The RNA–Methyltransferase Misu (NSun2) Poises Epidermal Stem Cells to Differentiate

Homeostasis of most adult tissues is maintained by balancing stem cell self-renewal and differentiation, but whether post-transcriptional mechanisms can regulate this process is unknown. Here, we identify that an RNA methyltransferase (Misu/Nsun2) is required to balance stem cell self-renewal and differentiation in skin. In the epidermis, this methyltransferase is found in a defined sub-population of hair follicle stem cells poised to undergo lineage commitment, and its depletion results in enhanced quiescence and aberrant stem cell differentiation. Our results reveal that post-transcriptional RNA methylation can play a previously unappreciated role in controlling stem cell fate

The nucleolar RNA methyltransferase Misu (NSun2) is required for mitotic spindle stability

Myc-induced SUN domain–containing protein (Misu or NSun2) is a nucleolar RNA methyltransferase important for c-Myc–induced proliferation in skin, but the mechanisms by which Misu contributes to cell cycle progression are unknown. In this study, we demonstrate that Misu translocates from the nucleoli in interphase to the spindle in mitosis as an RNA–protein complex that includes 18S ribosomal RNA. Functionally, depletion of Misu caused multiple mitotic defects, including formation of unstructured spindles, multipolar spindles, and chromosome missegregation, leading to aneuploidy and cell death. The presence of both RNA and Misu is required for correct spindle assembly, and this process is independent of active translation. Misu might mediate its function at the spindle by recruiting nucleolar and spindle-associated protein (NuSAP), an essential microtubule-stabilizing and bundling protein. We further identify NuSAP as a novel direct target gene of c-Myc. Collectively, our results suggest a novel mechanism by which c-Myc promotes proliferation by stabilizing the mitotic spindle in fast-dividing cells via Misu and NuSAP

Food allergy in Poland as compared to other European countries – results of the EuroPrevall project

Alergia pokarmowa to znaczący problem medyczny i społeczny. Pomimo licznych publikacji naukowych brakuje pełnych danych epidemiologicznych. Celem dostarczenia dobrej jakości danych z krajów europejskich powstał projekt EuroPrevall – wieloośrodkowe międzynarodowe badanie kohortowe. Badanie dotyczyło epidemiologii, metod diagnostycznych, fenotypów alergii, zagadnień socjalno-ekonomicznych. Wyniki badań opublikowano w szeregu publikacji oryginalnych, a celem tej pracy jest podsumowanie doniesień naukowych w nich zawartych, a także porównanie charakterystyki alergii pokarmowej w Polsce względem alergii pokarmowej w innych krajach europejskich. Podsumowano doniesienia na temat alergii na wybrane grupy pokarmów: mleko krowie, jajo kurze, orzechy ziemne, orzechy laskowe, kiwi.Food allergy is a significant medical and social problem. Despite numerous scientific publications, complete epidemiological data are lacking. In order to provide good quality data from European countries, the EuroPrevall project - a multicentre international cohort study - was established. The study concerned epidemiology, diagnostic methods, allergy phenotypes, and socio-economic issues. The results of the research have been published in a number of original publications, and the aim of this work is to summarize the scientific reports contained therein and to compare the characteristics of food allergy in Poland against other European countries. Reports on allergies to selected following food groups were summarized: cow’s milk, hen’s egg, peanuts, hazelnuts, kiw

insideOutside: an accessible algorithm for classifying interior and exterior points, with applications in embryology

ABSTRACT A crucial aspect of embryology is relating the position of individual cells to the broader geometry of the embryo. A classic example of this is the first cell-fate decision of the mouse embryo, where interior cells become inner cell mass and exterior cells become trophectoderm. Fluorescent labelling, imaging, and quantification of tissue-specific proteins have advanced our understanding of this dynamic process. However, instances arise where these markers are either not available, or not reliable, and we are left only with the cells’ spatial locations. Therefore, a simple, robust method for classifying interior and exterior cells of an embryo using spatial information is required. Here, we describe a simple mathematical framework and an unsupervised machine learning approach, termed insideOutside, for classifying interior and exterior points of a three-dimensional point-cloud, a common output from imaged cells within the early mouse embryo. We benchmark our method against other published methods to demonstrate that it yields greater accuracy in classification of nuclei from the pre-implantation mouse embryos and greater accuracy when challenged with local surface concavities. We have made MATLAB and Python implementations of the method freely available. This method should prove useful for embryology, with broader applications to similar data arising in the life sciences.</jats:p

insideOutside: an accessible algorithm for classifying interior and exterior points, with applications in embryology

Peer reviewed: TrueAcknowledgements: The authors thank the members of the Fletcher and Nichols groups for their helpful feedback in the preparation of the manuscript, especially Ian Groves and Lawrence Bates. Collaboration between the Fletcher and Nichols groups was made possible through a Company of Biologists Travelling Fellowship awarded to S.E.S. (DEVTF-180513). S.E.S. also acknowledges a Sir Henry Wellcome Postdoctoral Fellowship (224070/Z/21/Z). A.G.F. acknowledges support from the Biotechnology and Biological Sciences Research Council (BB/V018647/1 and BB/R016925/1).Funder: Company of Biologists; doi: http://dx.doi.org/10.13039/501100000522Funder: University of Cambridge; doi: http://dx.doi.org/10.13039/501100000735A crucial aspect of embryology is relating the position of individual cells to the broader geometry of the embryo. A classic example of this is the first cell-fate decision of the mouse embryo, where interior cells become inner cell mass and exterior cells become trophectoderm. Fluorescent labelling, imaging, and quantification of tissue-specific proteins have advanced our understanding of this dynamic process. However, instances arise where these markers are either not available, or not reliable, and we are left only with the cells’ spatial locations. Therefore, a simple, robust method for classifying interior and exterior cells of an embryo using spatial information is required. Here, we describe a simple mathematical framework and an unsupervised machine learning approach, termed insideOutside, for classifying interior and exterior points of a three-dimensional point-cloud, a common output from imaged cells within the early mouse embryo. We benchmark our method against other published methods to demonstrate that it yields greater accuracy in classification of nuclei from the pre-implantation mouse embryos and greater accuracy when challenged with local surface concavities. We have made MATLAB and Python implementations of the method freely available. This method should prove useful for embryology, with broader applications to similar data arising in the life sciences

insideOutside: an accessible algorithm for classifying interior and exterior points, with applications in embryology.

Funder: Company of BiologistsFunder: University of CambridgeA crucial aspect of embryology is relating the position of individual cells to the broader geometry of the embryo. A classic example of this is the first cell-fate decision of the mouse embryo, where interior cells become inner cell mass and exterior cells become trophectoderm. Fluorescent labelling, imaging, and quantification of tissue-specific proteins have advanced our understanding of this dynamic process. However, instances arise where these markers are either not available, or not reliable, and we are left only with the cells' spatial locations. Therefore, a simple, robust method for classifying interior and exterior cells of an embryo using spatial information is required. Here, we describe a simple mathematical framework and an unsupervised machine learning approach, termed insideOutside, for classifying interior and exterior points of a three-dimensional point-cloud, a common output from imaged cells within the early mouse embryo. We benchmark our method against other published methods to demonstrate that it yields greater accuracy in classification of nuclei from the pre-implantation mouse embryos and greater accuracy when challenged with local surface concavities. We have made MATLAB and Python implementations of the method freely available. This method should prove useful for embryology, with broader applications to similar data arising in the life sciences

Oct4 is required for lineage priming in the developing inner cell mass of the mouse blastocyst

The transcription factor Oct4 is required in vitro for establishment and maintenance of embryonic stem cells and for reprogramming somatic cells to pluripotency. In vivo, it prevents the ectopic differentiation of early embryos into trophoblast. Here, we further explore the role of Oct4 in blastocyst formation and specification of epiblast versus primitive endoderm lineages using conditional genetic deletion. Experiments involving mouse embryos deficient for both maternal and zygotic Oct4 suggest that it is dispensable for zygote formation, early cleavage and activation of Nanog expression. Nanog protein is significantly elevated in the presumptive inner cell mass of Oct4 null embryos, suggesting an unexpected role for Oct4 in attenuating the level of Nanog, which might be significant for priming differentiation during epiblast maturation. Induced deletion of Oct4 during the morula to blastocyst transition disrupts the ability of inner cell mass cells to adopt lineage-specific identity and acquire the molecular profile characteristic of either epiblast or primitive endoderm. Sox17, a marker of primitive endoderm, is not detected following prolonged culture of such embryos, but can be rescued by provision of exogenous FGF4. Interestingly, functional primitive endoderm can be rescued in Oct4-deficient embryos in embryonic stem cell complementation assays, but only if the host embryos are at the pre-blastocyst stage. We conclude that cell fate decisions within the inner cell mass are dependent upon Oct4 and that Oct4 is not cell-autonomously required for the differentiation of primitive endoderm derivatives, as long as an appropriate developmental environment is established