Most adult hippocampal neural stem cells (NSCs) remain quiescent, with only a minor portion undergoing active proliferation and neurogenesis. The molecular mechanisms that trigger the transition from ...quiescence to activation are still poorly understood. Here, we found the activity of the transcriptional co‐activator Yap1 to be enriched in active NSCs. Genetic deletion of Yap1 led to a significant reduction in the relative proportion of active NSCs, supporting a physiological role of Yap1 in regulating the transition from quiescence to activation. Overexpression of wild‐type Yap1 in adult NSCs did not induce NSC activation, suggesting tight upstream control mechanisms, but overexpression of a gain‐of‐function mutant (Yap1‐5SA) elicited cell cycle entry in NSCs and hilar astrocytes. Consistent with a role of Yap1 in NSC activation, single cell RNA sequencing revealed a partial induction of an activated NSC gene expression program. Furthermore, Yap1‐5SA expression also induced expression of Taz and other key components of the Yap/Taz regulon that were previously identified in glioblastoma stem cell‐like cells. Consequently, dysregulated Yap1 activity led to repression of hippocampal neurogenesis, aberrant cell differentiation, and partial acquisition of a glioblastoma stem cell‐like signature.
Synopsis
In the adult hippocampus, most neural stem cells are quiescent and only few become activated and generate new neurons. This study demonstrates an important role of the transcriptional activator Yap1 in mediating the transition between quiescence and activation.
Yap1 activity is enriched in activated neural stem cells.
Loss‐of‐function and gain‐of‐function experiments support that Yap1 promotes neural stem cell activation.
Prolonged Yap1 gain‐of‐function represses hippocampal neurogenesis.
Prolonged Yap1 activation leads to aberrant neural cell differentiation and partial glioblastoma stem cell‐like signature.
The Hippo pathway effector protein Yap1 mediates the transition between quiescence and activation in adult hippocampal neural stem cells.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Our understanding of the signalling pathways regulating early human development is limited, despite their fundamental biological importance. Here, we mine transcriptomics datasets to investigate ...signalling in the human embryo and identify expression for the insulin and insulin growth factor 1 (IGF1) receptors, along with IGF1 ligand. Consequently, we generate a minimal chemically-defined culture medium in which IGF1 together with Activin maintain self-renewal in the absence of fibroblast growth factor (FGF) signalling. Under these conditions, we derive several pluripotent stem cell lines that express pluripotency-associated genes, retain high viability and a normal karyotype, and can be genetically modified or differentiated into multiple cell lineages. We also identify active phosphoinositide 3-kinase (PI3K)/AKT/mTOR signalling in early human embryos, and in both primed and naïve pluripotent culture conditions. This demonstrates that signalling insights from human blastocysts can be used to define culture conditions that more closely recapitulate the embryonic niche.
Growth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the ...topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Current understandings of cell specification in early mammalian pre-implantation development are based mainly on mouse studies. The first lineage differentiation event occurs at the morula stage, ...with outer cells initiating a trophectoderm (TE) placental progenitor program. The inner cell mass arises from inner cells during subsequent developmental stages and comprises precursor cells of the embryo proper and yolk sac
. Recent gene-expression analyses suggest that the mechanisms that regulate early lineage specification in the mouse may differ in other mammals, including human
and cow
. Here we show the evolutionary conservation of a molecular cascade that initiates TE segregation in human, cow and mouse embryos. At the morula stage, outer cells acquire an apical-basal cell polarity, with expression of atypical protein kinase C (aPKC) at the contact-free domain, nuclear expression of Hippo signalling pathway effectors and restricted expression of TE-associated factors such as GATA3, which suggests initiation of a TE program. Furthermore, we demonstrate that inhibition of aPKC by small-molecule pharmacological modulation or Trim-Away protein depletion impairs TE initiation at the morula stage. Our comparative embryology analysis provides insights into early lineage specification and suggests that a similar mechanism initiates a TE program in human, cow and mouse embryos.
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FZAB, GEOZS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
There is an increasing accumulation of evidence supporting the existence of a hyperbolic geometry underlying the network representation of complex systems. In particular, it has been shown that the ...latent geometry of the human protein network (hPIN) captures biologically relevant information, leading to a meaningful visual representation of protein-protein interactions and translating challenging systems biology problems into measuring distances between proteins. Moreover, proteins can efficiently communicate with each other, without global knowledge of the hPIN structure, via a greedy routing (GR) process in which hyperbolic distances guide biological signals from source to target proteins.
It is thanks to this effective information routing throughout the hPIN that the cell operates, communicates with other cells and reacts to environmental changes. As a result, the malfunction of one or a few members of this intricate system can disturb its dynamics and derive in disease phenotypes. In fact, it is known that the proteins associated with a single disease agglomerate non-randomly in the same region of the hPIN, forming one or several connected components known as the disease module (DM).
Here, we present a geometric characterisation of DMs. First, we found that DM positions on the two-dimensional hyperbolic plane reflect their fragmentation and functional heterogeneity, rendering an informative picture of the cellular processes that the disease is affecting. Second, we used a distance-based dissimilarity measure to cluster DMs with shared clinical features. Finally, we took advantage of the GR strategy to study how defective proteins affect the transduction of signals throughout the hPIN.
Many tools visualize protein-protein interaction (PPI) networks. The tool introduced here, CellMap, adds one crucial novelty by visualizing PPI networks in the context of subcellular localization, ...i.e. the location in the cell or cellular component in which a PPI happens. Users can upload images of cells and define areas of interest against which PPIs for selected proteins are displayed (by default on a cartoon of a cell). Annotations of localization are provided by the user or through our in-house database. The visualizer and server are written in JavaScript, making CellMap easy to customize and to extend by researchers and developers.
The Popularity-Similarity (PS) model sustains that clustering and hierarchy, properties common to most networks representing complex systems, are the result of an optimisation process in which nodes ...seek to form ties, not only with the most connected (popular) system components, but also with those that are similar to them. This model has a geometric interpretation in hyperbolic space, where distances between nodes abstract popularity-similarity trade-offs and the formation of scale-free and strongly clustered networks can be accurately described.
Current methods for mapping networks to hyperbolic space are based on maximum likelihood estimations or manifold learning. The former approach is very accurate but slow; the latter improves efficiency at the cost of accuracy. Here, we analyse the strengths and limitations of both strategies and assess the advantages of combining them to efficiently embed big networks, allowing for their examination from a geometric perspective. Our evaluations in artificial and real networks support the idea that hyperbolic distance constraints play a significant role in the formation of edges between nodes. This means that challenging problems in network science, like link prediction or community detection, could be more easily addressed under this geometric framework.
Indication expansion aims to find new indications for existing targets in order to accelerate the process of launching a new drug for a disease on the market. The rapid increase in data types and ...data sources for computational drug discovery has fostered the use of semantic knowledge graphs (KGs) for indication expansion through target centric approaches, or in other words, target repositioning. Previously, we developed a novel method to construct a KG for indication expansion studies, with the aim of finding and justifying alternative indications for a target gene of interest. In contrast to other KGs, ours combines human-curated full-text literature and gene expression data from biomedical databases to encode relationships between genes, diseases, and tissues. Here, we assessed the suitability of our KG for explainable target-disease link prediction using a glass-box approach. To evaluate the predictive power of our KG, we applied shortest path with tissue information- and embedding-based prediction methods to a graph constructed with information published before or during 2010. We also obtained random baselines by applying the shortest path predictive methods to KGs with randomly shuffled node labels. Then, we evaluated the accuracy of the top predictions using gene-disease links reported after 2010. In addition, we investigated the contribution of the KG's tissue expression entity to the prediction performance. Our experiments showed that shortest path-based methods significantly outperform the random baselines and embedding-based methods outperform the shortest path predictions. Importantly, removing the tissue expression entity from the KG severely impacts the quality of the predictions, especially those produced by the embedding approaches. Finally, since the interpretability of the predictions is crucial in indication expansion, we highlight the advantages of our glass-box model through the examination of example candidate target-disease predictions.
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