Assaying Environmental Nickel Toxicity Using Model Nematodes

Although nickel exposure results in allergic reactions, respiratory conditions, and cancer in humans and rodents, the ramifications of excess nickel in the environment for animal and human health remain largely undescribed. Nickel and other cationic metals travel through waterways and bind to soils and sediments. To evaluate the potential toxic effects of nickel at environmental contaminant levels (8.9-7,600 µg Ni/g dry weight of sediment and 50-800 µg NiCl2/L of water), we conducted assays using two cosmopolitan nematodes, Caenorhabditis elegans and Pristionchus pacificus. We assayed the effects of both sediment-bound and aqueous nickel upon animal growth, developmental survival, lifespan, and fecundity. Uncontaminated sediments were collected from sites in the Midwestern United States and spiked with a range of nickel concentrations. We found that nickel-spiked sediment substantially impairs both survival from larval to adult stages and adult longevity in a concentration-dependent manner. Further, while aqueous nickel showed no adverse effects on either survivorship or longevity, we observed a significant decrease in fecundity, indicating that aqueous nickel could have a negative impact on nematode physiology. Intriguingly, C. elegans and P. pacificus exhibit similar, but not identical, responses to nickel exposure. Moreover, P. pacificus could be tested successfully in sediments inhospitable to C. elegans. Our results add to a growing body of literature documenting the impact of nickel on animal physiology, and suggest that environmental toxicological studies could gain an advantage by widening their repertoire of nematode species

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