (2022) where the authors compared different ML algorithms to predict the chance of pregnancy in couples actively trying to conceive (without undergoing MAR). By using data from the Pregnancy Study Online (PRESTO), a web-based prospective cohort study of 4133 couples in North America (Wise et al., 2015), the authors analysed 163 potential variables and used supervised ML classification algorithms to identify the strength of each variable in predicting pregnancy outcomes of couples across the fertility spectrum (infertile: <12 menstrual cycles; subfertile: within 6 menstrual cycles; and fecundability: the average probability of pregnancy per menstrual cycle). The authors were able to predict pregnancy with a discrimination as high as 71.2% and identify the variables that most consistently predicted conception. These variables included lifestyle and reproductive characteristics such as age, BMI, history of infertility, daily use of vitamins or folic acid, intercourse timing, diet quality and reduced stress. ESHRE Journal Club discussion focused on promoting interdisciplinarity among ML and reproductive medicine specialists. It hosted 50 participants on Twitter together with experts Michelle Perugini, Vajira Thambawita and authors Jennifer Yland, Yannis Paschalidis and Lauren Wise. The discussion resulted in 902 tweets and around 1 million impressions over a 24-h perio

#ESHREjc report: trick or treatment—evidence based use of add-ons in ART and patient perspectives

Extract The September ESHRE Journal Club discussed a paper from Lensen et al. (2021) about the prevalence and pattern of add-on use in ART. The paper was based on an online national survey in Australia for women having ART treatment over a 3-year period. Survey questions covered demographics, IVF history and the use of IVF add-ons. This survey showed that 82% of the 1590 eligible patients have used at least one add-on during treatment and usually at an additional cost (72% of cases). The majority of patients shared the decision of add-on use with their fertility specialist and placed a high level of importance on safety and efficacy based on scientific evidence. The study also highlighted a high proportion of patient regret (83%) after unsuccessful treatments and when the specialist had a larger contribution in the decision to use add-ons (75%). Due to large add-on utilization, it was interesting for Journal Club participants to discuss whether their use is supported by scientific evidence and how this evidence is disseminated. What is the regulatory framework around add-on use and what are patients’ perspectives? The ESHRE Journal Club with 45 participants, experts Raj Mathur and Christos Venetis, as well as a representative from the patient association Fertility Europe, discussed the topic on Twitter; >800k impressions were recorded over the 24-h period